Enhanced Cognitive Femtocell Approach for Co-Channel Downlink Interference Avoidance

Sinan Khwandah, John Cosmas, Ian A. Glover, Pavlos Lazaridis, Giuseppe Araniti and Zaharias Zaharis

Sinan Khwandah and John Cosmas are with the Department of Electronic and Computer Engineering,

College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, Middlesex,

UB8 3PH, United Kingdom (e-mail: {sinan.khwandah, john.cosmas}@brunel.ac.uk).

Ian A. Glover and Pavlos Lazaridis are with the Department of Engineering and Technology, University of

Huddersfield, Queensgate, Huddersfield, HD1 3DH, United Kingdom (e-mail: p.lazaridis@hud.ac.uk).

Giuseppe Araniti is with the Department of Information Engineering, Infrastructure and Sustainable

Energy (DIIES), University Mediterranea of Reggio Calabria, Località Feo di Vito, Reggio Calabria 89100,

Italy (e-mail: araniti@unirc.it).

Zaharias Zaharis is with the Department of Electrical and Computer Engineering, Aristotle University of

Thessaloniki, 54124 Thessaloniki, Greece (e-mail: zaharis@auth.gr).

Index Terms—LTE, Femtocell, Cognitive Radio, Co-Channel, Interference, CQI, MCS.

Abstract

The deployment of low-power nodes such as femtocells within the macrocell’s coverage area is one of

the main features of future 5G networks. Although these small nodes increase the system capacity and

improve the link quality, there will be challenges in the femtocells planning and management

particularly due to the occurance of interference when they are deployed in a co-channel with the

macrocell. The cognitive radio inspired femtocell is one of the solutions to mitigate interference. In the

presented paper, the femtocell is made self-organised and performs uplink sensing to enhance the

cognitive information. In more detail, the femtocell discovers the interference-free resources of far

macro-users; in addition, it exploits the channel dependent scheduling to predict the future resources of

the nearby victim macro-users and avoids utilising them. Simulation results show that the co-channel

interference on the macrocell Downlink (DL) signal is minimal, and therefore performance improved,

under the condtion that cognitive femtocell operates adaptively and monitors the spectrum allocations

countinuously.

I. Introduction

The mobile phone industry has undergone significant evolution and introduced innovative services,

leading to a competitive market. Operators are always looking for new technologies which help to serve

this increasing growth. One of the current methods is to use broadband internet to increase radio

coverage by deploying the femtocell technology. Femtocells are beneficial to network operators and

users alike. For the former, offloading traffic from macrocells down to smaller base stations increases

network capacity [1] whereas for the users, femtocells improve the User Equipment’s (UE) reception

and the network availability. As studies have shown that the largest number of phone calls are still made

from indoors [2], femtocells can potentially bring large benefits to indoor wireless communications. One

femtocell provides coverage and support to a small number of users by utilising the customer’s internet

backhaul such as Digital Subscriber Line (DSL) to connect to the cellular network. Due to their low power

transmission, femtocells reduce the amount of interference with other electrical devices, whilst

providing improved indoor coverage. In addition, the battery life of the mobile device can be prolonged

as it only needs to connect over short distances when connecting to a femtocell, instead of to the

nearby macrocell [3].

However the presence of small base stations within the coverage of larger ones changes the

architecture of the cellular system. Network operators may prefer co-channel deployment as it provides

a higher system capacity, but it is also most likely to lead to interference [4].

Femtocell technology is evolving, and the effects of femtocells on non-femto-users such as macro-users

have been a subject of much research. Different approaches have been proposed in order to reduce and

mitigate the co-channel interference. The Almost Blank Subframe (ABS) is one method to enhance

intercell interference coordination, which is presented in [5], where the DL scenario was analysed in

order to find the required number of ABSs based on modelling the base station and UE placements.

Although the ABS method manages the interference well, it causes time-frequency resource waste for

the macrocell. Power control is exploited widely in 4G systems in order to reduce the effects of dead

zones created by the femtocell coverage. One approach for power control such as that presented in [6],

where a distributed femtocell DL power control is based on the minimum power required for the femto-

users to meet the DL outage probability. However, the major drawback in power control schemes is the

degradation of the SINR at the femto-user’s side if the femtocell reduces its transmitted power by a

large amount in order to reduce the interference level.

The cognitive femtocell has been proposed in many works such as in [7], where the femtocell is trained

to predict the traffic pattern of the macro-users in order allocate the spectrum opportunities. Another

cognitive approach, such as the one presented in [8], investigates opportunistic co-operation between

the macro-users and the femto-user so that the femto-user cannot transmit concurrently with the

macro-user and it should follow pre-defined models based on macro-users’ idle and busy periods.

Although cognitive femtocell is a promising technology, employing the cognitive approach to find the

access opportunities is not a trivial task. In LTE systems there is flexibility in assigning resources to the

users and allocations vary in response to the channel condition at the user location. In heavily loaded big

cells such as the macrocell, scheduling is highly dynamic and changes every subframe so the allocated

resource blocks in one subframe may not be allocated again to the same user in the following subframe.

This causes instability at the cognitive femto-scheduler and degrades the QoS as the cognitive femtocell

cannot sense and utilise/evacuate the resources simultaneously at every millisecond (subframe). Thus

the current cognitive information of the occupied macrocell resources may not be useful enough unless

it is enhanced by knowing the future candidate resources.

Therefore, in this paper we propose a method for robust sensing performed by the co-channel femtocell

in order to be aware of the current and future scheduling opportunities. The current cognitive approach

comprises of the DL listening to the macrocell signal to find the current scheduling and free resources,

and UL sensing to discover the remote macro-users resources such as the approach studied in [4]. The

superiority of the presented scheme is that the femtocell in addition to listening to the macrocell DL

signal and sensing the UL signal to discover the far macro-users, it is enhanced to decode the nearby

macro-user UL signal to predict future scheduling opportunities depending on the link adaptation

process and channel dependent scheduling in LTE. There is minor co-operation with the macrocell over

the X2 interface, which will be explained in the following sections, in addition to demonstrating the

advantages and possible drawbacks of the presented work. Where is the novelity

The remainder of this paper is organized as follows: in section two the proposed scheme is presented

and the importance of interference management is demonstrated in addition to the co-channel

deployment challenges and macro-user tracking. In the next section, interference avoidance

performance is evaluated by presenting the performance results of conducted simulations.

II. Proposed Scheme

a. Requirements

It is vital to the efficiency of the system that the performance of femtocell does not undermine the

activity level of the primary users, namely: the macro-users. A feature of these low power nodes is that

they are deployed without planning furthermore they can be deployed by the user at any time.

Therefore, any interference management scheme must afford priority to the macrocell and its users.

The random installation by the end-user and the ad-hoc nature of the femtocell positioning indoors

makes it difficult for the cellular operator to control the planning of these new low power base stations

(the HeNB) without any kind of self-control or intelligence added to them. The changing environment

and also the location of the femtocell, make it very important to equip the femtocell with some kind of

sensing capabilities. Therefore, it would be more efficient if femtocells are planned and introduced into

the market with self-configuration and self-optimization capabilities.

b. The co-channel deployment

Each LTE subframe consists of a control region and a data region. Co-channel interference happens

when there is collision between the control region of the transmitted subframes of the macrocell and

the control regions of the other transmitted frames from a femtocell operating on the same frequency

channel. The control signals are randomly distributed within the control region and they represent the

scheduled UEs. If the control region is corrupted, the UE will not be able to decode the information

contained in that region to find its resources and as a result it will not be able to access the network and

thus it is considered as a blind. Moreover, in the case of severe interference, the UE may not be able to

connect to the serving eNB (femtocell or macrocell).

Fig.1 Femtocell and Macrocell frame timing.

If the data region is corrupted in some places, the Hybrid Automatic Repeat Request (HARQ) mechanism

could help in restoring the corrupted data through multiple re-transmissions. Block Error Rate (BLER) of

10% is the critical recommended value for a successful reception of the BLOCK and should not be

exceeded [9]. However, there is no HARQ for the control channels, which span the entire bandwidth,

therefore they must be protected and the target BLER over them must typically be 1% or less.

In order to avoid interference or reduce it to the lowest possible value over the control channels, the

femtocell frame is shifted by three symbols relative to the macrocell’s DL frame. Fig.1 shows the frame

timing of the femtocell and the macrocell, assuming that the femtocell and the macrocell are time

synchronised. This is similar to what is presented in 3GPP technical report in [10]. Another measure that

could be performed by the femtocell is reducing the control region size from three symbols to one when

the number of connected femto-users is small, but this procedure applies only when the femto-users

have low activity level and require low number of resources.

Regarding interference mitigation over the data region, in the above-mentioned 3GPP report [10] they

rely on link adaptation process and channel dependent scheduling in LTE to mitigate the overlap over the

data channel where the victim macro-user can request scheduling over resources on which it receives

strong signals and are not occupied by the femtocells. However the efficiency of the channel dependent

scheduling depends on the availability of the vacant resources as will be illustrated in the following

sections.

c. Sensing

The femtocell self-configuration procedures mainly include spectrum sensing. The radio frequency

measurements performed by the femtocell are considered similar to the measurements performed by

the UE in the surrounding area of the femtocell. Many factors may affect the accuracy of the performed

measurements such as the power of the sensed signal, switching the femtocell off, and changing its

location from a place deep inside a building or close to a window. Therefore, the femtocell has to keep

performing the radio measurements in order to be aware of its surrounding radio environment.

For spectrum sensing and detection, many options could be utilized such as defining a power threshold

to determine the Uplink (UL) signals of interest. The threshold value depends on how successful the

femtocell is at finding resources. A higher threshold means that the femtocell selects the macro-users

which are very close, whereas a lower threshold implies that the femtocell considers more macro-users

to avoid their allocated subcarriers, which results in a low number of subcarriers being left to be utilized

by the femtocell and assigned to their femto-users as those subcarriers are assigned to the nearby

macro-users and could not be used by the femtocell for the interference avoidance considerations.

Then, the femtocell identifies the close macro-users to find their DL resources and avoids utilizing them.

The femtocell may discover free subcarriers (unused by the macrocell), in addition to subcarriers

allocated to distant macro-users. Femto-users are limited in number compared to macro-users and this

means that there are a low number of subcarriers that need to be allocated to femto-users. By

judiciously selecting the subcarriers, the femtocell interference on the macrocell DL could be avoided.

d. Search space and macro-user tracking

Fig.2a describes the proposed algorithm for interference avoidance and macro-user tracking. After start

up, the femtocell performs listening using a listening module to receive the DL reference symbols of the

macrocell. This is not straightforward and requires the femtocell to be synchronized with the macrocell

[11]. Once the synchronization signals from an eNB are audible, this eNB needs to be evaluated. The

femtocell detects the frame timing from Primary Synchronisation Signal (PSS) and Secondary

Synchronisation Signal (SSS) signals, and the DL bandwidth from the Master Information Block (MIB).

Once the femtocell discovers the macrocell bandwidth and the starting frequency fM (Fig.2c), it can

discover if there is any resource overlap with the macrocell.

The percentage of overlap depends on the bandwidth allocated to the femtocell. The femtocell may

share the whole macrocell’s spectrum (full spectral usage) or the femtocell is allocated smaller

bandwidth (partial channel sharing) (Fig.2b). After synchronization and system discovery, if the femtocell

discovers that there is an overlap with the macro-resources, it has to sense the macro-users presence

within its detection area and perform interference avoidance. The nearby macro-users could be

determined based on the Received Signal Strength (RSS) [12], and their uplink signals are captured and

demodulated through removing the cyclic prefixes and performing FFT on the received subframes.

The UE can be identified in the LTE system by different keys, whilst it is connected with its serving eNB.

UE identifiers are allocated to the user during Radio Resource Control RRC_Connection setup [13]. A

unique identifier, the Cell Radio Network Temporary Identifier (C_RNTI) is allocated by the eNB to the UE

at the moment when an attach procedure is initiated between the UE and the eNB. The UE depends on

the C_RNTI to track its data on the DL within the cell and this makes the C_RNTI a very important

identifier for marking RRC messages during connection. Another identifier such as the Radio Network

Temporary Identifier (RNTI) identifies the Physical Downlink Control Channel (PDCCH) allocations, which

carry the Downlink Control Information (DCIs). This identifier is used by the UE when it monitors the

search spaces in order to distinguish between a common search space and an UE specific search space.

The DCIs with general network information are broadcast on a common search space such as paging,

uplink power control and random access, while the DCIs with user-specific allocations are carried in the

UE’s specific search space.

Fig.2 Interference avoidance scheme

The C_RNTI thus defines uniquely which data is being sent in a DL direction within a particular LTE cell

that belongs to a particular UE. The nearby macro-user discovery is performed on the basis of a C_RNTI.

Thereby it is made possible for the femtocell to discover a relatively considerable number of macro-users

in its vicinity.

e. Future resource prediction

The femtocell performs two tasks upon sensing the UL signal of the macro-user of interest; first it locates

the macro-user’s C_RNTI in order to find its resources on the macrocell DL map. This gives the femtocell

a preliminary vision of the macrocell DL free resources in addition to resources allocated to far macro-

users. Secondly, the femtocell discovers the macro-user’s preferred subband for the next scheduling.

The femtocell performs calculations to locate the interference-free resources; i.e. free resources and

resources allocated to the far macro-users. Calculations are based on the smallest allocation unit in the

LTE system, which is the resource block (RB) (12 subcarriers, each subcarrier is 15 KHz). The bandwidth is

divided into groups of subbands and each subband includes a number of resource blocks according to

the bandwidth size as shown in Table-I.

Table-I LTE subbands and their resource blocks

BW (MHz

)

Num. ofRBs

Num. of Subbands

RBs/Subband

RBs/Last Subband

1.4 6 1 6 -

3 15 4 4 3

5 25 6 4 5

10 50 8 6 8

15 75 10 8 3

20 100 13 8 4

The femtocell uses the decoded UL information to predict the candidate subbands for the nearby

macro-users by exploiting the channel dependent scheduling process. The macrocell will try to schedule

the macro-user on its preferred subband through the channel dependent scheduling. Based upon the

SINR measurements on the DL, the macro-user reports a Channel Quality Indicator (CQI) value for each

subband including the wideband [14, 15] -to be translated to an appropriate Modulation and Coding

Scheme (MCS) index- based on the target link quality in terms of DL BLER. The wideband’s reported MSC

value is used by the serving macrocell to optimize the frame resources and differentiates the best

reported subband by its UEs, by calculating the metrics for the subbands, which depend on the

difference between the reported subband MCS and the wideband MCS, and the allocation block size.

The UE may span multiple subbands, and some of those subbands can be quite bad. If any subband goes

below the wideband MCS index, it is taken out of consideration for savings. The femtocell finds which

subband the CQI corresponds to, it obtains this information from the initial Physical Uplink Control

Channel (PUCCH) offset and CQI transmission parameters such as the periodic configuration index (which

controls the periodicity of the CQI reporting over PUCCH -the number of subframes between 2

consecutive CQI reports) and the subband report repetition count (This parameter controls the

periodicity of subband reporting before the next wideband CQI is reported) [16].

First the femtocell finds the wideband periodicity (in subframes):

CQI period (sf )=(1+N∗k )∗CQI periodicity (1)

Where N is the number of macrocell subbands, k is the subband repetition count and CQI_periodicity is

the number of subframes between 2 consecutive CQI reports.

When the femtocell gets the CQI report from the PUCCH, it finds the subframe number and computes

it’s modulo over the wideband periodicity. The femtocell finds how many subframes into the current

round:

CQI current=(10∗FN+SN)%CQI period (2)

Where FN is the frame number and SN is the subframe number, then the femtocell divides the number

of subframes by the CQI period between 2 consecutive reports to find out which transmission it is. The

femtocell finds the number of transmission as in equation 3:

CQI nth=CQI current/CQI periodicity (3)

If CQI_nth = 0, this indicates the wideband.

Subbandindex=CQI nth%N (4)

In this case the femtocell monitors the reported wideband and subbands CQI values. If the macro-user

has reported a low CQI value for one subband, the resources of this subband will be the target of the

femtocell (and vice versa for the macro-user’s preferred subband). Subbands that have high metrics are

suitable for the macro-user’s allocation and it is scheduled on one or more of these subbands depending

on the scheduling algorithm at the macrocell. When the femtocell allocates resources of a specific

subband to its UEs, it has to keep this allocation and does not switch to other resources without prior

knowledge of the vacancy status of these resources.

When the femtocell discovers the macro-user’s preferred subband (e.g. subband j) (Fig.2b), it can locate

the start-frequency fsub-j-S and the end-frequency fsub-j-E of this subband and the frequency range of the

subcarriers located in the range {fsub-j-S , fsub-j-E}. Then, the femtocell finds the matching femto-subcarriers

of the macro-subcarriers on the preferred subband (Fig.2c). Those resource blocks that include the

matching subcarriers are excluded from the femtocell allocations for interference avoidance.

f. The role of X2 interface

If an X2 interface exists between the femtocell and the macrocell through backbone connections,

flexibility in locating resources is possible, since on the one hand, the femtocell can request scheduling

information about the discovered nearby macro-users only and the macrocell may send the scheduling

information of the DL scheduling map and the candidate subcarriers to the femtocell, which firstly

enhances the sensing results, and secondly reduces the size of the femtocell’s efforts to perform

measurements. On the other hand, the femtocell may send its information about the sensed spectrum

and scheduling to the macrocell upon locating its resources. Comparing the spectrum sensing results

from the femtocell with the scheduling information from the macrocell helps to create spectrum

allocation opportunities.

However, there is a potential transmission delay over the X2 interface especially when there is huge

amount of data to be shared with a large number of nodes. If the macrocell shared its DL map

information over the X2 interface and the connection was congested, the femtocell will not be updated

on time due to aging of the received information.

The scheduling algorithm used in LTE is vendor-dependant and due to the fact that there are a large

number of macro-users waiting to be scheduled, it is not always guaranteed that the UE will be

scheduled on its preferred subband. Therefore in our proposed scheme, to reduce the overhead over

the X2 interface and enable the femtocell to predict the victim macro-user preferred subbands with

minor mismatch, the femto-macro communication over X2 interface will be limited only to reporting the

victim macro-users identifiers so that the macrocell assigns scheduling priorities to those users over

their preferred subbands; or in other situations when the macro-user reports a good value for multiple

subbands including its current subband, the macrocell keeps it scheduled over the same resources with

the aim to relieve the overhead over the femtocell scheduler through fewer interrupts of the channel

dependent scheduling process. Also in some situations where there is partial co-channel between the

femtocell and the macrocell, the femtocell reports its BW information to the macrocell so the later

schedules the victim macro-user over the preferred subband out of the co-channel region.

III. Performance Evaluation

In co-channel deployment, the percentage of the macrocell DL interference on the femtocell depends

on the location of the femtocell. In this respect, two cases could be considered; the first one is that the

femtocell is installed in a hotspot where the coverage of the macrocell is weak due to reasons of fading,

shadowing or other reasons that may create the need for this hotspot. In this case the femtocell’s

coverage would be strong enough so that it will not be affected by the macrocell’s signal. The second

case is when femtocell is deployed for offloading or in private places such as offices and houses where

the macrocell’s coverage may be relatively good. In this regard, if the femtocell utilizes co-channel

subcarriers that are simultaneously allocated to far macro-users, those subcarriers would be the

vulnerable femtocell DL resources due to macrocell interference. In this situation, interference could be

mitigated using a joint demodulation technique to recover the desired signal or by enabling interference

cancellation using multiple antennas.

OPNET 17.5 is used for simulation and the simulation parameters are shown in Table-II. The femtocell is

placed relatively far from the macrocell and the measured macro/femto pathloss shows that the

macrocell signal has negligible effect on the femtocell. There are 5 users per femtocell and 15 macro-

users. All users have IP flows to maintain resource utilization and data connectivity during simulations.

Table-II Simulation parameters

Parameter Value

Macrocell max TX power 27 dBm

Femtocell max TX power 3 dBm

UE max TX power 23 dBm

Macrocell PHY profile LTE 5 MHz FDD

Femtocell PHY profile LTE 3 MHz FDD

Macrocell DL base frequency 2.11 GHz

Femtocell DL base frequency 2.111 GHz

Path-Loss model Vehicular Env. (ITU-R M.1225)

Macro/Femto distance 550 m

Macro/femto pathloss 95 ~ 105 dB

Macro-user speed 1 m/s

Macro-user detection distance ≈ 150 m

Energy detector sensitivity -200 dBm

Antenna gain 15 dBi

Fig.3 Femtocell scheduling

The performance of the presented interference avoidance scheme depends on the vacancy of the

macrocell overlapped resources and the number of femto-users. Fig.3 depicts part of the femtocell

allocation procedure at certain time stamps and the allocation table according to the configured physical

profiles and the bandwidth on the macrocell and the femtocell. The allocation table shows that the

macrocell’s subband 0 is out of the co-channel overlapping therefore it is assigned the highest priority if

the victim macro-user requested scheduling over it and in this case the femtocell can schedule its users

over the entire allocated bandwidth of 3 MHz. Fig.3 shows the femtocell candidate subcarriers range

upon acquiring UL information.

Fig.4 System performance

The DL BLER statistic, (which represents the aggregated BLER measured at all macro-users in the

femtocell vicinity for all packets arriving from the macrocell), is used to evaluate the benefit of future

resource prediction. Fig.4a shows the BLER VS the macrocell’s overlapped resources vacancy for different

scenarios such as listening and sensing (traditional method- without prediction) and prediction with X2

enabled in addition to the X2 disabled case when the victim-macro-user did not get priority in macrocell

scheduling (assuming 5 femto-users). Furthermore Fig.4b shows the victim macro-user DL throughput VS

the number of users connected to the femtocell for different scenarios of resource vacancies.

When the percentage of vacant resources is small, (the macrocell is loaded and the number of nearby

macro-users is large), the channel dependent scheduling cannot schedule all those macro-users over the

interference-free resources as a result of the overlapped resources. Thus some of these macro-users will

be scheduled over the overlapped resources. When no-prediction is performed, the spectrum holes are

obtained by DL listening and UL sensing. Due to high demand on resources, listening and sensing will not

guarantee accurate scheduling of the femto-users without a low probability of collision with the macro-

users who are scheduled over the overlapped resources. Thus the femtocell requires an accurate

performance through predicting the future scheduling opportunities. In this case, performing prediction

showed to be more robust than DL listening and UL sensing only. Prediction enhances the cognitive

femtocell performance when the macrocell resources vacancy is low. Fig. 4a shows that in the case of

25% resource availability, the BLER is acceptable (<10%) and the femto-to-macro interference is reduced

significantly as the femtocell exploits this part of spectrum accurately. This helps the victim macro-user

to maintain a good throughput level as shown in Fig.4b.

In the case of no X2 interface is available, prediction performance is degraded due to the mismatch

between the predicted values and the actual allocated ones because the macro-user has not been

scheduled over its preferred subband. In this case prediction improved only when there is vacancy in the

macrocell overlapped resources so that the macrocell responds to the victim macro-user’s scheduling

demands.

On the other hand, it is clear from the results depicted in the figures that when plenty of resources are

available as shown in (Fig.4a) and the number of the femto-users is small (Fig.4b) there will be flexibility

in macro-users’ scheduling so that the BLER is acceptable and the victim macro-user throughput is

tolerable. This is related to the channel dependent scheduling process in LTE system where the nearby

macro-users and also the femto-user(s) request scheduling over the interference-free resources at the

macrocell and the femtocell respectively. Furthermore even if the femtocell utilised the same macro-

user’s subcarriers, the later can request scheduling on other available subcarriers.

As mentioned previously, since the femtocell is exploiting the cognitive approach, the priority is given to

the macro-users and the cognitive approach shouldn’t cause any degradation to their activity level.

However, it is also very important that the femtocell operates efficiently in the co-channel deployment in

order to achieve the benefits of this deployment such data offloading from the macrocell and increasing

the network capacity while providing a good close connection to the femto-users. Therefore another

issue considered here, concerning the cognitive approach, is the quality of service (QoS) at the femto-

users side which needs to be investigated. In Fig. 4a, if the case of 25% of the resources sensed by the

femtocell to be available, this indicates that there is at least one subband (25% of 15 RBs ≈ 4 RBs)

available for the femtocell to operate on. This is acceptable as a pair of two Resource Blocks (RBs) (in the

time domain-1ms length) is the minimum allocation unit used by the LTE scheduler while determining

the allocations on a frame. In order to investigate the QoS level at the femto-users side with low number

of available co-channel resources (4 RBs), the packet End-to-End delay statistic is presented for the

applied video conferencing for femto-users. Other deployed applications statistics such as FTP download

response time and Voice packet End-to-End delay are also presented. It can be seen from Fig.4c that the

performance of deployed applications depends the number of served femto-users and the application

type where some applications such as video conferencing consumes more resources.

Since the femtocell scheduler is considering the nearby macro-users scheduling, its performance in this

case depends on how dynamically the macrocell schedule its users. The scheduling type at the macrocell

is dynamic and offer flexibility and diversity of the resources allocation, however this may result in L1/L2

signalling load which is inefficient for scarce radio resources and also this reflects on considerable

overhead on the femtocell scheduling process. Fig.4d shows the percentage of the femtocell radio link

control (RLC) traffic increase while tracing the nearby macro-users. From the figure, it is obvious that the

femtocell RLC sublayer has to deal with more traffic when the number of nearby macro-users increases

and also when no X2 interface is considered with the macrocell (which may causes more dynamic

scheduling of the macro-users, section II – part f). Also channel reporting level contributes to the RLC

overhead increase because the CQI transmission parameters at the macrocell (subband report

repartition count) increase the computational level performed at the femtocell in case of dense

reporting of the CQI over PUCCH. Fig.4d presents a dense reporting at every 20 subframes between 2

consecutive CQI reports.

IV. Conclusion

Femtocell technology is one of the solutions for mobile networks when there is need for a capacity

boost, however, co-channel interference is the main drawback of this deployment. To address this

problem, this paper presented the co-channel interference management in macro-femto networks

based on macro-user tracking. The cognitive femtocell performed well without the need for power

control or to adapt the femtocell’s coverage area trade off. It was demonstrated that the macro-users

can exist with the femtocell in co-channel deployment as long as the femtocell enhances its cognitive

capabilities by prediction of the macro-user’s future resources. Enhancing sensing and detecting by

prediction is needed for better performance when the macrocell is loaded. The QoS at the femto-users

side and the overhead at the femtocell RLC sublayer also presented. It has been shown that femtocells

are able to avoid utilizing the same subcarriers that have been allocated to the victim macro-user. With

this dynamic algorithm, there is less need for femtocell radio planning and proper femtocell placement.

V. References

[1] N. Saquib, E. Hossain, L. B. Le and D. I. Kim, "Interference management in OFDMA femtocell networks: Issues and approaches," Wireless Communications, IEEE, vol. 19, pp. 86-95, 2012.

[2] W. Wang and Q. Zhang, "Local cooperation architecture for self-healing femtocell networks," Wireless Communications, IEEE, vol. 21, pp. 42-49, 2014.

[3] V. Chandrasekhar, J. G. Andrews and A. Gatherer, "Femtocell networks: a survey," Communications Magazine, IEEE, vol. 46, pp. 59-67, 2008.

[4] Li Huang, Guangxi Zhu and Xiaojiang Du, "Cognitive femtocell networks: an opportunistic spectrum access for future indoor wireless coverage," Wireless Communications, IEEE, vol. 20, pp. 44-51, 2013.

[5] M. Cierny, Haining Wang, R. Wichman, Zhi Ding and C. Wijting, "On Number of Almost Blank Subframes in Heterogeneous Cellular Networks," Wireless Communications, IEEE Transactions On, vol. 12, pp. 5061-5073, 2013.

[6] J. Y. Wu, Xiaoli Chu and D. Lopez-Perez, "Downlink Outage Probability of Co-Channel Femtocells in Hierarchical 3-Sector Macrocells," Communications Letters, IEEE, vol. 16, pp. 698-701, 2012.

[7] G. Gur, S. Bayhan and F. Alagoz, "Cognitive femtocell networks: an overlay architecture for localized dynamic spectrum access [Dynamic Spectrum Management]," Wireless Communications, IEEE, vol. 17, pp. 62-70, 2010.

[8] R. Urgaonkar and M. J. Neely, "Opportunistic Cooperation in Cognitive Femtocell Networks," Selected Areas in Communications, IEEE Journal On, vol. 30, pp. 607-616, 2012.

[9] TR 136 912, "LTE; Feasibility study for Further Advancements for E-UTRA (LTE-Advanced)," 3GPP, 2011.

[10] ETSI TR 136 921, "Evolved universal terrestrial radio access (E-UTRA); FDD home eNode B (HeNB) radio frequency (RF) requirement analysis," 3GPP, 2010.

[11] C. Lee and J. Kim, "System Information Acquisition Schemes for Fast Scanning of Femtocells in 3GPP LTE Networks," Communications Letters, IEEE, vol. 17, pp. 131-134, 2013.

[12] H. Laitinen, S. Juurakko, T. Lahti, R. Korhonen and J. Lahteenmaki, "Experimental evaluation of location methods based on signal-strength measurements," Vehicular Technology, IEEE Transactions On, vol. 56, pp. 287-296, 2007.

[13] TS 36.321, "Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Medium Access Control (MAC) protocol specification," 3GPP, 2013.

[14] ETSI TS 136 212, "LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Multiplexing and channel coding," 3GPP, 2013.

[15] TS 36.213, "Evolved universal terrestrial radio access (E-UTRA); physical layer procedures," 3GPP, 2010.

[16] ETSI TS 136 331, "LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Protocol specification," 3GPP, 2013.