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Research on Selection Scheme for Integrated
Satellite-terrestrial Networks
A Thesis Submitted to the Department of Computer Science and Communications Engineering
the Graduate School of Fundamental Science and Engineering of Waseda University
in Partial Fulfillment of the Requirements for the Degree of Master of Engineering
Submission Date: January 25, 2021
Liu Xuanning
(5119FG12-1)
Advisor: Prof. Shigeru Shimamoto
Research guidance: Research on Wireless Access
2
Acknowledgments
First of all, I would like to sincerely gratitude to my advisor, Shigeru
SHIMAMOTO, for his patient guidance and tolerance during my two years of
master's studies.
Secondly, I would like to express my heartful gratitude to Ms. Liu Jiang,
Ms. Jenny Pan, and Ms. Megumi Saito for their help during my master's studies.
And I appreciate a lot of the help from Kenta Matsufuji, for his excellent
guidance on my topic. And I would like to thank Yang Chen and Dr. Huan Wang
for their advice in my life.
Finally, I would like to thank my family for their support and
encouragement for my study experience in Japan.
Thank you all in the Shimamoto lab for spending two happy and meaningful
years together, and I will always be grateful for the experience of learning and
progressing with you all at Waseda.
3
Abstract
An integrated satellite-terrestrial network is the integration of satellite network and
the terrestrial network, providing anytime, global seamless, ubiquitous services of
accessing to Internet. The integration and complementation can achieve high speed,
wide access, and multi-application commercial effect. That is a major technological
opportunity and challenge for the development of future networks. This paper considers
both QoS requirements of applications and network conditions. And discuss selection
algorithm using classic multiple attributes decision methods with special attention on
the characteristics of the integrated satellite-terrestrial network. Finally, simulation
results are obtained according to several application scenarios.
4
Table of contents
Acknowledgments.................................................................................................. 2
Abstract .................................................................................................................. 3
Chapter 1 ................................................................................................................ 7
Introduction ............................................................................................................ 7
1.1 Background ............................................................................................. 7
1.2 Motivation ............................................................................................. 10
Chapter 2 .............................................................................................................. 11
Overview of integrated satellite-terrestrial network ............................................ 11
2.1 LEO communication ............................................................................. 11
2.1.1 System composition .................................................................... 12
2.2 5G .......................................................................................................... 14
2.3 Integrate ................................................................................................. 16
Chapter 3 .............................................................................................................. 18
Radio Propagation for LEO ................................................................................. 18
3.1 Study on coverage of LEO .................................................................... 18
3.2 LOS and shadowing between LEO and mobile terminal in urban areas
............................................................................................................................. 20
3.2.1 LOS/NLOS model ...................................................................... 20
3.2.2 Probability of LOS and shadow situation ................................... 21
3.2.3 Influencing factors in the experimental area ............................... 22
5
3.3 LOS/NLOS judgment experiment ......................................................... 23
3.3.1 Experimental subjects ................................................................. 23
3.3.2 Model .......................................................................................... 24
Chapter 4 .............................................................................................................. 27
Network Selection ................................................................................................ 27
4.1 Overview ............................................................................................... 27
4.1.1 Horizontal handover and vertical handover ................................ 27
4.1.2 Handover process ........................................................................ 28
4.1.3 Network selection. ...................................................................... 30
4.2 QoS study of application scenarios ....................................................... 31
4.2.1 Analysis of QoS parameter weights based on service category .. 32
4.3 TODWA Network selection algorithm .................................................. 39
4.3.1 Network Selection Process ......................................................... 39
4.3.2 Candidate network evaluation based on TOPSIS ....................... 40
4.4 Simulation ............................................................................................. 42
4.4.1 Simulate scenario: .................................................................... 42
4.4.2 Simulation result ......................................................................... 43
Chapter 5 .............................................................................................................. 52
Conclusion and Future Work ............................................................................... 52
5.1 Conclusion ............................................................................................. 52
5.2 Future work ........................................................................................... 52
6
Reference ............................................................................................................. 54
Research Achievements ....................................................................................... 58
7
Chapter 1
Introduction
1.1 Background
As mobile data traffic explodes with the popularity of mobile smart
terminals and the diversification of their applications, MSS (Mobile Satellite
System), an important component of next-generation networks, are also facing
the challenge of providing high-bandwidth connectivity to users. Our
requirements for wireless networks require continuity in crisis on the one hand,
and comprehensive coverage on the other. Currently, it is mainly cellular
networks that are providing wireless coverage, but they are far from meeting the
requirements[1].
Satellite communications rely on line-of-sight (LOS) connections, and
signal strength will be significantly degraded in an obscured environment, so the
coverage characteristics of terrestrial networks are used to complement satellite
coverage, and the two are fused to build an integrated satellite-ground network.
It can provide the most effective coverage for sparsely populated areas as
well as high-capacity and economical services for densely populated areas, and
it is not affected by natural disasters and other unexpected events, which is an
effective way to achieve "any time" and "any place" communication goals. It is
an effective means to achieve the goal of "any time" and "any place"
communication and is a hot issue for academic research. Terrestrial networks can
maintain high-speed services at a low cost, while satellite networks can help
8
achieve 100% coverage. The two can work together to meet the needs of the
network.
Fig 1.1 Integrated satellite-terrestrial network
From 1G to 4G terrestrial mobile communication base station construction
density is proportional to the density of population distribution and economic
development in geography, base station construction priority in densely
populated and economically developed cities and industrial parks, and then
expand to relatively sparsely populated suburbs and rural areas.
At present, terrestrial mobile communication covers only about 20% of the
land area, which is less than 6% of the earth's surface area. As the goal of 5G is
to serve thousands of industries and interconnections, it is reasonable to assume
that the future distribution of 5G base stations will depend on population
distribution and industry application demand. In the industrial applications,
especially in the air, sea, forests, desert areas, and other sparsely populated areas,
it is difficult to achieve 5G for airplanes and drones, offshore oil wells and ships,
forest fire prevention and wildlife video monitoring, inspection along power
lines and railroads, border control and other application scenarios. Even for such
applications on land if 5G coverage is used, the pre-business model faces great
9
challenges, with the scale of revenue not matching the cost of 5G station
construction and operation and maintenance[2]. This brings business
opportunities to low-orbit satellite communications, low-orbit satellite
communications global coverage, and its cost sensitivity and industry
applications of geographic location and communications access points regional
density are not directly related, especially for low-density user access scenarios
of broadband interconnection and communications more advantageous.
At present, the global LEO field is undergoing a profound industrial, system,
and technological change, which has triggered a disruptive revolution in the core
technologies of satellite manufacturing and launching, and the expansion of
satellite application fields and modes of innovation, as shown in Table 1.
While competing with terrestrial networks, LEO communication networks
are also constantly learning from the advanced technologies developed by
terrestrial networks and moving toward complementarity and integration with
terrestrial networks, and the development of sky-ground-sea integration is
gradually becoming a trend.
Tab1 Low Orbit Satellite Constellation
10
1.2 Motivation
In essence, the satellite-terrestrial integrated network is a heterogeneous
wireless network, which, in terms of topology, consists of space-based network
and terrestrial-based network: the space-based network is a mobile satellite or
constellation located in different orbits, and the ground-based network is a
cellular network deployed based on terrestrial wireless standards, both of which
are unified and managed by the mobile satellite network to form an integrated
network[3]. The characteristics of the integrated network are that the mobile
satellite network can improve the coverage of the blocked area of traditional
satellite signals by deploying terrestrial cellular network based on MSS band on
the ground, which can lead to an increase of the number of system users and the
better utilization of network resources.
In this paper, a network selection judgment algorithm based on users’
application QoS demand is proposed and simulated and analyzed based on the
communication characteristics of integrated satellite-terrestrial network and
selection technology mechanism.
Keywords: LEO satellite communications, LOS, 5G, Network selection,
QoS
11
Chapter 2
Overview of integrated satellite-terrestrial
network
2.1 LEO communication
Low earth orbit (LEO) satellite communication is an essential part of
building 6G (The sixth-generation mobile communication system). Compared
with ground communication, LEO has wider coverage and is more suitable for
global communication in desert Gobi, deep forest, sea and other uninhabited
areas. The LEO is more suitable for global communication in desert, Gobi, deep
forest, sea and other unmanned areas. Compared with high-orbit satellite
communication, LEO has the advantages of low transmission loss, low time
delay and low cost[4]. Therefore, the research and design of LEO satellite mobile
communication is of far-reaching significance for the construction of integrated
air-space-sea communication.
12
Fig 2.1.&Table 2 Satellite orbits and major orbital characteristics.
Characteristics of LEO Systems:
1. “Anytime, Anywhere”
2. Blends cellular and satellite technologies Satellite Terrestrial Gateways
3. 500 – 2000 km orbit Below Van Allen Belts
4. Fiber-like propagation delay
5. Voice and data capabilities
6. Hand-held multi-mode phones
However, LEO communication also has some disadvantages, such as higher
cost of LEO satellite launch and onboard payload, high link transmission
attenuation, and high transmission delay, etc. LEO has gradually broken through
some unified satellite communication technologies, such as Space X's multi-
arrow, rocket recovery and reuse technology, etc.
2.1.1 System composition
LEO communication is to achieve communication between two or more
points by relaying radio waves used for communication between mobile users or
between mobile users and fixed users through LEO satellites[5]. The LEO
system architecture is shown in figure 2.2.
13
Fig 2.2. Satellite communication system composition and structure
The space segment, i.e., the constellation, consists of multiple low-orbit
satellites, which are interconnected by inter-satellite links and have the capability
of global networking and data exchange routing.
The ground segment is the control center, data exchange center and
operation center of the system, and consists of signal gateway stations,
measurement and control stations, mobile communication networks, operation
and control systems, integrated network management systems and business
support systems. The signal gateway stations and measurement and control
stations are located according to the coverage requirements and deployed
globally in a distributed manner, while the rest of the network equipment is
14
deployed in a centralized manner to meet the reliability requirements of telecom
operations.
The user segment consists of various types of user terminals, and the user
segment consists of various user terminals, including handheld terminals,
vehicle-mounted stations, shipboard stations, airborne terminals, etc.
2.2 5G
Key technologies for 5G include large-scale multiple-input multiple-output,
dense heterogeneous networks, millimeter-wave communications, D2D
communications, energy-aware communications energy harvesting, cloud-based
radio access networks (Cloud-Radio Access Network, C-RAN)[6], and
virtualization of network resources, among others. Compared to 4G cellular
networks, 5G:
1. increases throughput by at least 1000 times;
2. supports higher network density;
3. significantly reduces wait times;
4. improves energy efficiency;
5. supports high-density mobile broadband users, D2D communications,
ultra-reliable and large-scale machine-type communications.
The application of 5G is manifested in three application scenarios.
Enhanced Mobile Broadband (eMBB) scenarios, massive Machine Type of
Communication (mMTC) scenarios, and Ultra-reliable and Low Latency
Communications (URLLC) scenarios[7].
15
Fig2.3 5G three typical application scenarios and features
However, in order to meet the three typical application scenarios, 5G's
(Radio Resource Management, RRM) also faces many challenges, such as user
access, spectrum allocation, and power management. The main causes of these
problems include
1. heterogeneity of HWNs and dense distribution of UDs;
2. heterogeneous radio resources;
3. unbalanced coverage and traffic load of base stations;
4. high switching frequency;
5. capacity limitation of forward and backhaul links;
6. a large number of users and stakeholders with different objectives such
as operators.
Among them, our research focuses on the access selection problem in 5G,
which focuses on designing a network selection scheme based on the
characteristics of different types of service requests, the scenarios where users
5G
URLLC mMTC
eMBB
High accuracy positioning
Ultra-high reliability
High availability
Ultra-low latency
Ultra-low energy
Ultra-low density
Deep coverage
Ultra-low interruption time
Ultra-high spectral efficiency
Extreme data rates
16
are located (e.g., in-vehicle users)[8], and the state of the network to select the
appropriate network for users requesting specific types of services to meet their
needs, improve the utilization of network resources in 5G, and enable network
operators and users to reach This will improve the utilization of 5G network
resources and balance the interests of network operators and users.
2.3 Integrate
According to the definition of the International Telecommunication Union
(ITU), the satellite-terrestrial communication system can be divided into two
categories: integrated satellite-terrestrial system and hybrid satellite-terrestrial
system.
To address the issue of satellite and terrestrial 5G convergence, the ITU has
proposed four application scenarios for satellite-terrestrial 5G convergence,
including relay-to-station, cell backhaul, dynamic transit and hybrid multicast
scenarios, and the key factors that must be considered to support these scenarios,
including multicast support[9], Intelligent routing support, dynamic cache
management and adaptive flow support, latency, consistent quality of service,
NFV (Network Function Virtualization)/SDN (Software Defined Network)
compatibility, and flexibility of business models.
3GPP has been working on star-ground convergence since R14. In
TS22.261, the role and advantages of satellites in 5G systems are discussed. As
one of the multiple access technologies for 5G, satellites have significant
advantages in some industrial application scenarios that require wide-area
coverage. Satellite networks can provide low-cost coverage solutions in areas
with weak terrestrial 5G coverage. For M2M/IoT in 5G networks, and for
17
providing ubiquitous network services to passengers on high-speed mobile
carriers, broadcast/multicast information services can be provided to network
edge network elements[10] and user terminals with the superior
broadcast/multicast capability of satellites.
Fig2.4. Network architecture of integrated satellite-terrestrial network1
Determine the service classification of each communication scenario of the
convergence between heaven and earth, such as the service types of pipeline,
mobile and IoT/data collection; and determine the performance requirements
under each service scenario, including satellite coverage, channel bandwidth,
communication delay, reliability and maximum number of connections.
1 https://www.researchgate.net/figure/Integrated-satellite-terrestrial-architecture-of-prevalent-standards-to-
connect-with_fig1_319901529
18
Chapter 3
Radio Propagation for LEO
The analysis of the bit error rate (BER) performance of direct sequence code
division multiple access (DS-CDMA) communication systems based on low
earth orbit (LEO) satellites under various propagation channel models and
communication scenarios has been well studied by many researchers in almost
all cases. However, most of these works consider a hypothetical snapshot of the
communication scenario[11], such as the worst-case scenario, to evaluate the
BER performance under that scenario, without considering the probability of
occurrence of that scenario.
3.1 Study on coverage of LEO
From the perspective of the ground station, the position of the satellite
within its orbit is defined by the azimuth (Az) and elevation (°) angles. The
azimuth angle is the angle of the satellite's orientation, measured clockwise from
geographic north in the plane of the horizon. The azimuth angle ranges from 0º
to 360º. The elevation angle is the angle between the satellite and the observer's
(ground station) horizon plane. The range of the elevation angle is 0º to 90º. The
coverage area of a single satellite is a circular area of the Earth's surface within
which the satellite can be seen at an elevation angle equal to or greater than the
minimum elevation angle determined by the system link budget requirements.
The coverage area of a satellite on the Earth depends on the orbital parameters
and is maximum at an elevation angle of 0.
19
The ground station (GS) can communicate with LEO satellites only when
the ground station is below the coverage area (satellite footprint). Because LEO
satellites move too fast over the Earth, the duration of visibility and the duration
of communication will vary for each LEO satellite as it passes the ground
station[12]. As the satellite moves, the footprint also moves, taking the ground
station out of the footprint and thus losing communication with the ground
station.
Fig3.1. LEO coverage geometry
𝜀0 + 𝛼0 + 𝛽0 = 90°
sin 𝛼0 =𝑅𝑒
𝑅𝑒 + 𝐻cos 𝜀0
𝑆𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒 = 2𝜋𝑅𝑒2(1 − cos 𝛽0)
As in Fig 3.1, H is the elevation of the satellite, 𝑅𝑒 is the radius of the Earth,
20
and 𝜀0 is the elevation angle from the observation point to the satellite, from
which the relationship between the elevation angle of the LEO satellite
(assuming the altitude is 1200km) and the signal coverage area can be studied as
Fig 3.2.
Fig 3.2. The LEO Coverage of the Earth’s surface
3.2 LOS and shadowing between LEO and
mobile terminal in urban areas
3.2.1 LOS/NLOS model
Satellite signals are always largely reflected and be blocked in cities[13].
Due to the influence of the building, the signal shows two manifestations of line-
of-sight (LOS) and non-LOS (NLOS) as shown in Fig 3.3.
21
Fig 3.3 LOS and NLOS of satellite signal
3.2.2 Probability of LOS and shadow situation
According to the model in Figure 3.3, it is possible to know the variation of
the probability of LOS and NLOS by the elevation angle for the satellite orbit
altitude at 1200 km and assuming the average of buildings’ height of 20m.
Fig 3.4 Probability of LOS influenced by the change in elevation angle
NLOS
NLOS
LOS
22
Fig 3.5 The probability of NLOS influenced by the change in elevation angle
3.2.3 Influencing factors in the experimental area
Here I examine the relationship between average building height and
building density in several major areas of Tokyo Shinjuku is my main study site2.
2 Land Use in Tokyo https://www.toshiseibi.metro.tokyo.lg.jp/seisaku/tochi_c/tochi_5list.html
23
Fig 3.6 the relationship between average building height and building density
The study result shows the average height of buildings and building density.
The circled marks represent the average height of buildings in meter. The
waterfront has the highest average height of building followed by Shinjuku,
Shibuya, Ueno/Asakusa.
3.3 LOS/NLOS judgment experiment
3.3.1 Experimental subjects
Satellite ephemeris can accurately calculate, predict, depict and track the
time, position, speed and other operational status of satellites and flying bodies.
According to these data, do a simulation of 110 OneWeb satellites’ operation[14].
And to study the positional relationship and visibility between the observation
point and the LEO satellites.
24
3.3.2 Model
Fig 3.7 Experiment model
As shown in figure 3.7. If the coordinates of the building and the UE are
known, and the height of the building is known, an angle θ can be derived, and
this angle can be compared with the maximum elevation angle at the observation
point learned from the satellite tle to know whether the observation point is LOS
at this time.
Due to the shape of the Earth, the latitude and longitude elevation
coordinate vectors are converted to actual coordinate vectors[15] as shown in
Figure 3.8.
Fig 3.8 Representation of latitude and longitude elevation coordinates
25
The conversion between latitude and longitude coordinates and xyz
coordinates is performed by the following formula.
x = (R + h)cosφcosλ
y = (R + h)cosφsinλ
𝑧 = [(1 − 𝑒2)𝑅 + ℎ] sinφ
R = 63710004m
𝑒 = 0.0167 (Eccentricity of earth)
From a point outside the laboratory building (35°42', 139°42', 27m), the
number of OneWeb satellites that can be accessed in real time, as well as
information about them.
The minimum elevation angle of the observation point UE and the building
can be calculated by the previous equation as 44.47deg. Comparing this angle
with the results calculated by 110 satellites respectively, it can be determined
whether it is LOS.
Fig 3.9 Satellite visible judgment test result
Since the changes are in real time, this data collection was done on January
6, 2021 at 17:17 PM. The figure3.9 shows a part of the test about 1 satellite, and
26
the data of another 109 satellites are not shown here.
By studying the relationship between elevation angle and distance, derive
the number of the visible satellites.
Fig.3.9 Visible number of satellites
27
Chapter 4
Network Selection
4.1 Overview
In order to meet the diversification of communication service requirements
and provide users with the best quality of communication services, the
interoperability and convergence of wireless networks with different
architectures and characteristics will be an inevitable trend. In the environment
of heterogeneous network convergence, the complexity and heterogeneity of
network conditions make it necessary for users to frequently change network
affiliation and management domains, so how to seamlessly provide users with
high-quality data transmission services, regardless of the user's location and
conditions, the best access method according to the current network state[16],
adaptive to provide the best experience for users and according to the user's
location and service situation in the Smooth handover between different access
methods is a key issue that must be addressed.
4.1.1 Horizontal handover and vertical handover
Mobility management technology is the key technology to solve these
problems. Handover management is one of the important components of
mobility management. Network handover can be divided into different
categories from different perspectives, including horizontal handover and
vertical handover according to the type of network[17]. When handover occurs
in the same network technology between different access points is called
28
horizontal handover. When the handover occurs between different network
technology access points is called vertical handover, as shown in Figure 1.
Because heterogeneous networks mainly study the communication between
different types of network access methods, so the handover management
problem of heterogeneous networks is mainly the problem of vertical handover.
Fig4.1. Horizontal handover and vertical handover
4.1.2 Handover process
There are mainly two kinds of handover brought by satellite or terminal
movement, one is the handover within the satellite system, for the low-orbit
satellite system, its position relative to the ground changes rapidly, the terminal
is continuously covered by the same satellite for only ten minutes, for the low-
orbit satellite using multiple beams, the same beam continuously covers the
29
terminal for only a few minutes, so the inter-satellite or inter-beam handover
must be performed quickly and prevent data loss during the handover process.
The second is the handover of terminals between the terrestrial 5G network and
the satellite network, and the inter-network handover process needs to consider
various factors.
- Simultaneous support for on-satellite processing and bend-transparent
forwarding architecture
- Handover preparation and handover failure handling
- Time synchronization
- Coordination of measurement objects
- Support for lossless handover
It should be noted that the handover direction is different and the triggering
conditions are not the same. For example, when the signal of the terrestrial
cellular network is sufficient, the terminal handover from the satellite network to
the terrestrial network; however, the terminal leaves the cellular network only
when the signal of the cellular network is very weak.
The process of handover can be divided into three phases: handover
initialization, handover decision and handover execution. The handover
initialization phase is mainly for collecting some parameters, such as received
signal strength, network load, etc. The handover decision phase is to use the
collected information to select the target cell. The handover execution phase
sends a series of signaling to complete the handover according to the selected
target cell.
30
4.1.3 Network selection.
In the heterogeneous wireless network environment, how to choose the
most effective and suitable access network for mobile terminals to meet the
quality of service (Quality of Service, QoS) of the service has been a hot research
topic in recent years. And the terminal in the heterogeneous network
environment mobile necessarily involves the problem of handover between
networks. The existing vertical handover algorithms mainly include the
handover algorithm based on Received Signal Strength (RSS) and a series of
improved algorithms.
Since the QoS of a network includes parameters such as bit rate, packet loss
rate, delay and jitter, the selection problem of a heterogeneous network with the
goal of meeting the QoS requirements of the service is a multi-parameter
decision problem. The literature proposes a network selection framework
structure with user QoS as the core, but no specific selection algorithm is given.
The literature applies both fuzzy logic and multiparametric decision making to
the network selection process. The literature uses a combination of hierarchical
analysis and entropy value method to determine the weight values of network
parameters when performing multi-parameter decision making. However, when
the above algorithms are applied to network selection with the goal of satisfying
QoS, the weight values of each QoS parameter are often assigned equally. Since
the differences in QoS requirements between different types of services are not
considered, the network with the best QoS access is always selected regardless
of the type of service, and thus the load of each candidate network cannot be
reasonably and effectively balanced.
31
4.2 QoS study of application scenarios
The traditional communication services specified by the 3rd Generation
Partnership Project (3GPP) are classified into conversational services (CS),
background services, (BS) streaming services (SS) and interactive services (IS)
according to the requirements of multimedia on network latency3.
Conversational services are more sensitive to latency, tolerate certain packet
loss and BER, and generally require little bandwidth. The maximum tolerable
delay and packet loss rate for a video session are 150ms and 0.001%, respectively.
The streaming service does not require high latency, tolerates certain packet loss
and BER, and has higher bandwidth requirements. Live online video streaming
requires at most 300ms of network latency and 0.000001% of packet loss.
Interactive services require less delay, unlike the above two types of services,
interactive services have higher requirements for packet loss and BER, and
require less bandwidth resources. Contextual services are not sensitive to
network attributes, but they must ensure the security and reliability of data
transmission, so they have higher requirements for packet loss and bit error rate
and require very few bandwidth resources.
To address the above problems, this paper analyzes the relative importance
of QoS parameters for the four types of services defined by 3GPP, sets the
weights of QoS parameters for each type of services using Analytic Hierarchy
Process (AHP), and then uses Technique for Order Preference by Similarity to
3 https://spectrum.ieee.org/telecom/wireless/3gpp-release-15-overview
32
Ideal Solution (TOS). Preference by Similarity to Ideal Solution (TOPSIS) is
used as the basis to design a business class-oriented algorithm for differentiating
weights for heterogeneous network selection, and finally, the algorithm is
verified by simulation experiments.
4.2.1 Analysis of QoS parameter weights based on
service category
The types of business considered, which mainly include session-based
business, interaction-based business, stream-based business and backend-based
business, are shown in the table.
Session-based services are usually real-time services, and latency is critical
to the user's service experience. For background services, the latency has almost
no impact. The large data volume transmission of streaming media services is
very dependent on high network bandwidth. The session class services tend to
be less sensitive to bandwidth. Based on the above analysis, this paper selects 5
parameters that best reflect the performance of wireless networks, namely,
Received Signal Strength (RSS), Bandwidth (B), Delay (D), Jitter (J), and Packet
Loss Rate (PLR), and uses fuzzy hierarchical analysis to find the weights of each
network parameter based on the corresponding network services, and analyzes
their impact on the QoS performance of different mobile network services one
by one.
The algorithm AHP is a method to derive the relative weight relationship
between all parameters by a two-by-two comparison between parameters[19].
The relevant parameters of the candidate object are analyzed and the
33
parameter hierarchy is represented by the parameter hierarchy diagram shown in
Fig. A nine-level scale is usually used in AHP to express the relative importance
of each parameter based on subjective perceptions, feelings and knowledge.
Fig.4.2 Parameter hierarchy diagram
In the fuzzy logic criterion layer, the relative importance of each factor to
the target layer is composed in the form of a matrix. The quantitative scaling
value is obtained by a two-by-two comparison between factors. In this paper, the
fuzzy complementary judgment matrix 𝑅 = (𝑟𝑖𝑗)𝑛×𝑛 is obtained by using the
0~0.9 scaling method.
To ensure that the weights are obtained more reasonably and accurately, the
fuzzy consistency matrix is introduced to improve the fuzzy mutual judgment
matrix. The specific weight acquisition process is as follows.
1. Establishing a hierarchy of top-down dominance relationships
As shown in the figure, the target problem is first hierarchized. In general,
it can be divided into an objective layer, a criterion layer, and a solution layer. In
the network selection algorithm, the objective layer is the optimal network to be
selected. The criterion layer is the various judgment factors that affect the
network selection. In this paper, five factors, namely, received signal strength,
34
jitter, bandwidth, transmission delay, and packet loss rate, are chosen to measure
the performance of different networks. The scheme layer is the candidate
networks in the ground-satellite converged network environment, i.e., terrestrial
5G and low-orbit satellite communication networks.
Fig.4.3 Hierarchical structure model
2. Construction of fuzzy complementary judgment matrix
Fuzzy judgment matrix calculation method, the relative importance of each
factor in the criterion layer to the target layer is composed in the form of a matrix.
The quantitative scale values are obtained by two-by-two comparison between
factors and analysis. Thus, the fuzzy complementary judgment matrix 𝑅 =
(𝑟𝑖𝑗)𝑛×𝑛 is obtained. The method of taking the value of 𝑟𝑖𝑗 is 0~0.9 scaling
method, as shown in the table.
Scale Definition Description
0.5 Equal importance Two elements contribute
equally to the objective
0.6 Moderate importance Experience and judgment
moderately favor one element
35
over another
0.7 Strongly importance Experience and judgment
strongly favor one element
over another
0.8 Very strong importance One element is favored
very strongly over another, its
dominance is demonstrated in
practice
0.9 Extreme importance The evidence favoring
one element over another is of
the highest possible order of
affirmation
0.1 0.2 0.3 0.4 inverse comparison Inverse comparison of
the above comparison
Table.2 0~0.9 Scale Method
3. Weight vector calculation
The weight vector 𝑊 = (𝑊1, 𝑊2, … , 𝑊𝑛)−1 is obtained by the weight
value calculation formula, where ∑ 𝑊𝑖𝑛𝑖=1 = 1,𝑊𝑖 ≥ 0, i=1, 2, ..., n.
4. Consistency test of the fuzzy matrix 𝑹 = (𝒓𝒊𝒋)𝒏×𝒏
36
Firstly, construct the characteristic matrix 𝑊∗ = (𝑊𝑖𝑗)𝑛×𝑛 of matrix R,
where,
Matrix compatibility calculations are then performed,
If the compatibility value 𝐼(𝑅, 𝑊∗) ≤ 𝑇 , then the consistency is found to
meet the criteria, otherwise the matrix 𝑅 = (𝑟𝑖𝑗)𝑛×𝑛 needs to be adjusted and
reweighted. The critical value of compatibility index T=0.1 is usually set.
Whether the fuzzy judgment matrix is consistent or not is a crucial issue in
constructing the matrix, often in the obvious case because of the intricacies of
the problem to be solved and the limitations of human thinking leading to the
lack of consistency. The method based on the fuzzy complementary matrix to
find the weights better overcomes some of the shortcomings of the AHP
algorithm, although there are still shortcomings for the guarantee of consistency.
Therefore, the fuzzy consistency matrix is introduced to improve it so that the
weights are more reasonable and accurate.
5. Establishing fuzzy consistent judgment matrix
Firstly, the fuzzy complementary judgment matrix 𝑅 = (𝑟𝑖𝑗)𝑛×𝑛 is
summed up by rows and by columns, which are denoted as,
37
The following transformations are then implemented.
Derive the fuzzy consistent judgment matrix F.
𝐹 = (𝑓𝑖𝑗)𝑛×𝑛
6. Final weight calculation (hierarchical single ranking)
This layer is used to calculate the priority weights of the importance of the
factors in this layer for the previous layer. This step is used to calculate the weight
vector 𝑊𝑠 = (𝑤1, 𝑤2, … , 𝑤𝑛) of each judgment factor for the business S.
Which,
According to the above process of fuzzy hierarchical analysis (FAHP)
algorithm, in order to obtain the relative weights of each network parameter
index for different businesses.
A fuzzy complementary judgment matrix about four types of services is
38
established. The fuzzy complementary judgment matrices are obtained for
session class, background class, streaming class and interactive class services.
The final weight vector of each network parameter indicator with
concerning different services is obtained through a series of operations processed
39
by the BJTE algorithm.
In the judgment process, to ensure fair and effective network utility
evaluation, the judgment factors are normalized to eliminate the differences
between them.
4.3 TODWA Network selection algorithm
4.3.1 Network Selection Process
1. Network discovery and static information collection
Assume that the entire network is configured with Media Independent
Handover Function (MIHF) of IEEE802.21, and adopts the handover mode of
terminal decision. When mobile terminals communicate in the current Radio
Access Network (RAN), they can find adjacent RAN through MIHF and collect
the static information of adjacent RAN, which can be stored in the local database.
2. Determine the candidate network and QoS parameter value
collection
When the mobile terminal detects that the Radio Signal Strength (RSS) of
the current RAN drops to the handover threshold, the RSS of the adjacent RAN
40
is detected successively according to the static information of the adjacent RAN
in the local database, the adjacent RAN above the communication threshold is
included into the candidate network, and then the current QoS parameter value
of the candidate network is collected through MIHF.
3. Select target network
Candidate QoS parameter values into judgment matrix structure, the main
business categories, based on the current mobile terminal using chapter 2 QoS
parameters of the corresponding weight vector, using section 3.2 evaluation
algorithm of computing candidate network the ideal approximation value,
choose the ideal approximation candidate networks as the target of maximum
value, a handover request.
4.3.2 Candidate network evaluation based on TOPSIS
TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)
algorithm is proposed to solve the multi-objective problem of a single decision
maker[20], which is close to the linear weighting method. Its basic idea is: the
selected satisfactory scheme should be as close to the positive ideal scheme as
possible, and as far away from the negative ideal scheme as possible.
Assuming that the number of candidate networks determined by mobile
terminals is N, the decision matrix is constructed by using the obtained candidate
network QoS parameter values.
41
Each element is a related network QoS parameters of the original values,
all the QoS parameters of the 𝑖th a network 𝛮𝑖
can show as matrix row vector
{𝑑𝑖′, 𝑗𝑖
′, 𝑝𝑟𝑖′, 𝑟𝑠𝑠𝑖
′, 𝑏𝑖′} .
Then, the elements of the judgment matrix are normalized using the following
equation, and to obtain the matrix R.
𝑟𝑖𝑗 =𝑥𝑖𝑗
√∑ 𝑥𝑖𝑗2𝑚
𝑖=1
; 𝑗 = 𝐷, 𝐽, 𝑃𝑅, 𝑅𝑆𝑆, 𝐵
𝑋’ =
𝛮1
⋮
𝛮𝑖
⋮
𝛮𝑚
𝑑1’
⋮
𝑑𝑖’
⋮
𝑑𝑚’
𝑗1’
⋮
𝑗𝑖’
⋮
𝑗𝑚’
𝑝𝑟1’
⋮
𝑝𝑟𝑖’
⋮
𝑝𝑟𝑚’
𝑟𝑠𝑠1’
⋮
𝑟𝑠𝑠𝑖’
⋮
𝑟𝑠𝑠𝑚’
𝑏1’
⋮
𝑏𝑖’
⋮
𝑏𝑚’
𝐷 𝐽 𝑃𝑅 𝑅𝑆𝑆 𝐵
𝑋 =
𝛮1
⋮
𝛮𝑖
⋮
𝛮𝑚
𝑑1
⋮
𝑑𝑖
⋮
𝑑𝑚
𝑗1
⋮
𝑗𝑖
⋮
𝑗𝑚
𝑝𝑟1
⋮
𝑝𝑟𝑖
⋮
𝑝𝑟𝑚
𝑟𝑠𝑠1
⋮
𝑟𝑠𝑠𝑖
⋮
𝑟𝑠𝑠𝑚
𝑏1
⋮
𝑏𝑖
⋮
𝑏𝑚
𝐷 𝐽 𝑃𝑅 𝑅𝑆𝑆 𝐵
42
According to the idea of TOPSIS, the result of judging duan should be as close as
possible to the positive ideal solution and the principle negative ideal solution. The
distance between each candidate network and the positive and negative solutions is
calculated and compared to get the judgment result.
4.4 Simulation
4.4.1 Simulate scenario:
Performing network selection scheme based on AHP and TOPSIS which
considering differentiated weight of criterion according to specific access
network.
Fig.4.4 Simulation Scenario
The simulation scenario is shown in Figure4.4 It is assumed that the 5G
network has full coverage and the satellite footprint is dynamic. The observation
points start moving from static in an urban environment.
UE’s position:
Latitude: 35°42’25.60” Longitude: 139°42’32.19”; Elevation: 27m
43
Building’s position:
Latitude:35°42’25.61”; Longitude:139°42’31.28”; Elevation:61m
The initial environment is a single building impact. And then UE get in
movement at a uniform speed of 1.5m/s along a direction 9 degrees west of south.
4.4.2 Simulation result
In this simulation, the services analyzed are backend services. The
importance weights of a set of AHP criteria and the weights of the network that
provides this service. The weight does not indicate that the network is more
dominant in terms of QoS, but only that it is sufficient to provide a weight of
network capacity sufficient for that service.
Background class
When the end-user sends and receives data-files in the background, this
scheme applies. Background traffic is one of the classical data communication
schemes that on an overall level is characterized by that the destination is not
expecting the data within a certain time. The scheme is thus more or less delivery
time insensitive. E.g.: background downloading of mails, sending of SMSs over
the internet.
44
Fig 4.5 Weights associated with the criteria for background
Comparison of possibilities when performing network selection algorithms
at fixed points.
Fig 4.6 Network selection probability of background (Fixed)
45
Comparison of network selection possibilities when UE is moving at a
uniform speed of 1.5m/s along direction 9 degrees west of south.
Fig 4.7 Network selection probability of background (Mobile)
The same approach was used to study conversational, streaming and
interactive services.
Conversational class
It is the service class with the highest QoS requirements including least
transfer delay and time relation between information elements. This is the only
scheme where the required characteristics are strictly given by human perception.
E.g.: telephony speech, VoIP, video conferencing and other real time
activities.
46
Fig 4.8 Weights associated with the criteria for conversational
Comparison of possibilities when performing network selection algorithms
at fixed points.
Fig 4.9 Network selection probability of conversational (Fixed)
47
Comparison of network selection possibilities when UE is moving at a
uniform speed of 1.5m/s along direction 9 degrees west of south.
Fig 4.10 Network selection probability of conversational (Mobile)
Streaming Class
This scheme is one of the newcomers in data communication, raising a
number of new requirements in both telecommunication and data
communication systems. It is characterized by that the time relations (variation)
between information entities (i.e. samples, packets) within a flow shall be
preserved, although it does not have any requirements on low transfer delay.
Acceptable delay variation is thus much greater than the delay variation given
by the limits of human perception. E.g.: streaming of audio and video.
48
Fig 4.11 Weights associated with the criteria for streaming
Comparison of possibilities when performing network selection algorithms
at fixed points.
Fig 4.12 Network selection probability of streaming (Fixed)
49
Comparison of network selection possibilities when UE is moving at a
uniform speed of 1.5m/s along direction 9 degrees west of south.
Fig 4.13 Network selection probability of streaming (Mobile)
Interactive class:
When the end-user, is on line requesting data from remote equipment (e.g.
a server), this scheme applies. Interactive traffic is the other classical data
communication scheme that on an overall level is characterized by the request
response pattern of the end-user. At the message destination there is an entity
expecting the message (response) within a certain time. Round trip delay time is
therefore one of the key attributes. Another characteristic is that the content of
the packets shall be transparently transferred.
50
Fig 4.14 Weights associated with the criteria for interactive
Comparison of possibilities when performing network selection algorithms
at fixed points.
Fig 4.15 Network selection probability of interactive (Fixed)
51
Comparison of network selection possibilities when UE is moving at a
uniform speed of 1.5m/s along direction 9 degrees west of south.
Fig 4.16 Network selection probability of interactive (Mobile)
52
Chapter 5
Conclusion and Future Work
5.1 Conclusion
In this paper, the research on wireless network selection mechanism is mainly
focused on the requirement of communication terminals needing to access multiple
wireless communication networks in integrated satellite-terrestrial network. Using
the traditional multi-attribute decision theory method, a network selection
judgment algorithm based on user experience and mobility is proposed, and on this
basis, the network selection mechanism under the star-ground integrated network
architecture is modeled, and finally the network selection status of the proposed method
under different application scenarios is studied through simulation.
5.2 Future work
This research focuses on the network selection algorithm for integrated
satellite-terrestrial network, which achieves seamless service QoS-based study
in these two wireless networks, and proposes a network selection algorithm
based on it. further work lies in.
1. this research is only for LEO and terrestrial 5G communication networks,
there are many more types of wireless networks that have been included or will
appear in today's heterogeneous wireless network systems, so further derivation
is needed to study the expression of other wireless networks so that the service
QoS requirements are seamlessly delivered throughout the heterogeneous
53
wireless network system, making the design of the network selection algorithm
more convenient.
2. In this research, only the design of the network selection algorithm is
discussed, and other processes of the access control and vertical switching
process are not studied, such as the triggering conditions of switching, etc.
Meanwhile, the algorithm considers the mobility of users, which may differ from
the actual scenario of the system and needs further study.
3. From the existing network selection algorithms, we can see that the
network selection algorithms are diverse and pluralistic, and there is no unified
standard to measure the performance of the algorithms. Therefore, designing a
unified standard to measure the performance of each network selection algorithm
is the next research direction.
54
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58
Research Achievements
Kenta MATSUFUJI, Xuanning LIU, Kazutoshi YOSHII, Pan Zhenni,
Megumi SAITO, Jiang LIU, Shigeru SHIMAMOTO, Takuya OKURA, Syuu
MIURA, Hiroyuki TSUJI, Naoko YOSHIMURA, Morio TOYOSHIMA,
“Expansion of Satellite Communication to the ground using Engineering Test
Satellite-9”, RCS IEICE Technical Conference, Japan, March 9-12, 2021.