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Link Adaptation Strategies for Cellular Downlink with low-
fixed-rate D2D Underlay
A 3GPP LTE Case Study
Afonso Fernandes Lopes Marques Eduardo
Thesis to obtain Master of Science Degree in
Electrical and Computer Engineering
Supervisor: Prof. António José Castelo Branco Rodrigues
Examination Committee
Chairperson: Prof. José Eduardo Charters Ribeiro da Cunha Sanguino
Supervisor: Prof. António José Castelo Branco Rodrigues
Member of the Committee: Prof. Américo Manuel Carapeto Correia Dr. Ivo Luís de la Cerda Garcia e Sousa
April 2015
I
Acknowledgement
I would like to thank all my colleagues, friends and family that supported me during this work. A special
word of gratitude to my family that supported me emotionally and financially.
Likewise, I want to thank Prof. António Rodrigues for his friendship, mentorship and advice. Plus, a word
of appreciation to Nuno Kiilerich which was a source of motivation, support and productive debate.
Finally, I am also grateful to all the people from APNet, at Aalborg University, for their hospitality and
much needed help. Specially, Prof. Petar Popovski.
II
III
Abstract
The new paradigm of Machine-to-Machine (M2M) communications poses problems in regard to the
spectrum usage. In a situation where a massive number of devices wants to communicate, efficient
Radio Resource Management (RRM) algorithms are of extreme importance. It might even occur that
the number of devices to be served exceeds the number of available resources which reinforces the
need to share those resources. Downlink with low-fixed-rate Device-to-Device (D2D) underlay and
Successive Interference Cancellation (SIC) techniques are emergent solutions. The considered
scenario can be described as a Base Station (BS) that is transmitting to a given User Equipment (UE)
– Infrastructure-to-Device (I2D) link – while a Machine-Type communications Device (MTD) is also
communicating with the same UE – D2D link. Long Term Evolution (LTE) physical layer, Single-Input
Single-Output (SISO) mode and block flat Rayleigh fading are assumed for both links. Only the BS is
able to perform Link Adaptation (LA). In this context, the problem is to devise appropriate SIC LA
strategies that guarantee the feasibility of Interference Cancellation (IC). The author presents and
evaluates two SIC LA strategies that operate under different circumstances: one seeks to maximize the
system efficiency provided that information regarding the D2D link is known; the other allows the system
to satisfy the Quality of Service (QoS) requirement despite not knowing this information. Simulations
prove that the proposed rules meet the design criteria.
Keywords: M2M, D2D Underlay, SIC, LTE, LA
IV
V
Resumo
O novo paradigma de comunicações Machine-to-Machine (M2M) acarreta problemas relacionados com
o uso eficiente do espectro eletromagnético. Em situações onde existe a necessidade de comunicar
por parte de um número massivo de dispositivos, algoritmos eficientes de gestão de recursos rádio são
de extrema importância, sendo porventura necessário a partilha desses mesmos recursos por vários
links. Surgem, assim, os conceitos de Device-to-Device (D2D) Underlay e Successive Interference
Cancellation (SIC). O cenário considerado é representado por dois links: D2D e Infrastructure-to-Device
(I2D). O primeiro consiste na transmissão por parte de um Machine-Type communications Device
(MTD) para um dado User Equipment (UE); enquanto, no segundo, o transmissor é a Base Station
(BS), tendo como alvo o mesmo recetor. É assumido que comunicam via LTE, usando para tal efeito o
modo Single-Input Single-Output (SISO), e que apenas a BS é capaz de adaptar o seu link – Link
Adaptation (LA). Adicionalmente, os canais são caracterizados por block flat Rayleigh fading. Neste
contexto, o problema consiste em encontrar técnicas de LA aliadas a SIC que permitam tornar possível
o cancelamento da interferência. O autor apresenta e avalia duas técnicas distintas. Uma visa
maximizar a eficiência do sistema requerendo o uso de informação sobre o link D2D. A outra permite
garantir uma certa qualidade de serviço sem ser necessário o conhecimento dessa informação. As
simulações apresentadas validam a utilidade destas técnicas.
Palavras-chave: M2M, D2D Underlay, SIC, LTE, LA
VI
VII
Index
Chapter 1 Introduction ............................................................................................. 1
1.1. Motivation .................................................................................................................... 2
1.2. Goals and Project Contribution .................................................................................... 4
Chapter 2 Technical Approach .………………………………………………………….7
2.1. Long Term Evolution ................................................................................................... 8
2.1.1. Introduction............................................................................................................................. 8
2.1.2. Architectural Overview ........................................................................................................... 9
2.1.3. LTE Physical Layer .............................................................................................................. 11
2.1.3.1. Time-Frequency Representation ................................................................................... 11
2.1.3.2. Physical Channels and Signals ..................................................................................... 12
2.1.3.3. Downlink Signal Processing .......................................................................................... 14
2.1.3.4. OFDM Implications ........................................................................................................ 15
2.1.3.5. Adaptive Feedback ........................................................................................................ 16
2.2. Performance Metrics ..................................................................................................17
2.3. Interference Mitigation Techniques .............................................................................18
2.3.1. Introduction........................................................................................................................... 18
2.3.2. Successive Interference Cancellation .................................................................................. 19
2.3.2.1. Principle ......................................................................................................................... 19
2.3.2.2. Strategies ....................................................................................................................... 20
2.4. Link Adaptation Strategies ..........................................................................................21
2.4.1. Introduction........................................................................................................................... 21
2.4.2. Single Link ............................................................................................................................ 22
2.4.3. SIC Best-Case Rule ............................................................................................................. 23
2.4.4. SIC Worst-Case Rule ........................................................................................................... 25
Chapter 3 Scenario Description and Simulator Specification …………………...29
3.1. Scenario Description ..................................................................................................30
3.1.1. Link Level Overview ............................................................................................................. 30
3.1.2. Indoor on-body MTD Scenario ............................................................................................. 31
3.1.2.1. METIS Framework ......................................................................................................... 31
3.1.2.2. Description ..................................................................................................................... 31
3.2. Simulator Specification ...............................................................................................32
3.2.1. Introduction........................................................................................................................... 32
3.2.2. Link Level Model .................................................................................................................. 32
3.2.2.1. Overview ........................................................................................................................ 32
3.2.2.2. Preliminary Channel Model ........................................................................................... 33
VIII
3.2.2.3. Transmitter Model .......................................................................................................... 34
3.2.2.4. Receiver Model .............................................................................................................. 35
3.2.2.5. Modulation and Coding Schemes ................................................................................. 37
3.2.2.6. Effective SINR-CQI mapping ......................................................................................... 37
3.2.2.7. Operation Modes ........................................................................................................... 39
3.2.3. Indoor on-body MTD scenario model ................................................................................... 39
Chapter 4 Results and Discussion ……………………………………………………41
4.1. Preliminaries ..............................................................................................................42
4.2. Simulations over Average SNR ..................................................................................42
4.2.1. Introduction........................................................................................................................... 42
4.2.2. Average MCS ....................................................................................................................... 42
4.2.3. BLER .................................................................................................................................... 43
4.2.4. Throughput per Total RBs .................................................................................................... 45
4.2.5. Aggregate Efficiency ............................................................................................................ 47
4.3. Indoor On-Body MTD Simulations ..............................................................................48
4.3.1. Introduction........................................................................................................................... 48
4.3.2. Intermediate Results ............................................................................................................ 48
4.3.2.1. Path Losses ................................................................................................................... 48
4.3.2.2. CDF of Average SNR .................................................................................................... 49
4.3.1. Final Results......................................................................................................................... 49
4.3.1.1. CDFs of Average I2D MCS ........................................................................................... 49
4.3.1.2. CDFs of BLER ............................................................................................................... 50
4.3.1.3. CDFs of Throughput Rate per total RBs ....................................................................... 52
4.3.1.4. CDFs of Aggregate Efficiency ....................................................................................... 53
4.4. Applications ................................................................................................................54
Chapter 5 Conclusions and Future Work ……………………………………………55
5.1. Results Summary .......................................................................................................56
5.2. Final Outlook and Future Work ...................................................................................56
References .............................................................................................................. 59
Annex A …………………………………………………………………………………….63
Annex B …………………………………………………………………………………….67
Annex C …………………………………………………………………………………….69
Annex D …………………………………………………………………………………….71
IX
List of Figures
Figure 1. Mobile Data Traffic Forecast ....................................................................... 2
Figure 2. Possible Future Cellular Interactions ........................................................... 4
Figure 3. Timeline of 3GPP standards ........................................................................ 8
Figure 4. Overall EPS Network Architecture ............................................................... 9
Figure 5. LTE Layer Structure .................................................................................. 10
Figure 6. LTE Time-Domain Structure ...................................................................... 11
Figure 7. LTE Resource Grid .................................................................................... 12
Figure 8. Resource Allocation for a Particular Configuration .................................... 13
Figure 9. Downlink Signal Processing Chain ............................................................ 15
Figure 10. Channel Abstraction Model ..................................................................... 17
Figure 11. SIC Principle ............................................................................................ 19
Figure 12. SIC Best-Case Rule ................................................................................ 25
Figure 13. SIC Worst-Case Rule .............................................................................. 26
Figure 14. Link Level Topology ................................................................................. 30
Figure 15. Layout for the Indoor On-body MTD Scenario ......................................... 32
Figure 16. Small-Scale Fading plus Noise Channel ................................................. 34
Figure 17. Block Diagram of the LTE Downlink Transmitter ..................................... 35
Figure 18. Block Diagram of the Implemented LTE receiver .................................... 35
Figure 19. Implemented SIC Algorithm ..................................................................... 36
Figure 20. BLER AWGN Channel ............................................................................. 38
Figure 21. Operation Thresholds .............................................................................. 38
Figure 22. I2D Average MCS .................................................................................... 43
Figure 23. D2D BLER ............................................................................................... 44
Figure 24. I2D BLER ................................................................................................ 44
X
Figure 25. D2D Throughput per Total RBs ............................................................... 46
Figure 26. I2D Throughput per Total RBs ................................................................. 46
Figure 27. Aggregate Efficiency ................................................................................ 47
Figure 28. I2D Path Loss Map .................................................................................. 48
Figure 29. CDF of Average I2D SNR ........................................................................ 49
Figure 30. CDFs of I2D Average MCS ..................................................................... 50
Figure 31. CDFs of D2D BLER ................................................................................. 51
Figure 32. CDFs of I2D BLER .................................................................................. 51
Figure 33. CDFs of D2D Throughput per Total RBs ................................................. 52
Figure 34. CDFs of I2D Throughput per Total RBs ................................................... 53
Figure 35. CDFs of Aggregate Efficiency ................................................................. 53
Figure 36. Possible Transmission Scheme for M2M applications ............................. 54
Figure 37. Geometry of the Outdoor Propagation Model .......................................... 63
Figure 38. Geometry of the Indoor Propagation Model ............................................. 66
Figure 39. CDF of Average D2D SINR (Estimate) .................................................... 67
Figure 40. CDF of Average I2D SINR (Estimate) ..................................................... 67
XI
List of Tables Table 1. Standardized LTE Bandwidths ................................................................... 12
Table 2. Link Level Simulation Parameters .............................................................. 33
Table 3. CQI-to-MCS Look-Up Table ....................................................................... 37
Table 4. Indoor On-body MTD Simulation Parameters ............................................. 40
XII
XIII
List of Abbreviations
2G 2nd Generation
3G 3rd Generation
3GPP 3rd Generation Partnership Project
5G 5th Generation
ACK Acknowledgement
ARQ Automatic Repeat Request
AWGN Additive White Gaussian Noise
BLEP Block Error Probability
BLER Block Error Rate
BPSK Binary Phase-Shift Keying
BS Base Station
CAGR Compound Annual Growth Rate
CB Code Block
CCI Co-Channel Interference
CDMA Code Division Multiple Access
CoMP Coordinated Multipoint
CP Cyclic Prefix
CQI Channel Quality Indicator
CRC Cyclic Redundancy Check
CSI Channel State Information
CW Code Word
D2D Device-to-Device
DLSCH Downlink Shared Channel
EB Exabyte
EDGE Enhanced Data rates for GSM Evolution
EESM Exponential ESM
eNodeB Evolved Node B
EPA Extended Pedestrian A
XIV
EPC Evolved Packet Core
EPS Evolved Packet System
ESM Effective SINR Mapping
E-UTRAN Evolved UMTS Terrestrial RAN
EVA Extended Vehicular A
FDD Frequency Division Duplex
FFT Fast Fourier Transform
GERAN GSM EDGE RAN
GPRS General Packet Radio Service
GSM Global System for Mobile communications
HARQ Hybrid ARQ
HSDPA High-Speed Downlink Packet Access
HSPA High-Speed Packet Access
HSS Home Subscriber Server
HSUPA High-Speed Uplink Packet Access
I2D Infrastructure-to-Device
IC Interference Cancellation
IDFT Inverse Discrete Fourier Transform
IFFT Inverse FFT
IP Internet Protocol
LA Link Adaptation
Legacy Legacy mode
LLR Log-Likelihood Ratio
LTE Long Term Evolution
M2M Machine-to-Machine
MAC Medium Access Control
MCL Minimum Coupling Loss
MCS Modulation and Coding Scheme
MIESM Mutual Information ESM
MIMO Multiple-Input Multiple-Output
XV
MME Mobility Management Entity
MTD Machine-Type communications Device
NACK Negative Acknowledgment
NAS Non-Access Stratum
O2I Outdoor-to-Indoor
OFDM Orthogonal Frequency-Division Multiplexing
Ortho Orthogonal mode
PBCH Physical Broadcast Channel
PCFICH Physical Control Format Indicator Channel
PCRF Policy Charging and Rules Function
PDCCH Physical Downlink Control Channel
PDCP Packet Data Convergence Protocol
PDN Packet Data Network
PDSCH Physical Downlink Shared Channel
P-GW PDN Gateway
PHY Physical
PHICH Physical HARQ Indicator Channel
PMCH Physical Multicast Channel
PRACH Physical Random Access Channel
PSS Primary Synchronization Signal
PUCCH Physical Uplink Control Channel
PUSCH Physical Uplink Shared Channel
QAM Quadrature Amplitude Modulation
QoS Quality of Service
RAN Radio Access Network
RB Resource Block
RE Resource Element
RLC Radio Link Control
ROHC Robust Header Compression
RRC Radio Resource Control
XVI
RRM Radio Resource Management
SAE System Architecture Evolution
SGSN Serving GPRS Support Node
S-GW Serving Gateway
SIC Successive Interference Cancellation
SICBCR SIC mode with Best-Case Rule
SICWCR SIC mode with Worst-Case Rule
SINR Signal to Interference plus Noise Ratio
SIR Signal to Interference Ratio
SISO Single-Input Single-Output
SNR Signal to Noise Ratio
SSS Secondary Synchronization Signal
TB Transport Block
TDD Time Division Duplex
TTI Transmission Time Interval
UE User Equipment
UMTS Universal Mobile Telecommunication System
UTRAN UMTS Terrestrial RAN
VoIP Voice over IP
Wi-Fi Wireless-Fidelity
WSN Wireless Sensor Network
ZF Zero Forcing
XVII
List of Notation
Indices � BS (transmitter), also refers to I2D link
� Desired (or Detected)
��� Effective
� Interference
� MTD (transmitter), also refers to D2D link
� UE (receiver)
Symbols �� Transmission bandwidth
Capacity
��[�] Channel coefficient associated to link � on ��
�� Antenna gain of � �� Path loss associated to link � �� Number of paths
�� Noise spectral density
���� Number of FFT points
���� Total number of subcarriers
��� Noise Factor of � �� Average transmit power of � ���� Outage probability
�� Probability function
�� Absolute throughput of link � ���� � Number of allocated RBs per link
����� Number of total allocated RBs (both links)
�!" Equalization weight
#[$]|�& Component of �� at time $
#�[�] QAM symbol transmitted by � on ��
XVIII
'[�] Post-detection received signal on ��
)� Delay of the �th path
�� Subcarrier �
ℎ� Impulse response associated to path or link � +� Probability density function of � �� Normalized throughput of link � (in regard to the maximum absolute value)
, White Gaussian noise
-� Transmitted OFDM signal associated to link � -./0[$] Sampled transmitted OFDM symbol at time $
1[$] Sampled received OFDM signal at time $
Γ� Operation threshold of the �th MCS (or associated to link �) Δ� Subcarrier frequency spacing
4 Noise scaling parameter
5�[�] SNR of signal � on ��
567[�] Average SNR of signal � on ��
89: Aggregate efficiency
8(�) Efficiency of the �th MCS
=�[�] SINR of signal � on ��
=>� Infimum SINR of signal � =67[�] Average SINR of signal � on ��
? Target BLER
@AB White Gaussian noise power (variance)
XIX
List of Software
1. MATLAB R2013a
2. Vienna LTE Link Level Simulator v1.7r1089 (modified)
XX
1
Introduction
The present chapter provides an overview of the work, discussing the conceptual background and
motivations. The scope of the project is then defined, followed by the presentation of the work structure
at the end of the chapter.
2
As users increasingly turn to mobile broadband, mobile data traffic has been growing at an extraordinary
rate. With the advent of new paradigms, such as the Internet of Things, mobile traffic is likely to reach
even higher growth rates so much so that it is expected to grow nearly tenfold by the end of 2019 when
compared to that of 2014, growing at a Compound Annual Growth Rate (CAGR) of 57 percent [1], as
depicted in Figure 1. This, in turn, poses many challenging problems as the wireless cellular networks
have to evolve in order to accommodate this boom.
To date, wireless standards have been evolving so that they can achieve higher data rates, lower
latencies and use the spectrum in a more efficient fashion. With such objectives in mind, these
standards, namely the Long-Term Evolution (LTE) [2], make use of higher Modulation and Coding
Schemes (MCS) that can be dynamically adjusted to cope with different channel conditions (also known
as Link Adaptation – LA) such that a certain Quality of Service (QoS) can be guaranteed. In addition,
LTE supports Multiple-Input Multiple-Output (MIMO) modes and standardizes multiple bandwidths and
cell sizes which increase spectrum and latency flexibility.
Currently, the design of future 5th generation (5G) cellular networks is in the works and, within this
framework, several technologies are being investigated. In [3], it is argued that a significantly better
performance cannot be accomplished by simply adding new features to the existing standards and that
such goal requires instead the introduction of disruptive technologies that force the deep-rooted cellular
principles to be reformulated. Among those technologies, there are two that fall into the scope of this
work: Device-to-Device (D2D) and Machine-to-Machine (M2M) communications.
Cellular networks of the previous generations were designed under the principle that the infrastructure,
specifically the Base Station (BS) within each cell, is the controlling entity such that all the
communications must go through it – Infrastructure-to-Device (I2D) communications. While this principle
FIGURE 1. MOBILE DATA TRAFFIC FORECAST (EXTRACTED FROM [1])
3
is reasonable in a voice-driven system in which the two parties are usually far from one another, the
same might not be as efficient in an increasingly data-driven system. As a matter of fact, the situation
where several devices within a cell want to share content is likely to occur in the near future. In this
scenario, if this very same premise was to be maintained, these devices would require multiple hops in
order to communicate as opposed to a single hop if D2D communications were to be allowed. It should
be noted that not only a lower latency could be achieved, but gains related to the amount of resources
used and battery drain could be had. In addition, the use of such type of communications would also
reduce the network management load even more so if a large number of connected devices were to be
located within a single cell. D2D communication refers, thus, to the ability of one wireless device to
communicate, via direct link, with another using the spectrum that is available for regular cellular
communications. It is noteworthy that existing technologies, such as Wi-Fi and Bluetooth, already allow
devices to directly communicate with each other, but the usage of unlicensed radio resources leads to
several limitations given that interference levels cannot be properly controlled.
On the other hand, M2M consists of a multitude of Machine-Type communications Devices (MTDs)
connected to the network. These devices are often described in the context of Wireless Sensor Networks
(WSNs) which are able to offer numerous applications related to data collection, ranging from smart-city
communications to health-care and environmental monitoring. These services share the fact that are
supported by low-data-rate communications, but have different requirements in regard to the number of
supported devices, latency and reliability.
At this point, it is evident that D2D and M2M are closely related and that the potential synergies between
the two should be exploited in the design of future 5G cellular networks. One problem that arises is
related to the spectrum usage. In fact, in a situation where there is a need for a massive number of
devices to communicate, efficient Radio Resource Management (RRM) algorithms are of extreme
importance. In certain circumstances, it might even occur that the number of devices to be served
exceeds the number of resources which further reinforces the emergence of Interference Cancellation
(IC) techniques.
IC techniques allow a specified receiver to operate with higher levels of Co-Channel Interference (CCI),
therefore the network can use the spectrum in a more efficient fashion by assigning the same resources
to multiple links within a given cell. Thus, in a future cellular network such as the one depicted in Figure
2, D2D and I2D communications are likely to coexist in time and spectrum.
In [4], it is explained that 3rd Generation Partnership Project (3GPP), the consortium responsible by the
LTE standardization, is currently evaluating the D2D technology as an add-on in the form of a new LTE
release version. Given that D2D is not a feature that is natively supported by LTE, it is important to limit
its impact on the already existing networks. Therefore, in that context, it is assumed that these type of
communications are to be performed within uplink carriers in FDD or uplink subframes in TDD. However,
in the 5G networks, D2D is likely to be natively included and, in that sense, extending the option to be
able to perform these communications during the downlink allows for a greater flexibility in resource
management which might lead to a better performance.
4
Thus, it follows that this work focuses on the cellular downlink with low-fixed-rate D2D underlay [5]. This
scenario can be summarily described as a BS that is transmitting to a given User Equipment (UE) - I2D
link - while a MTD is also communicating with the same UE - D2D link. LTE physical layer and Single-
Input Single-Output (SISO) mode are assumed in both links. Additionally, the D2D link operates at a
fixed low-data-rate, whereas the I2D link is able to perform LA. In this context, the problem is to devise
appropriate link adaptation strategies to guarantee the feasibility of IC and, thus, ensure an effective
and efficient system.
The author presents and evaluates two SIC LA strategies that serve different purposes: one seeks to
maximize the system efficiency provided that information regarding the D2D link is known; the other
allows the system to satisfy the Quality of Service (QoS) requirement despite not knowing this
information.
This study’s main contribution is twofold. First, the author investigates the possible Successive
Interference Cancellation (SIC) techniques that can be applied in a D2D and I2D coexistence scenario
while focusing on the LTE physical layer. In this stage, a survey of the existing IC techniques is
performed. These findings are then used to design a D2D-enabled device and its performance is
evaluated under a cellular downlink with low-fixed-rate D2D underlay scenario. It should be noted that
this scenario contrasts to that of the established model, where D2D underlay only operates during the
uplink, as it is believed that also using the cellular downlink leads to an overall better performance
because it adds an increased flexibility that RRM algorithms can exploit.
FIGURE 2. POSSIBLE FUTURE CELLULAR INTERACTIONS
5
The other contribution encompasses the presentation and evaluation of two SIC LA strategies: the first
strategy aims to maximize the system efficiency provided that Channel State Information (CSI) of the
D2D is known; the other guarantees the reliability of the system under the aforementioned scenario
assuming that D2D CSI is not known. The latter was originally presented in [6] and allows zero outage
to be achieved in theory. The approach here devised differs in the sense that LTE physical layer is
considered which naturally introduces limitations in regard to its applicability and performance.
It is noteworthy that the system under evaluation is SISO and the channel can be described as a block
flat Rayleigh fading channel. However, during this work, it is given additional insight as to how these
strategies can also be applied to frequency selective channels.
The rest of the thesis has the following organization: initially, “Technical Approach” is provided in
Chapter 2; secondly, “Scenario Description and Simulator Specification” is revealed in Chapter 3; thirdly,
“Results and Discussion” are reported in Chapter 4; finally, “Conclusions and Future Work” are
discussed in Chapter 5.
6
7
Technical Approach
This chapter begins by providing an overview of LTE, focusing on explaining the essential concepts that
are required to understand the work in this thesis. Interference Cancellation and Link Adaptation
strategies are also investigated and described. The state of the art is presented throughout this chapter.
8
Long Term Evolution (LTE) was standardized by the 3rd Generation Partnership Project (3GPP)
consortium and appeared as the successor of the Universal Mobile Telecommunications System
(UMTS) standard. LTE follows the assumption that all of the services are packet-switched, continuing
the trend of the previous generation. This trend first began with the introduction of General Packet Radio
Service (GPRS) as an enhancement to Global System for Mobile communications (GSM), a circuit-
switched technology. After GPRS, Enhanced Data Rates for GSM Evolution (EDGE), UMTS, and High-
Speed Packet Access (HSPA) were released. Figure 3 depicts the evolution.
During this progression, the focus has been to provide higher throughput and lower latency services.
These needs combined with affordability integrate the list of requirements specified by the 3GPP
consortium for LTE and some of the main points are [7]:
• Improved spectrum efficiency leading to increased peak data rates of 50 Mb/s in the uplink and
100 Mb/s in the downlink.
• Flexible bandwidth.
• Simple network architecture and easy interworking with previous 3GPP technologies such that
it is cost-effective to migrate and operate.
The rest of this section presents the basics of the architecture and layer structure, followed by a more
in-depth discussion of the physical layer (PHY), namely resource arrangement, physical channels and
signals, signal processing, Orthogonal Frequency-Division Multiplexing (OFDM) and indicators that
facilitate link adaptation.
FIGURE 3. TIMELINE OF 3GPP STANDARDS (ADAPTED FROM [2])
9
LTE has been designed to exclusively support packet-switched services and aims to provide seamless
IP connectivity between the mobile terminal and the Packet Data Network (PDN) [2]. Allied to the term
LTE, which covers the evolution of the Radio Access Network (RAN), there is also the evolution of non-
radio aspects under the term System Architecture Evolution (SAE) whose main component is the
Evolved Packet Core (EPC) network, i.e. the core network. The complete system, Evolved Packet
System (EPS), is composed by LTE and SAE.
EPS makes use of bearers to route IP traffic from a gateway in the data network to the mobile terminal.
A certain QoS can be assigned to a bearer and multiple bearers, providing different QoS streams or
connectivity, can be established for a single user. It should be noted that while EPS natively supports
voice services using Voice over IP (VoIP), it also allows interworking with previous existing systems that
provide circuit-switched voice support.
The basic network architecture of EPS, depicted in Figure 4, is comprised of three parts: the core
network (EPC), the radio access network, termed Evolved-UTRAN (E-UTRAN), and the mobile terminal,
or equivalently User Equipment (UE).
The EPC is responsible for Non-Access Stratum (NAS) procedures, packet routing and network
management. The main elements are PDN Gateway (P-GW), Serving Gateway (S-GW) and Mobility
Management Entity (MME).
FIGURE 4. OVERALL EPS NETWORK ARCHITECTURE (ADAPTED FROM [31])
10
The P-GW serves as the router for sessions towards external PDNs, being responsible for IP address
allocation for the UE and, together with the Policy and Charging Rules Function (PCRF), decides the
treatment of the different data flows.
On the other hand, the S-GW is the user-plane termination point towards the access network, acting as
the local mobility anchor when the UE moves between cells. It also serves as interworking anchor for
other 3GPP technologies. Note that the user-plane connection is directly routed to the UMTS RAN for
3G, while both planes need to be routed through the Serving GPRS Support Node (SGSN) for 2G
mobility.
The MME is the principal control node, processing the signaling between the end user and the core
network via NAS protocols. The main functions are related to bearer management, connection
management and interworking with other networks, providing security control together with the Home
Subscriber Server (HSS) that contains the user subscription information.
Connected to the EPC, the RAN handles the radio interface-related functions for active terminals. The
LTE RAN, i.e. E-UTRAN, is a meshed network whose nodes are the Base Stations (BSs), or equivalently
eNodeBs. Despite the BS being connected to the core network, it is also able to communicate with other
BSs, allowing to employ cooperation schemes to increase performance. As depicted in Figure 5, the
BSs are responsible for Radio Resource Control (RRC), Packet Data Control Protocol (PDCP), Radio
Link Control (RLC), Medium Access Control (MAC) and physical (PHY) layers. For an in-depth
description, the reader is referred to [2].
FIGURE 5. LTE LAYER STRUCTURE (ADAPTED FROM [30], [31])
11
The LTE physical layer uses OFDM, meaning that radio resources span in time and frequency domains.
In the time domain, LTE arranges the transmission into radio frames whose length is 10 ms. As displayed
in Figure 6, each radio frame is subdivided into ten subframes of 1 ms (TTI), subsequently composed
of two 0.5 ms slots. One slot, in turn, consists of six or seven OFDM symbols depending on whether a
normal or extended cyclic prefix is used. This prefix is a guard period that consists of a repetition of the
end of the corresponding OFDM symbol and allows to avoid inter-symbol interference provided that its
length is at least equal to the maximum path delay. The normal cyclic prefix is suitable for transmissions
over the majority of urban and suburban environments, being thus the one considered throughout this
work.
FIGURE 6. LTE TIME-DOMAIN STRUCTURE
One of the requirements, when standardizing, was spectrum flexibility and, as such, LTE has a list of
spectrum allocations that range from 1.4 to 20 MHz. The bandwidth itself is divided into equally-spaced
orthogonal subcarriers whose typical spacing is 15 kHz. Each subcarrier group (12 consecutive
subcarriers for this specific configuration) has a total bandwidth of 180 kHz and it is often known as a
Resource Block (RB). Table 1 presents the set of possible bandwidths along with other parameters
arising thereof [2].
12
TABLE 1. STANDARDIZED LTE BANDWIDTHS
Combining the time and frequency domains, the LTE resource grid is obtained and each basic element
is referred as Resource Element (RE) as observed in Figure 7. The horizontal coordinate specifies the
OFDM subcarrier in the frequency domain, and its counterpart the OFDM symbol to which the RE
belongs in the time domain.
Naturally, the information transmitted is not only composed of user data, but a whole plethora of physical
channels and signals to allow successful communication between the devices. There is thus the need
to map reference signals, control and data channels onto the resource grid. There are two groups of
physical channels depending on whether uplink or downlink occur.
During the uplink, there are three physical channels active: Physical Uplink Shared Channel (PUSCH),
Physical Random Access Channel (PRACH) and Physical Uplink Control Channel (PUCCH). PUSCH
is responsible for carrying the user data transmitted by the UE, while PRACH is used to request access
to the network through transmission of random access preambles. PUCCH carries control information
such as acknowledgements of transmission success or failure and channel quality feedback.
On the other hand, during the downlink, there are six physical channels: Physical Downlink Shared
Control Channel (PDSCH), Physical Broadcast Channel (PBCH), Physical Downlink Control Channel
(PDCCH), Physical Control Format Indicator Channel (PCFICH), Physical Hybrid ARQ Indicator
Channel (PHICH) and Physical Multicast Channel (PMCH). PDSCH carries unicast user data, while
PBCH, PDCCH, PCFICH and PHICH transport control information. Lastly, PMCH carries multicast data
if said mode is active.
Channel Bandwidth [MHz] 1.4 3 5 10 15 20
Number of RBs 6 15 25 50 75 100
Number of data subcarriers 72 180 300 600 900 1200
Transmission Bandwidth [MHz] 1.08 2.7 4.5 9 13.5 18
Bandguard size (%) 23 10 10 10 10 10
FIGURE 7. LTE RESOURCE GRID
13
A variety of physical signals is also mapped onto the resource grid. There are reference and
synchronization signals. The former, used in both uplink and downlink, are spread over the bandwidth
and allow the devices to estimate the channel conditions such that equalization can be performed. The
latter, namely Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS), are
only active during the downlink and are used for cell search and UE network synchronization.
The mapping of a particular physical channel and signal to a given RE depends on several factors such
as CP, channel bandwidth, cell ID or whether Frequency-Division Duplex (FDD) or Time-Division Duplex
(TDD) is being used. Explaining the actual assignment goes beyond the scope of this work, but Figure
8 shows the mapping of these channels and signals for the particular case of a downlink unicast SISO
transmission using FDD and normal CP over a 1.4 MHz bandwidth.
For this particular configuration, it can be observed that the overhead is significant. In fact, it accounts
for roughly 23% of the total resources.
Additionally, for the scenario that is being considered in this thesis, namely the case where two links
purposely share the same resources, this mapping does not fulfill the requirements, mostly because in
order to accurately estimate both channels, reference signals belonging to each link should be
orthogonal. Whether the overhead regarding the reference signals doubles is unclear because reducing
the number of reference signals spread over the bandwidth for each link or a completely different
approach might yield better results. Given that this is a novel scenario, no current 3GPP release
specifies the mapping and this problem also goes beyond the scope of this thesis.
That said, in this study, only user data is modeled which also has the benefit of allowing the
implementation of a simpler simulator and reducing the simulation runtime. Note that this does not affect
the analysis of what is being proposed, since the focus is to compare the performance of the suggested
methods.
FIGURE 8. RESOURCE ALLOCATION FOR A PARTICULAR CONFIGURATION (ADAPTED FROM [32])
14
The simplified chain of downlink signal processing procedures is depicted in Figure 9. This chain results
of the combination of Downlink Shared Channel (DLSCH) and PDSCH processing.
DLSCH is the main downlink MAC transport channel type in LTE and is responsible for carrying
information related to user data, dedicated control information and other downlink system information.
All this information, belonging to a specific user, is grouped into chunks of variable length known as
Transport Blocks (TBs). Its further processing encompasses TB-CRC (TB - Cyclic Redundancy Check)
attachment, Code Block (CB) segmentation, CB-CRC insertion, turbo coding, rate matching and CB
concatenation.
The processing chain begins by calculating and attaching a 24-bit CRC to each TB as a means to detect
errors at the receiver side. The CRC-attached TB may be split into several CBs since the turbo coder
interleaver has a maximum size of 6144 bits. In such case, each CB is attached with its own 24-bit CRC
(CB-CRC). Subsequently, each CRC-attached CB is rate-1/3-turbo-code encoded followed by a rate
matching procedure which includes additional interleaving and produces CB Code Words (CWs) with
the desired code rate. The CB-CWs are then concatenated to form a TB-CW, completing the DLSCH
processing phase and moving to the PDSCH processing stage. A complete specification of this
processing can be found in [8].
The next stage, PDSCH processing, is responsible for converting the bit stream coming out of DLSCH
processing into radio frame data to be transmitted by each antenna. Its first operation is to scramble the
CW with a Gold scrambling sequence. It should be noted that the generated scrambling sequences are
unique to neighboring cells, based on their PHY level identity, so that the interference is randomized
and transmissions from adjacent cells can be separated before decoding. After the scrambler procedure,
the scrambled CW passes through a symbol mapper whose modulation can be 4-, 16- or 64-QAM
depending on the Modulation and Coding Scheme (MCS) index. The selection of this index and,
therefore, the modulation to be applied will be addressed in a subsequent subchapter. The following
steps, layer mapping and precoding, relate to MIMO transmissions where multiple transmit antennas
are present. In such scenario, up to two CWs can be transmitted simultaneously. Layer mapping is thus
the procedure that assigns the CWs to layers, depending on the selected transmission mode. Precoding
then combines the information in such layers and generates a sequence for each antenna port. Next,
each sequence is mapped to REs that form the resource grid which, in turn, serves as input to the OFDM
modulator, producing the baseband time-domain signal to be transmitted by each antenna after radio-
frequency upconversion. For a more detailed specification, the reader is referred to [9].
15
FIGURE 9. DOWNLINK SIGNAL PROCESSING CHAIN (ADAPTED FROM [10])
In this subsection, the focus is to analyze the main implications of OFDM. Summarily, this procedure
operates on the resource grid by taking the QAM modulated symbols row wise (Figure 7) and performing
an IFFT operation followed by CP addition to generate the OFDM modulated signal.
In fact, assume a subcarrier frequency spacing �, each subcarrier �� is thus considered as follows:
�� D �E�, � D 0, 1, … , ���� J 1, (1)
where ���� corresponds to the total number of subcarriers. Each sampled transmitted OFDM symbol
can then be expressed as [11]:
-./0 $� D ∑ # $�|�& ,# $�|�& D#L ���MBN� OPQRQSQRQTU�V� , $ D 0, 1, … , ���� J 1, (2)
where# $�|�& denotes the ��component and #L �� the corresponding QAM symbol. Note that equation
(2) is the ����-point IDFT of the QAM symbols and can be computed by the IFFT algorithm. This
procedure is then followed by CP addition, extending the duration of the OFDM symbol by using its last
samples as a prefix.
16
Additionally, in a multipath fading channel, without AWGN, consisting of �� paths and each
characterized by a delay )� and an impulse response *�, the sampled received OFDM signal, in the time
domain, is [10]:
1 $� D ∑ *� $�. -L $ J )��XY�V� ,$ D Z�$;)�< , …,∞, (3)
where -L denotes the sampled transmitted OFDM signal.
Assuming that the CP length is greater or equal than the maximum path delay, then the orthogonality
between consecutive OFDM symbols is preserved which, in turn, allows the post-detection received
signal on �� to be written as [10]:
' �� D �L ��#L ��,�L �� D\ *� �TMBN�]^SQRQXY�V� , (4)
where �L �� is the channel coefficient on ��. This expression indicates that the effects of multipath fading
can be removed (or, at least, mitigated) at the receiver side by using a frequency-domain equalization
whose channel gain estimates are obtained from reference signals.
In order to achieve a better performance, LTE introduces flexibility in regard to its transmission
parameters based on the received channel conditions. There are three parameters worth mentioning:
Rank Indicator, Precoding Matrix Indicator and Channel Quality Indicator (CQI). The first two are used
when MIMO transmission occurs and, since this work focuses on SISO mode, CQI is the only indicator
to be addressed in this subsection.
The CQI is a measurement index that allows the receiver to report the downlink radio channel quality,
specifying the modulation constellation and coding rate that best fit the measured link quality. This index
signals, on a per-codeword basis, the highest of 15 standardized MCSs that guarantees a BLER lower
or equal to 10% [12]. Since the selection of the CQI is to be performed at the receiver side, it is a
procedure that is not standardized, being the device manufacturer responsibility to conceive a method
that allows the device to exploit the channel conditions in the best possible fashion. However, this field
has already been the subject of extensive studies and the current practice is to determine the SINRs at
which the instantaneous BLER, or alternatively Block Error Probability (BLEP), is 10% for each MCS, a
method known as SINR-CQI mapping. Note that this mapping is receiver-specific in the sense that a
better receiver might achieve a similar BLEP at a lower SINR for a given MCS.
Another issue that arises relates to the channel model, namely frequency-selective fading channels. In
this type of channels, as opposed to flat fading channels, distinct subcarriers generally experience
17
different SINRs, but the CQI reports the channel quality on a per-codeword basis. It is then of utmost
importance to conceive an abstraction of the channel model. Luckily, several studies have already
addressed this issue and [13] provides a succinct, yet comprehensive analysis. In summary, to
overcome this problem, the SINR-CQI mapping is obtained from a SISO AWGN simulation and an
effective SINR, based on a compression method that factors in the different SINRs from the several
subcarriers, is computed such that there is a close match between the BLEPs of the real frequency-
selective fading channel and the equivalent AWGN channel. This method is known as Effective SINR
mapping (ESM) and the compression methods can be either the Mutual Information ESM (MIESM) or
Exponential ESM (EESM). Figure 10 depicts this channel abstraction model.
FIGURE 10. CHANNEL ABSTRACTION MODEL
In order to introduce the metrics, first consider a single link whose received SNR is denoted by 5L. Unlike
AWGN, in a fading environment, SNR is a random variable with distribution +_`;5<. In such case, one
metric that arises is the outage probability and corresponds to the probability of the SNR being lower
than a given threshold, ΓL, which normally specifies the minimum SNR that is necessary for acceptable
performance:
���� D ��;5L a ΓL< D b +_`;5<c`� )5. (5)
In a Rayleigh fading environment, the outage probability is thus given by
���� D b 15Lddd �T_/_`ddddc`� )5 D 1 J �Tc`/_`dddd, (6)
where 5Lddd denotes the average SNR. By inverting this formula, the required average SNR for a given
outage probability can be found
18
5Lddd D ΓLJ ln;1 J ����<. (7)
While the outage probability is a good enough metric for most theoretic analysis in which it is assumed
the use of optimal coding; in real systems, non-zero error rate of the transmitted block (BLER) also
occurs for non-outage events. In this case, BLER becomes thus a more reliable performance metric.
For a block flat fading channel in which each transmitted block experiences a given SNR, the BLER is
given by
��h� D b +_`;5<��h�;5<i� )5
D b +_`;5<��h�;5<c`� )5 jb +_`;5<��h�;5<i
c` )5≅ ���� jb +_`;5<��h�;5<i
c` )5, (8)
where BLEP indicates the estimated error block probability for a given receiver and SNR. Plus,
considering that the BLEP for 5 a ΓL is 1 (which is a slightly pessimistic but fair approximation), it can
be readily understood that ���� approximately corresponds to the maximum achievable BLER. It can
also be observed that the BLER is the result of two events: fading, for 5 a ΓL, and non-optimal channel
coding for 5 l ΓL.
Additionally, for a system where interference is present and there is no mechanism capable of controlling
such interference, the link is characterized instead by its SINR (=). In this scenario, outage is caused by
fading and an eventual strong interference leading to = a ΓL.
As the amount of users increases, there is a need to use the spectrum in a more efficient fashion.
Interference mitigation techniques emerge as a promising solution allowing users to share the spectrum.
These techniques can be categorized into two types depending on the entity that is in control of
performing such operations.
One is applied at the transmitter side and requires the knowledge of CSI which, in turn, imposes several
limitations in a real scenario as this information can only be tracked by such devices if the fading
conditions are relatively slow due to the feedback delay. Despite such limitations, CoMP (Coordinated
Multipoint) transmission arises in the context of LTE-Advanced and is an example of such technique. It
19
was developed as a means to mitigate interference and thus improve the performance at the cell edge
by giving neighboring BSs the ability to transmit in a coordinated fashion [14], [15].
In the context of this work, i.e. cellular downlink with low-fixed-rate D2D underlay, the first option is not
feasible since the transmit entities (MTD and BS) are not able to coordinate their transmissions in such
way. Luckily, the other type of interference mitigation relies on receiver-based solutions which, naturally,
do not involve an accurate CSI at the transmitter, but come with the cost of an increased computational
complexity. One of such techniques is data Interference Cancellation (IC).
Unlike CDMA systems, OFDM systems, of which LTE is part of, do not use spreading. Therefore,
extracting the signal of an interferer by means of channel decoding is generally the only processing gain
available [16]. It is precisely in this circumstance that the concept of Successive Interference
Cancellation arises.
The idea of SIC is to decode different users sequentially, such that the interference due to the decoded
users is subtracted before decoding the other users.
In order to further illustrate this principle, consider a scenario where two entities are transmitting. The
received signal is composed by signal �, to be regarded as a strong interference and whose power is �m, and signal D, as the desired encoded signal with power �L. In such case, the receiver decodes the
received signal in two stages as seen in Figure 11. First, it decodes � while treating � as white Gaussian
noise. The interference is then reconstructed by re-encoding the detected message and multiplying it
by the estimated channel response. This estimated interfering signal is then subtracted, usually on a
symbol basis, from the detected signal such that, in an ideal case, the resulting signal only consists of
the desired encoded signal (D) and white Gaussian noise with power @AB ; therefore, the ability of the
receiver to be able to successfully decode the message is improved.
This technique can achieve a significantly higher performance when compared to a system where no
SIC is applied, especially so when the interference is significantly stronger than the intended signal.
FIGURE 11. SIC PRINCIPLE
20
Actually, in the latter and assuming that the interference can be treated as Gaussian noise, the maximum
achievable rate (normalized) is [17]:
�omp D logB s1 j �L�m j @ABt D logB;1 j =L<, (9)
where =L denotes the SINR with respect to the desired signal, while in the former, after perfect IC, the
capacity is given by
omp D logB s1 j �L@ABt D logB;1 j 5L<. (10)
It thus follows that omp l �omp.
It should be noted, however, that SIC is not always applicable. First, letΓm and ΓL denote the thresholds
below which the receiver is not able (or is very unlikely) to decode signals � and � respectively. These
thresholds, in turn, depend mainly on the MCS used to encode a given signal such that a more robust
MCS has a lower threshold. There are four regions of operation worth mentioning:
• =L l ΓL: The receiver does not need to apply SIC in order to decode �.
• =L a ΓL and =m D uvu`wxyz a Γm: The receiver cannot apply SIC (undecodable interference)
nor can decode �. Outage occurs.
• =L a ΓL , =m l Γm and 5L a ΓL: The receiver can apply SIC, but still cannot decode �.
Outage occurs.
• =L a ΓL , =m l Γm and 5L l ΓL: The receiver needs to apply SIC in order to decode �.
From this analysis, it follows that SIC works best when robust MCSs are used to encode � and/or �,
which is consistent with the cellular downlink with low-fixed-rate D2D underlay scenario. It is also
noteworthy that the SIC concept can be applied to several interferers, but as shown in [18], most of its
throughput gain is obtained by cancelling a single interferer.
In the literature there are several approaches to SIC, however, when it comes to OFDM systems, there
are two general types based on how the symbol estimates for cancellation are devised.
In the Hard SIC, the cancellation is based on a hard decision symbol estimate which means the
estimates take values from one of the modulation constellation points. In such case, if there are too
many decision errors in the estimate, this strategy could worsen the performance compared to not using
SIC. Therefore, when adopting this technique, SIC is only performed when a CRC check succeeds
which, in LTE, can either be the one belonging to a CB or TB. In [19], both approaches were tested and
21
the Hard SIC receiver based on CB CRC has practically no gain over the one based on TB CRC. Thus,
it is more efficient to use the latter.
On the other hand, in the Soft SIC, the cancellation is centered on soft decision symbol estimates which,
in turn, are based on bit likelihood values provided by the decoder, not necessarily corresponding to any
modulation constellation point. This type of decoders provides an increase in performance when
compared to Hard SIC at a cost of higher complexity [19].
As previously explained, SIC is an interference mitigation technique. However, it can also be considered
as a multiple packet reception scheme for it allows various users to share the same resources. In fact,
if one is to interpret the interferers as yet other sources of useful information (from other devices), one
can readily understand that SIC permits the coexistence of multiple links.
SIC is therefore an extremely useful tool, especially so in the cellular downlink with low-fixed-rate D2D
underlay scenario that is being considered in this work, where a given BS is transmitting to a given UE
(I2D link), while a MTD is also communicating with the same UE, D2D link (refer to Chapter 3 for a more
detailed description). It thus follows that in order for this system to operate in an appropriate fashion, LA
strategies are paramount. SIC is not always applicable and, thus, in this context, LA strategies appear
not only as a means to adapt the link to the fading conditions, but also to limit the SIC outage regions
by adjusting the MCSs and hence the operation thresholds.
In order to select the MCS several algorithms can be applied. In this study, the aim is to select the MCS
that maximizes the expected instantaneous efficiency under the constraint that the estimated BLEP is
smaller or equal than the target BLER. It should be stressed that the focus is solely on the selection of
the MCS which means that it is assumed that a power control scheme is in place such that the expected
received SNRs of each link are within the operating limits of the system. Another consideration, and as
previously explained, is that LA in LTE is the process of selecting the MCS based on the CQI report
(SISO mode) made by the receiver. It thus follows that the presented strategies focus on devising
receiver-sided rules to select the CQI that is to be reported to the BS. Alternatively, since the CQI report
is assumed instantaneous and that there is a one-to-one relationship between CQI and MCS, these
strategies could also be applied at the BS (transmitter) provided that the instantaneous CSIs at the
transmitter are known.
In the following subsections, the author particularizes this optimization problem for four scenarios. In the
first scenario, D2D and I2D links use orthogonal resources, meaning that, from a LA standpoint, it is as
22
if there is only a single link without interference. In the second scenario, D2D and I2D share the same
resources, but the receiver is not capable of performing SIC - equivalent to the case where there is a
single link with interference. In the other two, both links share the resources and the receiver is SIC-
capable. The difference between the two resides in the knowledge of D2D CSI and, as such, two
different strategies are formulated: best-case and worst-case rules.
First consider the scenario where there is only a single link (I2D) without interference. Let � denote the
index of a given MCS, { the set of available MCSs such that � ∈ { ∶D ~1, … , �� and that a higher MCS
index corresponds to a more efficient (and less robust) MCS. Each MCS has thus its own spectral
efficiency, denoted by �;�<, and operation threshold Γ� that corresponds to the point at which the �th
MCS experiences a BLEP equal to the target BLER (?) , i.e. ��h�;�< D ?. Recalling that 5 denotes the
received instantaneous SNR, the focus is to maximize the expected instantaneous efficiency (equivalent
to throughput) under the constraint that the estimated BLEP is smaller or equal than the target BLER. It
is, thus, obvious that this problem can only be solved if 5 l ΓU. Provided that this condition is met, the
optimization problem for this scenario can be written as:
max�∈{ �;�<. �1 J ��h�;5, �<� .�. �.��h�;5, �< � ?
(11)
In LTE, however, the available MCSs are spaced in such way that the highest MCS that meets the target
BLER (10% in LTE) for a given SNR is generally the scheme that allows the maximization of the
expected instantaneous efficiency. This results from the fact that the great majority of MCSs were
designed such that the expected instantaneous efficiency of the highest satisfying MCS is always
greater when compared to the second highest. And for the other few MCSs, this does not hold only for
a small fraction of SNRs, much due to BLEP decreasing rapidly once the target BLER is met. That said,
and recalling that the MCS indices are sorted in terms of increased efficiency, the optimization problem
can be simplified into:
max�∈{ � �. �.��h�;5, �< � ?. (12)
Based on this approach, the values that need to be stored are the operation thresholds Γ�. The algorithm
then consists in finding the highest MCS whose threshold is smaller or equal than the received SNR.
Consider now the scenario where the single link is also affected by interference. In this case, the
optimization problem can be written as in (11) and, consequently, (12) , noting that the metric to measure
the link quality becomes SINR (=) instead of SNR (5) assuming that the interference can be treated as
Gaussian noise. SINR is thus the metric that is used in the algorithm when determining the MCS. It
should be noted however that this assumption naturally introduces errors when estimating the actual
23
BLEPs. Also note that the problem is only solvable if = l ΓU, which means that the system is in outage
not only due to a deep fade, but also if the interference is strong.
In this subsection, D2D and I2D links are assumed to share the resources, the receiver is able to perform
SIC and the CSI of both links is known. In this case, the focus is still to maximize the expected
instantaneous efficiency of the I2D link by choosing the MCS, but under the constraint that the BLEPs
of each links are smaller or equal than the target BLER:
max�∈{ �;�<. ;1 J ��h��< �. �.��h�X;=X, =�, 5X , ��X , �< � ?��h��;=X, =�, 5� , ��X , �< � ?
(13)
where the indices � and � specify the transmit entity and consequently the link, i.e. D2D and I2D links
respectively. Recalling the SIC principle and that the D2D MCS is fixed (��X), ��h�X can be written
as
��h�X;=X, =�, 5X, ��X , �< D ��h�;=X, ��X<J��h�;=X, ��X<. �1 J ��h�;=� , �<�. �1 J ��h�;5X , ��X<�. (14)
Note that in comparison to the non-SIC case, the BLEP is smaller. In fact, the first term corresponds to
the case where the receiver attempts to decode the desired signal without interference cancellation,
thus making use of SINR. However, there is also the possibility in which the receiver, despite not being
able to decode the desired signal at first, is able to effectively cancel the interference and decode the
desired signal after perfect cancellation. These three events are independent as reflected by the
multiplication in the second term. It should also be noted that in order for ��h�X to satisfy the target
BLER, at the very least ��h�;5X, ��X< must be smaller than the target BLER, but this is assumed to
be satisfied at almost all times given that ��X is supposed to be robust and the power at which the
MTD is transmitting is high enough, meaning that 5X lΓX unless a deep fade occurs.
Similarly, ��h�� is
��h��;=X, =�, 5� , ��X , �< D ��h�;=� , �<J��h�;=� , �<. �1 J ��h�;=X, ��X<�. �1 J ��h�;5�, �<�. (15)
At this point, a possible algorithm would be to compute ��h�X and ��h�� for all possible I2D MCSs
according to (14) and (15) respectively, determining the feasible set of I2D MCSs followed by the
selection of the I2D MCS that maximizes the expected instantaneous efficiency for that link. If a deep
fade occurs in the D2D link, meaning that if the D2D signal cannot be decodable even after perfect
cancellation, the first constraint cannot be met. In such case, a possible strategy would be to completely
24
disregard the first constraint, meaning that the problem would degenerate into the single link with
interference scenario. Still, given that this event should be unlikely to occur, the average ��h�X (over
time) would satisfy the target BLER provided that the power at which the MTD transmits is appropriate.
However, this approach leads to two potential inconveniences. First, it requires the storage of a large
number of BLEPs for all the possible MCSs, much like in the case described in the previous subsection
prior to the simplification. Secondly, the assumption that interference caused by the other link can be
treated as white noise means that the computed BLEPs might not reflect the actual BLEPs, potentially
leading to QoS degradation.
To tackle these problems, the formulation is simplified while also adding some robustness to the
solution. For this purpose, consider that BLEP can only take values from ~0,1�, being 1 if SINR (prior to
cancellation) or SNR (after perfect cancellation) is smaller than the operation threshold for a given
modulation and 0 otherwise. For instance, prior to cancellation where SINR is used, the BLEP
corresponding to the �th MCS is:
��h�;=, �< ≅ �1, = a ΓM0, = l ΓM. (16)
This intuitively means that optimal coding, rather than sub-optimal coding, is being assumed. In this
case, if the threshold is not met, the message is always undecodable; but, once met, the message is
always decodable. The former is a slightly pessimistic approximation and is responsible for adding
robustness to the solution. The latter is an optimistic approximation, but valid given that BLEP decreases
rapidly once the threshold is met, so much so that a small increment in signal strength allows ��h� ≪?.
Following this simplification, there are only two cases to be evaluated. Recalling that ��X is fixed, if =X lΓX, the first constraint in (13) is automatically satisfied and the second degenerates into ��h�;5� , �< � ? which is akin to the single link with no interference case. On the other hand, if =X aΓX,
the second constraint becomes ��h�;=�, �< � ?, forcing the I2D MCS to be chosen such that its
associated threshold verifies the following condition: Γ� �=�. This in turn means that ��h�;=�, �< ≅ 0
and the first constraint simplifies into ��h�;5X, ��X< � ? which, as explained earlier, is independent
of I2D MCS and is satisfied for most cases, unless a deep fade occurs. This is thus akin to the single
link with interference case.
For practical purposes, the procedure can be explained as follows. First, the receiver evaluates if there
is a high chance of the D2D signal being decodable without applying SIC such that the target D2D BLER
is satisfied. This evaluation is done by comparing the instantaneous D2D SINR (=X) with its fixed MCS
threshold ΓX. If =X lΓX, then when adapting the I2D MCS, one can assume that the interference
caused by the D2D link can be removed by means of SIC and thus, rather than using the I2D SINR (=�)
to determine the MCS, I2D SNR (5�) is used instead. On the other hand, if the former condition is not
satisfied, =� is used such that the I2D signal can be decoded without the need of SIC. Notice again that
25
the only values that need to be stored are the thresholds corresponding to each MCS, seeing as the
selection of the MCS to be used by the I2D link consists in finding the highest MCS whose threshold is
smaller or equal than the received I2D SNR or SINR.
It follows that this simplified rule for the best-case scenario, in which the CSI of both links is known, can
be applied by the LTE SIC-capable receiver according to the following pseudo-code:
Similar to the previous case, consider again that D2D and I2D links share the same resources and that
the receiver is able to perform SIC. Thus, the I2D link adaptation problem can still be expressed by (13),
but now the D2D CSI is not known which naturally leads to a different approach.
The problem of not knowing the D2D CSI arises from the fact that, in the context of M2M
communications, MTDs might exhibit intermittent activity, rather than continuous. However, the receiver
is only able to report the CSI estimate if transmission occurs, given that this estimate is computed from
reference signals. Thus, in such scenarios, the receiver can report an accurate I2D CSI due to
continuous transmission, but, since D2D communications occur intermittently, the reported D2D CSI
can be fairly outdated, reporting an estimate that might be highly inaccurate.
In order to formulate this rule, assume again the use of optimal channel coding which leads, as explained
in the previous subsection, to two potential cases: =X lΓX and =X aΓX.
1. ΓX D get_threshold;MCS�< // fixed MTD MCS – known a priori 2. ������ TTI£� // new channel conditions (I2D and D2D)3. ¤�=� lΓX ¥ �¦ // determine effective I2D channel quality metric4. SINR©ªª D 5� 5. �«¬� 6. SINR©ªª D =�7. �¦£¤�8. CQI_idx D get_max_MCS;SINR©ªª< // SINR-CQI mapping9. report;CQI_idx< 10. �¦£���
FIGURE 12. SIC BEST-CASE RULE
26
Recall that if the former is met, the MTD signal can be decoded without interference cancellation. This
in turn means that the effective I2D channel quality metric becomes its SNR (5�) since the interference
caused by the D2D link can be decoded and completely removed.
On the other hand, under the latter condition, the MTD signal becomes undecodable without interference
cancellation, provided that 5X lΓX. Therefore, I2D MCS must be chosen according to I2D SINR (=�).
However, D2D CSI, or equivalently 5X, is unknown and, consequently, =� is also unknown. Arises thus
the need to determine the worst =�. For this purpose, first notice that
=X = 5X1 + 5� < ΓX ⇔ 5X a ΓX. ;1 + 5�<. (17)
As a result, I2D SINR satisfies the following condition:
s=� = 5�1 + 5Xt > s 5�1 + ΓX. ;1 + 5�< ∶= =�²t, (18)
where =�² corresponds to the infimum and is the I2D channel quality metric used to determine I2D MCS.
Similar to the best-case rule, the selection of the MCS consists in finding the highest MCS whose
threshold is smaller or equal than the I2D channel quality metric. This rule is summarized in the following
pseudo-code:
At this point, it should be stressed that by choosing the I2D MCS according to this metric, ��h�� ≤ ? is
guaranteed if =�² is within the operating limits (i.e., =�² ≥ ΓU). In fact, as detailed in [6], in theory, i.e. for a
theoretic system in which the BS uses a capacity-achieving Gaussian codebook, zero outage for the
I2D link is achieved.
Additionally, the second constraint in (13), i.e., ��h�X ≤ ?, should also hold given that I2D MCS (���)
is chosen according to =�², which, in turn, means that in general ��h�;=�, ���< ≪ ?. It is again required
that 5X ≥ ΓX.
1. ΓX = get_threshold;MCS�< // fixed MTD MCS – known a priori 2. ������ TTI£� // new channel conditions (I2D)3. SINR©ªª = _³
Uwc´.;Uw_³< // effective I2D channel quality metric 4. CQI_idx = get_max_MCS;SINR©ªª< // SINR-CQI mapping5. report;CQI_idx< 6. �¦£���
FIGURE 13. SIC WORST-CASE RULE
27
Finally, note that
limµ³→i;=�²< = lim_³→i s 5�1 + ΓX. ;1 + 5�<t = 1ΓX. (19)
This fact underlines the importance of using a robust ��X such that ΓX is low; otherwise, I2D
throughput is severely limited even when =� → ∞.
28
29
Scenario Description and
Simulator Specification
This chapter provides a detailed description of the cellular downlink with fixed-low-rate D2D underlay
scenario, specifying the devices, their interactions and the assumed channel models. The rest of the
chapter focuses on the implementation of the simulator and the description of the parameters that were
considered in the simulations and whose results are presented in the next chapter.
30
Consider the topology depicted in Figure 14 which consists of two Single-Input Single-Output (SISO)
links. The link denoted by D2D refers to the transmission of the MTD data to a given UE, whereas the
I2D targets the same UE but has the BS as the transmitting entity. The D2D is considered to be a low-
rate link when compared to the I2D and, given that both are assumed to use the same time-frequency
resources the former has, in general, a lower channel-coding rate. It is also assumed that the BS is able
to dynamically adjust its rate as opposed to the MTD which operates at a fixed rate.
Another assumption is that the system operates under non-line-of-sight conditions such that these links
are characterized by two independent block flat Rayleigh fading channels whose gains remain constant
over each transmission time interval (TTI), which is considered to be equal to the length of one LTE
subframe, and over all the assigned subcarriers. Furthermore, it is assumed that the UE has perfect CSI
knowledge so much so that no estimation error arises when determining the channel gain estimates and
the noise power. For LA purposes, the UE computes the CQI to be reported to the BS according to a
given rule. In turn, this CQI report is assumed to occur in an instantaneous fashion so that the BS can
apply the most appropriate MCS for each instantaneous channel realization. It should be noted that, in
a real scenario, rather than instantaneous, the CQI report has a delay, but if one considers that the
fading is sufficiently slow, the reports are still accurate enough, validating the simplification. Notice,
however, that, for fast fading channels, this simplification no longer applies and those report parameters
need to be modelled accordingly.
Since the goal is to evaluate the behavior of the system under the aforementioned scenario, let the post-
detection received signal on subcarrier ��, be defined as
' �� = �X ��#X �� + �� ��#� �� j ,, (20)
where #X �� and #� �� are the QAM symbols on subcarrier �� and transmitted by the MTD and the BS
respectively; �X �� and �� �� are the complex coefficients on subcarrier �� of the two independent
channels such that �X ��, �� ��~¸¹;0, 1< and , represents the white Gaussian noise with variance
FIGURE 14. LINK LEVEL TOPOLOGY
31
@AB , i.e., ,~¸¹;0, @AB<. It is noteworthy that h |�X ��|B� D h |�� ��|B� D 1, therefore the average SNR
of each link on subcarrier �� can be written as
56 ��dddddd = h º|�� ��|B�� @AB » D ��@AB , � ∈ ~�, ��, (21)
where �� D h |#� ��|B� is the average transmitted power of the respective entity (MTD or BS).
At this point, it is also convenient to introduce the average SINR on subcarrier �� of both links as
=6 ��dddddd D h ¼ |�� ��|B��@AB j ½�M ��½B�M¾ D h º 5� ��1 + 5M ��» , ;�, �< ∈ ~�, ��B, � ¿ �. (22)
Furthermore, particularizing for a flat fading channel, �� �� = �� À�, ∀;�,�<. Thus, all subcarriers
experience the same instantaneous and, consequently, average SNR and SINR, meaning that the �
index can be dropped.
The METIS project is a European consortium in which several telecommunication vendors, operators
and academic organizations work together to provide technical analysis for future wireless systems often
referred as 5G systems [20]. The project, among other aspects, defines several simulation cases in
which the proposed systems are to be evaluated. This framework not only describes the possible
environmental models, but also specifies the deployment of the network infrastructure along with the
propagation, traffic and mobility models.
In this thesis, the case considered is based on Test Case 2, termed Dense Urban Information Society.
The environment consists of a grid model based on the city structure of Madrid that, according to the
consortium, provides a more realistic non-homogenous building layout than the Manhattan grid and is,
thus, capable of capturing real life effects more accurately. The reader is advised to consult [21] for an
in-depth description.
The building layout considered for this scenario is depicted in Figure 15 and it consists of four 8-floor
square buildings. The BS is placed in the southwest corner of the roof of building 2. On the other hand,
the user (UE) is inside building 3 and is stationary during each measurement, but, for each floor, several
different positions that are uniformly distributed are picked. Additionally, this user has an on-body sensor
32
(MTD) which directly communicates with the corresponding UE. It is assumed that the only source of
interference is caused by the fact that the D2D link can share the same resources as the I2D link.
In this subsection, the implemented simulator is specified. The link level simulator is based on a publicly
available LTE simulator [22]. Thus, the information here presented focuses on the modifications that
were applied to that particular simulator along with the specification of simulation parameters.
It should be also noted that no calibration procedure had to be conducted since the original LTE
simulator was already proved to be LTE standard compliant [22] [23].
The link-level simulator consists of two transmitters and one SIC-capable receiver. Recalling the
scenario described in 3.1.1, it then follows that the transmit entities are the MTD and the BS, while the
UE is modelled by the receiver. Each link has an independent channel which is characterized as being
a block flat Rayleigh fading channel. Table 2 specifies other simulation parameters.
FIGURE 15. LAYOUT FOR THE INDOOR ON-BODY MTD SCENARIO
33
TABLE 2. LINK LEVEL SIMULATION PARAMETERS
In order to implement the preliminary channel model (small-scale fading and noise) the following
approach was adopted. First, recall that, in a multipath fading channel (without AWGN), the received
signal, in the time domain, can be described by equation (3). Particularizing this formula for a flat
channel, one obtains:
1 �� D *�-L � J )��, (23)
where *� is the complex tap gain and )� the corresponding path delay. If one further particularizes for
the case where there is no path delay, )� D 0, and now recalling equation (4), one can write, in the
frequency domain:
' �� D �L#L ��, �L D *�, (24)
which means that not only the channel coefficients are the same for all subcarriers, they are also equal
to the complex tap gain defined in the time domain. Additionally, a memoryless block fading channel is
being considered and, as a result, the channel coefficient remains unchanged during one TTI, after
which a new uncorrelated coefficient is generated for the next TTI. In turn, since the amplitude of the
coefficients has to follow a Rayleigh distribution, |*�|~Â, and the expected value of its squared
amplitude was considered as being equal to one,h |*�|B� D 1, the coefficients are drawn from a
Parameters Value
Number of transmitters 2
Number of receivers 1
Transmit mode SISO
Available MCSs 27
Retransmissions Not Modeled
Overhead Not Modeled
Target BLER (Ã) 10%
Bandwidth 1.4 MHz
Number of RBs (ÄÅÆÇÈÉ) 6
Number of subcarriers/RB 12
Number of subcarriers (ÊËÌË) 72
Channel type Flat Rayleigh
Filtering Block Fading
Subframe duration (TTI) 1 ms
Frame size 10 TTIs
OFDM symbols / TTI 14
Subcarrier frequency spacing (ÍÎ) 15 kHz
CP length Normal
Number of FFT points (ÊÎÎË) 128
34
standard complex normal distribution, *�~¸¹;0, 1<. It should be noted that two links are being
modelled which naturally requires the generation of two independent channel coefficients (*X and *�)
per TTI.
On the other hand, in order to model the received signal, the expected SNRs of both links,5Xdddd and 5�ddd, are set a priori. Bearing in mind that MTD and BS are modelled by the same transmitter model which
generates OFDM signals of average unit power (-X and -�), arises thus the need to adjust their transmit
power accordingly. This results in the following instantaneous signal in the time domain:
1 D �X *X-X j��*�-� j 4,, (25)
where �X and �� are the average transmit powers, , is the white Gaussian noise such that ,~¸¹;0,1< and 4 D Ï����/���� is a scaling parameter whose purpose is to guarantee that 5X and 5� refer to the SNRs after detection (post-FFT) at the receiver as this allows direct comparisons to
theoretic results [23]. On a side note, since noise power is defined as being one, the average SNRs,5Xdddd and 5�ddd, are numerically equal to the corresponding average transmit powers, �X and ��.
As previously described, the transmit entities are the MTD and the BS, both of which are modelled as
two separate LTE downlink transmitters whose model is shown in Figure 17. Notice that the signal
processing chain was already described in Chapter 2. It is however worth mentioning that, during the
scrambling operation, different scrambling sequences are generated according to the physical level
identity that is assigned to MTD and BS. Additionally, overheads are not modeled.
FIGURE 16. SMALL-SCALE FADING PLUS NOISE CHANNEL
35
As previously discussed, there are two types of SIC strategies that are applicable to LTE. Rather than
applying Soft SIC, the implemented receiver follows a Hard SIC strategy. It is known that Hard SIC does
not reach a performance as high as Soft SIC, but, given the context of this work whose main purpose is
to evaluate different LA strategies under the cellular downlink with low-fixed-rate D2D underlay, adopting
a Hard SIC strategy does not compromise this objective and has the added benefit of being significantly
less computationally intensive, allowing thus simulations to run faster. That said, the implemented
receiver can be described as a Hard SIC receiver based on TB CRC checks.
The receiver model is shown in Figure 18 and it can be observed that to perform IC, there is the need
to re-encode the decoded message. Additionally, the UE has perfect knowledge of both CSIs and, as
such, rather than estimating the CSIs based on reference signals, genie information is used instead.
Another aspect worth mentioning is the equalization process. The implemented receiver applies ZF
(Zero Forcing) frequency-domain equalization such that the weight to be applied to each received
symbol is
�!" D 1�L *L , (26)
FIGURE 17. BLOCK DIAGRAM OF THE LTE DOWNLINK TRANSMITTER
FIGURE 18. BLOCK DIAGRAM OF THE IMPLEMENTED LTE RECEIVER
36
where �L is the average transmitted power of the signal to be decoded and *L the corresponding
channel coefficient. Recall that the channel is block flat, thus *L is the same for all the detected symbols
within one TTI.
After equalization, the bit LLRs (Log-Likelihood Ratios) are generated by the soft symbol demodulator,
based on a soft sphere algorithm [22], [23], [24]. All the other unspecified functional blocks correspond
to the dual operations of those performed at the transmitter and the only noteworthy aspect is that for
the Turbo decoder an 8-iteration max-log-MAP algorithm was chosen.
In the literature, namely where SIC is applied to LTE systems, the focus is on cancelling transmissions
from multiple antennas, MIMO transmission, pertaining to the same transmit entity [19], [25]. In such
case, all the signals share the same MCS, it thus follows that the optimal order of decoding is to sort the
signals according to their SINR such that the signal experiencing the highest SINR is decoded first. In
the case that is being studied, this does not apply, seeing that MTD and BS generally encode their
messages with different MCSs. Consequently, the rule to determine the order of decoding is not based
on which link experiences the highest SINR, but rather on how great the margin between the
experienced SINR and the threshold associated to the given MCS is. Recalling that =X and =� are the
post-detection instantaneous SINR of the D2D and I2D links respectively, while Γ� and ΓÐ are the
thresholds associated to MCS� and MCSÐ, the implemented SIC process can be described as follows:
FIGURE 19. IMPLEMENTED SIC ALGORITHM
1. ΓX = get_threshold;MCS�< // fixed MTD MCS – known a priori 2. ���TTI£� // new channel conditions3. Γ� = get_threshold;MCSÐ< 4. ¤�=X J Γ� ≥ =� J ΓÐ¥ �¦ // determine the decode order5. sU D M; sB D B; 6. �«¬� 7. sU D B; sB D M; 8. �¦£¤�9. decode;sU<
10. ¤�TB_CRC_check DD true¥ �¦
11. cancel;sU<; decode;sB<; 12. �«¬� 13. decode;sB< 14. ¤�TB_CRC_check DD true¥ �¦15. cancel;sB<; decode;sU<; 16. �¦£¤�17. �¦£¤�18. �¦£���
37
Notice that, in order to obtain smooth performance results, even if the margins are negative the
implemented receiver still attempts to decode. Similarly, if the receiver cannot decode the signal that
has been determined to be the first, it still attempts to decode the other signal. If successful, cancels the
contribution of the latter and re-attempts to decode the former.
The supported MCSs are shown in Table 3 and are the same as those used in [26]. It should be noted
that, in real-life LTE systems, the CQI is a 4-bit index, only allowing to report up to 15 different MCSs
[12]. Therefore, in such system, the transmitter (BS) uses the CQI report allied to other factors, such as
ACK/NACK rate, to determine the MCS. In this work, the author considered that the CQI is not restricted
to 4 bits such that the receiver is able to report any of the available schemes. The MCS selection, for
the I2D link, is thus delegated to the receiver. On the other hand, the D2D link uses a fixed MCS
corresponding to CQI 1 and as such there is no need to report the CQI since LA is not performed.
Furthermore, since there is a one-to-one relationship between the CQI and the chosen MCS, these
terms will be used interchangeably henceforth.
TABLE 3. CQI-TO-MCS LOOK-UP TABLE
Index Modulation Code Rate Index Modulation Code Rate
1 4-QAM 1/9 14 16-QAM 0.54
2 4-QAM 1/6 15 16-QAM 0.58
3 4-QAM 0.21 16 16-QAM 0.61
4 4-QAM 1/4 17 16-QAM 2/3
5 4-QAM 1/3 18 16-QAM 0.73
6 4-QAM 0.42 19 16-QAM 4/5
7 4-QAM 1/2 20 64-QAM 0.58
8 4-QAM 0.58 21 64-QAM 0.62
9 4-QAM 2/3 22 64-QAM 2/3
10 4-QAM 0.73 23 64-QAM 0.70
11 16-QAM 0.43 24 64-QAM 0.74
12 16-QAM 0.46 25 64-QAM 4/5
13 16-QAM 1/2 26 64-QAM 0.85
27 64-QAM 0.90
As previously mentioned, link adaptation is designed to dynamically assign a suitable CQI to match the
channel conditions. In order to achieve this, the receiver determines the effective SINR and chooses the
CQI based on a look-up table whose results were obtained from a SISO AWGN simulation for each
MCS such that a BLEP lower than 10% is achieved. These results are shown in Figure 20 and Figure
21 and they were obtained from 5000 TTI simulations with 0.1 dB steps.
Recall that in frequency-selective fading channels, distinct subcarriers generally experience different
SINRs and, as a result, there is a need to calculate an effective SINR based on an ESM method such
38
that there is a close match between the BLEPs of the equivalent AWGN channel and the real fading
channel. However, in a block flat fading channel, all subcarriers experience the same SINR conditions
within a TTI and, as such, this calibration step can be avoided since its performance for a given SINR is
similar to that of the AWGN assuming that the interference can be treated as Gaussian noise.
FIGURE 20. BLER AWGN CHANNEL
FIGURE 21. OPERATION THRESHOLDS
39
Additionally, it should be noted that if a link experiences a SINR below the threshold of the lowest CQI
(CQI 1), then the BLER will increase. On the other hand, for a SINR above the threshold of the highest
CQI (CQI 27), the BLER will decrease. It is also noteworthy that even if the SINRs fall within the range
depicted in Figure 21, it is expected that the BLER will be substantially lower than 10% because, despite
having 27 schemes, the spacing is roughly 1 dB and, from Figure 20, it can be seen that there is a sharp
decrease in BLEP with SINR.
In this study, four different operation modes were considered and are the following:
1. Orthogonal mode (Ortho): D2D and I2D links occur simultaneously, but different
resources are assigned to each link. There is thus no interference, at the expense of
having to allocate twice the bandwidth. The adaptation of the I2D link follows the single
link with no interference strategy, i.e. SNR.
2. Legacy mode (Legacy): D2D and I2D links occur simultaneously and share the same
resources which leads to interference. However, the receiver (UE) is not capable of
performing SIC. It then follows that the I2D LA is based on the single link with interference
strategy, i.e. SINR.
3. SIC mode with best-case rule (SICBCR): Similar to the previous mode in the sense that
both links operate at the same time and share the same resources. The UE is now a SIC-
capable device and the LA strategy follows the best-case rule
4. SIC mode with worst-case rule (SICWCR): Same conditions as the previous mode,
except that the D2D CSI cannot be determined when performing LA. The strategy is thus
based on the worst-case rule.
The last two operation modes are the proposed modes, while the other two only serve as a benchmark.
The link level model presented in the previous subsection allows to run simulations by specifying the
average SNR for each link. For this particular scenario, path losses, antenna gains and the noise figure
of the receiver also need to be modeled, the average SNR for each link becomes
567 ]�� D �� j �� j �Ö J �� J ��Ö J @AB ,� ∈ ~�, ��, (27)
where
40
@AB ]�0� D �� j 10 logU�;��<, (28)
��, �� and �� denote transmit average power, transmit antenna gain and path loss corresponding to the
D2D and I2D links (M and B indices respectively). The receiver is also characterized by a noise figure, ��Ö, and antenna gain, �Ö. The values of these parameters and other considerations are summarized
in the following table [21], [27], [28]:
TABLE 4. INDOOR ON-BODY MTD SIMULATION PARAMETERS
Notice that the path loss is assumed to be fixed for the D2D link which, in turn, allows to determine the
corresponding average SNR. Plugging the parameters in (28) and (27), 5Xdddd ≈ 44.67)� is obtained.
On the other hand, in the I2D link, the average SNR can only be computed after a position has been
selected. For this purpose, the approach is to first calculate the I2D path loss map whose propagation
model is referred as Urban Macro Outdoor-to-Indoor (O2I) by METIS and is described in Annex A. Then,
the set of positions is uniformly picked and the corresponding path losses are obtained. This allows to
calculate the set of average SNRs of the I2D link. From this point, the I2D SNR range is determined and
the link level simulator runs the simulations with steps of 1 dB. Additionally, since I2D SNR is real-
valued, cubic spline interpolation was applied to the outputs of the link level model, i.e. BLER and
average throughput. The length of the simulations for this scenario is 10Û TTIs.
Environment
Building
Layout
Number Floors Floor Height [m] Length x Width [m] Spacing [m]
4 8 3.5m 120x120 18
Network
Layout
BS height [m] UE Height [m] Trials
3.5 (roof #2) 1.5 500 per floor (building #3)
Network
Both Links
Carrier Freq.
[MHz]
Transmission
Bandwidth
(��) [MHz]
Noise Spectral
Density
(�Ü) [dBm/Hz]
UE Noise
Figure
(��Ö) [dB]
UE Antenna
Gain
(�Ö) [dBi]
Traffic Model
800 1.08 -174 9 0 Full Buffer
I2D
Link
BS TX Power
(��) [dBm]
BS Antenna Gain after
cable loss (��) [dBi] Small-scale Fading MCL [dB]
Propagation
Model
46 15 Block Flat Rayleigh 70 Urban Macro O2I
(PS#4)
D2D
Link
MTD TX Power
(�X) [dBm]
MTD Antenna Gain
(�X) [dBi] Small-scale Fading MCL [dB]
Propagation
Model
0 0 Block Flat Rayleigh 60 MCL is assumed
41
Results and Discussion
In this chapter, the results are presented and analyzed. The author focuses first on simulations over
SNR as they provide a broader understanding of the behavior of the different operation modes. The
results corresponding to the indoor on-body MTD scenario are presented near the end of the chapter,
followed by the discussion of the applicability of the proposed LA strategies.
42
In this subchapter, the operation modes are evaluated in respect to the individual BLER, throughput rate
per total number of assigned RBs (to both links) and aggregate efficiency (89:). The aggregate efficiency
is, in this study, defined as the product of the normalized throughput of the D2D (�X) and I2D (��) links
in regard to their respective maximum absolute throughput, after which it is divided by the total number
of assigned RBs (�����) and normalized such that max�89:� D 1 for Ortho, allowing thus to directly
compare the other modes with respect to Ortho:
89: D ;�X Ý ��<2���� ������ . (29)
The need to use the normalized throughputs, rather than the absolute values, comes from the fact that
the D2D link can only achieve a fraction of the throughput of the I2D link due to the usage of a low MCS
by the former, but both are considered to be equally important. This, thus, ensures a fair treatment. The
product, rather than the sum, allows to easily pinpoint the regions where both links are simultaneously
operating at relatively high throughputs.
As a side note, in this chapter, the notations h ���� and 5̅ are used interchangeably as both denote the
average SNR. Similarly, h ����� and =̅ stand for average SINR.
The simulations over average SNR have a length of 5000 TTIs and range from 5 dB to 80 dB with steps
of 1 dB. The lower bound results from the fact that there is only interest in testing these LA strategies if
the power control scheme is appropriate. In this circumstance, the average BLER due to fading must be
lower than the specified target BLER (10%), therefore, according to (7), the margin needs to be at least
10 dB over the first operation threshold (Figure 21).
In Figure 22, the average MCS used by the I2D link is shown. Recall that while the BS is allowed to
dynamically change its MCS, the MTD encodes its messages with a fixed MCS, i.e. MCS 1.
As a general rule, the higher the 5̅�, the higher the average MCS is, being Legacy the exception unless
low values of 5̅X (hence, high =�ddd) are involved. Comparing the SIC modes, it can be observed that
43
SICBCR uses the full range of MCSs, while SICWCR chooses more robust MCSs so much so that the
highest MCS used is 10 whose value is explained by (19) and Figure 21 (operation thresholds).
Overall, the D2D link is less prone to errors which can readily be observed by comparing the BLER of
both links (Figure 23 and Figure 24) for any given method under reversed average SNR conditions. This
is consistent with the expected behavior because the I2D link has an average MCS that is higher (hence,
a less reliable average MCS) than the one used in the D2D link.
Another remark is that, in the SIC modes, similar average SNR conditions in both links lead to a higher
BLER because, on average, there is not a strong signal that can be easily detected and cancelled
according to the SIC algorithm. Indeed, in a system where both links use the same MCS, the system
exhibits the worst performance when the received signals have similar SNR, i.e. SIR near 0 dB.
FIGURE 22. I2D AVERAGE MCS
44
FIGURE 23. D2D BLER
FIGURE 24. I2D BLER
45
However, this is not verified in the tested scenario because the BS generally uses a higher MCS than
that of the MTD and, consequently, this worst performance region is slightly shifted, occurring when 5̅� ± 5̅X. Note that this behavior also leads to the fact that higher average SNR values do not necessarily
mean lower BLERs as clearly observed in SICBCR. Furthermore, this worst performance region is not
highlighted in SICWCR due to the usage of robust MCS so much so that the actual BLERs are lower
than the minimum resolution (2 Ý 10Tà). It is also worth pointing that Ortho and the SIC modes exhibit similar BLERs for the region where the
average SNR is approximately between 5 dB and 15 dB. This is due to fading being the main contribution
to BLER. From roughly 15 dB onward, SICWCR generally experiences lower BLERs which is explained
by the use of robust MCSs. Meantime, in Ortho and to a lesser extent in SICBCR, BLER can be seen
fluctuating from 20 dB to 40 dB as a direct result of switching MCSs.
From all the operation modes, Legacy performs the worst. In fact, Legacy is the only mode where BLER
is not met (10% target). This result is intuitive because in Ortho, D2D and I2D signals do not interfere
with one another, while, in Legacy, they do. And since the latter does not perform any kind of cancellation
(unlike SIC modes), it is natural that, when the interference has a significantly higher strength, the
receiver is less likely to be able to decode the intended signal successfully, ultimately leading to system
outage.
Regarding the D2D throughput, in Figure 25, it can be observed that it is proportional to the success
probability (1 J ��h�) given that the MTD always uses the same MCS. Additionally, the throughput does
not change significantly once ��h� ≲ 10TB, so, from a throughput standpoint, there is barely no gain in
trying to achieve a lower D2D BLER. That said, all modes except Legacy exhibit similar behavior in
terms of absolute throughput, but the SIC modes are twice as efficient because they require half the
RBs.
Moving on to the I2D throughput, whose results are shown in Figure 26, it is readily seen that overall a
lower I2D BLER leads to a higher throughput for a given mode. However, since the BS has the capability
of adapting its MCS and chooses it differently across different modes, it follows that, despite achieving
a lower BLER, SICWCR does not reach a throughput as high as SICBCR. This occurs because the loss
of efficiency inherent to using a significantly more conservative MCS outweighs the gain in having a
lower BLER which results in an inferior throughput. From all modes, SICBCR achieves the best
performance as it is able to select the MCS that is likely to perform the best, given the conditions of both
links, and uses half the RBs compared to Ortho.
46
FIGURE 25. D2D THROUGHPUT PER TOTAL RBS
FIGURE 26. I2D THROUGHPUT PER TOTAL RBS
47
The fact that the SICBCR provides the best performance is further highlighted in the aggregate efficiency
plots shown in Figure 27. It achieves an efficiency that is always equal or higher than any other modes
including Ortho. It can be twice as efficient as the latter and up to four times as efficient as SICWCR.
On the other hand, Legacy clearly has the worst performance as it cannot achieve high throughputs in
both links simultaneously.
FIGURE 27. AGGREGATE EFFICIENCY
48
This subchapter is divided in two parts: intermediate and final results. The intermediate results shown
are the path losses and the CDFs (Cumulative Density Functions) of the average SNR. These allow
then to determine the final results, namely the metrics whose purpose is to evaluate the various
operation modes under the indoor on-body MTD scenario.
Figure 28 shows the computed losses that the BS signal experiences across the 8 floors. Note that only
building 3 (southwest building) is of interest. As expected, the losses are higher near the southwest
corner of each floor and on the bottom of the building due to a greater travel distance.
Recall from subsection 3.2.3 that the D2D path loss is fixed and is equal to 60 dB.
FIGURE 28. I2D PATH LOSS MAP
49
Figure 29 shows the CDF of the average SNR experienced by the I2D link. Additionally, in order to
analyze the behavior of the various operation modes in a more intuitive fashion, the CDFs of the average
SINRs were also computed and are shown in Annex B. It is apparent from (22) that calculating the exact
average SINR requires the knowledge of the instantaneous SNRs rather than the average values. In
fact, plugging the averages instead, results in the lower bound (see Annex C). Thus, in order to get a
better estimate of the average SINRs, formula (54) derived in Annex D was used.
Again, recall that the path loss for the D2D link is independent from the position of the user and,
consequently, the average D2D SNR is constant and approximately equal to 44.67 dB.
The CDFs corresponding to the average MCS used in the I2D link are shown in Figure 30.
In Ortho, since the D2D link does not cause any interference, the I2D average MCS corresponds to the
highest across virtually all positions which is explained by the fact that I2D link operates almost
exclusively in the high SNR regime regardless of the position of the user, thus leading to very small
fluctuations. In Legacy, however, the D2D and I2D links share the same resources, thus rather than
choosing the MCS according to the average SNR, the average SINR is used instead. Observing the
CDF of the average I2D SINR in Annex B, it is natural that the average chosen MCS is significantly
different depending on the position of the user.
FIGURE 29. CDF OF AVERAGE I2D SNR
50
In SICWCR, despite the interference caused by the D2D link, the I2D link uses a similar MCS for all
positions due to the MCS restriction that is imposed by the first operation threshold - refer to (19). Also
observe that in SICBCR the average chosen MCS is significantly higher.
The CDFs concerning the D2D and I2D links are depicted in Figure 31 and Figure 32 respectively. Note
that only the Legacy and SICBCR curves are shown in Figure 31. This is due to Ortho and SICWCR
exhibiting BLERs so low that all fall below the minimum resolution for a 10000 TTI simulation, i.e. ��h� a 10TÛ. Regardless, the primary goal is to evaluate if the modes meet the 10% BLER which is, in
fact, satisfied for all modes except Legacy. Also observe that all the experienced BLERs in SICBCR are
significantly lower than the target.
Although not visible in Figure 31, it is expected that Ortho achieves the lowest D2D BLERs because,
compared to SICWCR, the links do not interfere with one another and, secondly, the D2D link operates
at a fixed MCS across all modes. Conversely, despite the existence of interference, SICWCR managed
to achieve a set of I2D BLERs whose values are lower than that of Ortho because the former uses
MCSs that are more robust.
FIGURE 30. CDFS OF I2D AVERAGE MCS
51
FIGURE 31. CDFS OF D2D BLER
FIGURE 32. CDFS OF I2D BLER
52
As already stated, in the D2D link, the throughput (Figure 33) is proportional to D2D block success rate
(1 J ��h�X). Naturally, in Legacy the user experiences a wide range of throughput rates, while in the
other modes the absolute throughput is practically always the maximum. Although achieving the same
absolute throughput, twice the resources are allocated in Ortho, thus the throughput per RB is halved.
Note that even in Legacy, the receiver can experience, albeit not consistently, higher throughput rates
per RB than that of Ortho.
In Figure 34 the throughputs obtained for the I2D link are presented. It is clear that either in Ortho or
SICWCR, the range of throughputs experienced by the receiver is very contained so much so that it is
roughly 9kbps and 1kbps respectively, appearing thus as if the corresponding CDFs are vertical lines.
Despite using twice the resources, Ortho reaches a throughput per RB that is almost twice than that of
SICWCR. On the other hand, Legacy experiences a range of throughputs that practically covers the
whole range of possible throughputs, while in SICBCR the lowest throughput is around 660kbps per RB
which is greater than the highest observed in Ortho or SICWCR.
In conclusion, SICBCR is the mode that exhibits the best performance in regard to throughput per RB.
FIGURE 33. CDFS OF D2D THROUGHPUT PER TOTAL RBS
53
The CDFs of the aggregate efficiency are presented in Figure 35.
FIGURE 34. CDFS OF I2D THROUGHPUT PER TOTAL RBS
FIGURE 35. CDFS OF AGGREGATE EFFICIENCY
54
Begin by noticing the similarities between the curves of Ortho, SICWCR and SICBCR when compared
to the respective CDFs of the I2D Throughput per total RBs (Figure 34). This is explained by the absolute
D2D throughputs for these modes being maximum practically at all positions, hence �X ≈ 1. However,
the same does not hold for Legacy. In fact, the curve shrinks because both links are not able to
simultaneously operate at relatively high throughputs, validating the utility of using this metric to evaluate
the system performance and highlighting that SIC is a necessity if resources are to be shared between
links.
Meanwhile, SICWCR consistently shows an efficiency that is approximately half (53%) than that of
Ortho, while SICBCR is in the worst scenario 46% more efficient than the latter (scaling up to 100%).
At this point, it is clear that SICBCR provides the best overall performance. The knowledge of the D2D
CSI is however a requirement which implies that the D2D link needs to operate in a somewhat
continuous fashion. Under these circumstances, using the lowest MCS and bandwidth, an absolute
throughput of roughly 200 kbps can be achieved. This throughput is undoubtedly excessive in the
context of many M2M services, leading to an unnecessary use of the system resources and battery
power.
Admitting a service of 1 kbps, then one possible solution, as depicted in Figure 36, is to transmit only
once every 200 TTIs, i.e. the D2D link exhibits intermittent activity. In this case, the time span between
transmissions is so long that SICBCR is likely to not work correctly because the D2D CSI is outdated.
SICWCR arises thus as an excellent solution seeing as this information is not required in order for the
QoS to be met. Also note that since SICWCR is only active 1/200 of the time, the total efficiency loss is
negligible. It is, in fact, a 0.23% loss for the simulated scenario (indoor on-body MTD).
TTI #200 TTI #1 TTI #2 TTI #201 TTI #202
Ortho
(D2D inactive)
SICWCR(D2D active)
FIGURE 36. POSSIBLE TRANSMISSION SCHEME FOR M2M APPLICATIONS
55
Conclusions and
Future Work
The present chapter finalizes the work by providing a summary of the results and a final outlook. At the
end of the chapter, a few pointers to ideas that can be further explored are given.
56
Simulations carried in this work proved that the proposed rules meet the design criteria. SICWCR
focuses on combined link reliability by using robust MCSs. In fact, in certain circumstances, this mode
can even reach lower BLERs than that of Ortho, despite that, in the former, the D2D and I2D links share
the same resources. On the other hand, compared to Ortho, the aggregate efficiency of SICWCR is
roughly halved. However, the author cannot stress enough how important is, in the near future, sharing
resources among several links due to the massive amount of active devices (M2M communications).
So, in that sense, SICWCR is indeed a great tool to solve that incoming problem. Additionally, in such
scenarios, estimating the CSI of D2D links in an accurate fashion might prove to be difficult or
computationally intensive which further highlights the merit of this solution to the detriment of strategies
such as SICBCR in which constantly knowing accurate estimates of D2D CSIs is of utmost importance.
From all the evaluated methods, SICBCR is the mode that allows the highest aggregate efficiency to be
achieved so much so that it consistently exhibits double the performance than that of orthogonal mode.
Despite not reaching BLERs as low as the SICWCR due to the usage of more efficient and less robust
MCSs, the experienced BLERs are significantly below the target BLER.
It should be stressed however that SICBCR was evaluated under the assumption that there is perfect
knowledge of CSIs. In real systems, this does not happen as not only the CSI estimation is prone to
errors, the report of the CSI (via CQI in LTE) is also not instantaneous and occurs at periodic intervals.
Moreover, noting that to compute the estimates, transmission is necessary to occur because it uses
reference signals inserted in the resource grid, rules such as SICBCR suffer from some limitations. In
fact, if D2D communications are to operate in an intermittent fashion, which in the context of M2M
communications is likely to happen, allied to the presence of a relatively fast fading channel, reported
D2D CSI can be fairly outdated and thus highly inaccurate to the point of compromising the LA procedure
if one is to use such information. Note that this does not mean that these type of rules are not applicable
to real-life systems. In fact, the author believes that in sufficiently slow fading channels, SICBCR might
still yield a good performance.
With the advent of new paradigms, cellular systems that are able to operate at a higher efficiency are
essential. To accomplish such goal, new disruptive technologies such as D2D and M2M emerge.
However, these tools pose problems in regard to the spectrum usage, leading to the need to share the
available resources. Downlink with low-fixed-rate D2D underlay and IC techniques appear thus as
possible solutions to the latter problem. The former allows the D2D underlay to operate during the
57
downlink, adding an increased flexibility that RRM algorithms can exploit; while the latter permits the
coexistence of multiple links if one is to interpret the interferers as yet other sources of useful information.
Given the aforementioned context, the problem in this thesis was to devise appropriate LA strategies
that guaranteed the feasibility of IC for OFDM systems, namely LTE. The first stage consisted in a survey
of the existing IC techniques that can be performed in such systems and led the author to SIC whose
principle consists in decoding the messages from different devices sequentially. These findings allowed
then to design a SIC receiver. In the second stage, two SIC LA strategies (SICBCR and SICWCR) were
presented and their performance was compared with reference modes, i.e. Ortho and Legacy.
The first observation was that if resources are to be shared, SIC is indeed a necessity, because Legacy
cannot guarantee a certain QoS. Meanwhile, SICBCR provides the highest performance but requires the
knowledge of D2D CSI which might not be feasible in many M2M scenarios. On the other hand, SICWCR
can guarantee a certain QoS despite not knowing the D2D CSI, but its performance is roughly half than that
of Ortho.
Regarding the future work, it was noted that for SICWCR the maximum MCS is heavily conditioned by
the MCS used by the MTD which, in turn, affects the I2D link efficiency. In this work, the MCS chosen
for the MTD was 4-QAM with a 1/9 channel coding rate. Evaluation of the system performance for
MCSs even more robust, i.e. lower channel coding rate or Binary Phase-Shift Keying (BPSK), is a
possibility. Note that using such MCSs should not affect the services that can be provided by the MTDs
since those services tend to require extremely low throughput rates.
Additionally, the LA strategies here devised can be applied to frequency selective channels with minor
modifications, namely with the addition of a compression method (MIESM or EESM) to compute the
effective SINR. Performance evaluation under such channel conditions could then follow. Similarly,
SICBCR could be reworked by not considering the assumption of ideal coding.
Alternatively, one could forego the assumption of an instantaneous CQI report and perfect channel
estimation by applying a scheme that allows to model periodic CQI reports with delay and channel
estimation through the use of reference signals. The former would then imply the need for a new channel
model in which correlation among channel coefficients exist. Slow and fast fading conditions, namely
those standardized by 3GPP (EPA, EVA), could be evaluated. In order to mitigate the problem of
periodic CQI reports (periodic uplink frames), one could admit that instead of just sending one CQI report
regarding the current CSI, the receiver would send several reports that span all the TTIs between upload
frames. These CQIs would then be computed based on future CSI predictions by adding a filtering
method, e.g. Kalman or particle filters. Similarly, the problem of intermittent D2D communications and
the need to know the instantaneous D2D CSI could also be solved in a similar fashion. Evaluation of the
performance and computational complexity would follow.
Other possibilities include the evaluation of these LA strategies for MIMO and/or the presence of multiple
D2D links. In this case, reformulation of both strategies would be required. However, it is worth
58
mentioning that for the particular case of multiple D2D links, a rule that does not require D2D CSI
knowledge cannot be devised because no positive downlink rate can ensure that the signal of the BS
can be decoded [6].
59
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62
63
The propagation model for the I2D link follows the same model specified by METIS in [21]. The
propagation setting is the Urban Macro O2I scenario and is listed as PS#4.
The I2D path loss is given by
�� â³� D ���� j ��ã j �� . (30)
The geometry of the outdoor propagation model is depicted in the following figure.
FIGURE 37. GEOMETRY OF THE OUTDOOR PROPAGATION MODEL (EXTRACTED FROM [21])
The first term in (30) can thus be computed as
���� â³� D ���. j �ä�. j �0.] ,�ä�. j �0.] ± 0��.,�ä�. j �0.] � 0. (31)
The free space loss is expressed by
��. â³� D J20 logU� s åæ4ç�t, (32)
where åæ is the wavelength of the carrier and � is the Euclidean distance between the BS and the user,
not factoring in the height.
The diffraction from the rooftop down to the floor height where the user is located, and whose distance
is specified by Δ*0, is given by
64
�ä�. â³� D J20 logU� è12 J 1ç arctané��ê$(ë)ìçí4å �(1 − cos(ë))îï, (33)
where
ë = tanTU ð|Δ*0|- ñ , (34)
� D òΔ*0B j -B (35)
and - is the horizontal distance between the user and the diffracting edges.
For the calculation of the multiple screen diffraction loss, the criterion for grazing incidence is
). D åæ �BΔ*óB, (36)
where Δ*ó is the difference in height between the base station antenna (*ó) and the average rooftop
(*ä). Observing that ô is the length of the path covered by the buildings, �æ is the carrier frequency and õ
the spacing between buildings, then if ô ± ). �0]. â³� D �ó.ã j �9 j �] logU� s �1000t j �� logU� ö�æ X÷ø�ù J 9 logU�;õ<, (37)
where
�ó.ã â³� D �J18 logU�;1 j Δ*ó<,*ó ± *ä0,*ó � *ä , (38)
�9 ]�� D ü54,*ó ± *ä54 − 0.8Δ*ó ,*ó � *äþ$)� l 500,54 − 1.6Δ*ó�1000 , *ó � *ä þ$)� a 500
(39)
�] D �18,*ó ± *ä18 J 15Δ*ó*ä ,*ó � *ä (40)
and
�� D�����0.7 ð�æ X÷ø�925 J 1ñ , ifmediumsizedcitiesandsuburbancenters15 ð�æ ´�925 J 1ñ ,ifmetropolitancenters.
(41)
65
Otherwise,
�0.] = −10 logU�(�XB ), (42)
where
�X =���������2.35éΔℎó� ìõåî
�. , ℎó > ℎä
õ� , ℎó ≈ ℎäõ2ç� ìå� s1� − 12ç + �t,ℎó < ℎä
,
(43)
� = tanTU sΔℎóõ t (44)
and
� = òΔℎóB + õB. (45)
The rest of terms in (30) are computed as
��ã_ ]³� D 9.82 j 5.98 logU�;�æ ��< j 15;1 J sin(�))B (46)
and
�� D 0.5)� (47)
where �æ is again the carrier frequency, � is the complementary angle between the vector leading to the
outdoor path and the normal of the wall of the building in which the user is located and )� corresponds
to the distance between the wall and the user. This geometry is depicted in Figure 38. The wall that is
chosen is the one that allows the minimization of ��.
At this point, it is worth noting that, in general, ���� is simply given by the free space loss. The instance
where this does not occur is when the user is located towards the southwest corner of building 3. In this
circumstance, the minimization of �� corresponds to the case in which the ray propagates over building
3 and is reflected on the borders of the map which simulates the existence of surrounding buildings,
valid for a metropolitan center.
The implementation of this propagation model for the I2D link is provided by METIS [29]. The MATLAB
script allows to specify a map composed of buildings of equal width, length and spacing and calculates
the path losses for any point in the specified map, including the outdoor points.
66
FIGURE 38. GEOMETRY OF THE INDOOR PROPAGATION MODEL
67
FIGURE 39. CDF OF AVERAGE D2D SINR (ESTIMATE)
FIGURE 40. CDF OF AVERAGE I2D SINR (ESTIMATE)
68
69
Let ����� D oS�^UwoS�� , the author proves that h ������ l � oS�^�Uw� oS���.
First, note that # ≔ ���� and ' ≔ ���M are independent random variables that only take positive
values. The expected value of ����� can then be rewritten as:
h ������ D h º #1 j'» D h #�h º 11 j '» (48)
The second term inside the expectation operator is a convex function for the given domain (note that Y
only takes positive values).
According to the Jensen’s Inequality, if � is a convex function and � a random variable then:
�;h ��< � h �;�<�. (49)
Particularizing this inequality, one can write
11 j h '� � h � 11 j '�, (50)
and applying (50) to (48), one finally obtains
h ������ D h #�h º 11 j '» l h #�1 j h '� D h �����1 j h������ .∎ (51)
70
71
First, let the author determine the second order Taylor expansion of #/' where # and ' denote positive-
valued random variables (note that if ' had any nonzero probability of being 0 it would result in h #/'� being undefined). Additionally, for ease of notation, let �� and @�B denote the expected value and
variance of a generic random variable �. Next, applying the second order Taylor expansion around
�� , ��, one obtains:
#' ≈ #' !",!#
j ;# J ��< $$# s#'t !",!#
j ;' J ��< $$' s#'t !",!#
j 12 ;# J ��<B $B$#B s#'t !",!#
j 12 ;' J ��<B $B$'B s#'t !",!#
j ;# J ��<;' J ��< $B$#$' s#'t !",!#
(52)
After some algebraic manipulation and applying the expectation operator to the result:
h �#'� ≈ �%�/ j @�B�%
�/í J @���/B , (53)
where @�� denotes the covariance between # and '.
Particularizing (53) to the case where # D ����, ' D 1 j ���M and ���is defined as per (21) such that *~¸¹;0, 1<, one obtains
h ������ D h º ����1 j ���M» ≈ h �����1 j h����M� + h����M�Bh ������1 j h����M��í . (54)
Formula (54) makes use of the fact that ���� and ���M are uncorrelated and that |ℎ |B~ Exp ;1< which,
in turn, means '(�(|ℎ |B) = 1, therefore:
'(��1 + ���M� = '(� )½ℎM½B�M@AB * D '(� ö½ℎM½Bù . s �M@ABtB = h����M�B. (55)
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