BAHIRDAR UNIVERSITY
BAHIRDAR INSTITUTE OF TECHNOLOGY
SCHOOL OF RESEARCH AND POSTGRADUATE STUDIES
FACULTY OF ELECTRICAL AND COMPUTER ENGINEERING
Performance Analysis of Coverage Prediction and Cell-Edge Throughput
Improvement Techniques in LTE: Case for Amtel-Somalia
MSc Thesis Proposal
By: Said Ali Adan
Program: Communication System Engineering
Main advisor: DR: Amare Kassaw (PhD)
March, 2022
Bahirdar, Ethiopia
Bahir Dar Institute of Technology-Bahir Dar University
School of Research and Graduate Studies
Faculty of Electrical and Computer Engineering
THESIS PROPOSAL
Student:
________________________________________________________________________
Name
Signature
Date
The following graduate faculty members certify that this student has successfully presented the
necessary written thesis proposal and oral presentation of this proposal for partial fulfillment of
the thesis-option requirements for the Degree of Master of Science in Communication System
Engineering.
Approved:
Advisor:
________________________________________________________________________
Name
Signature
date
Chair Holder:
________________________________________________________________________
Name
Signature
Date
Faculty Dean:
________________________________________________________________________
Name
Signature
Date
i
List of acronyms
BS
Base Station
CS
Coordinated Scheduling
COMP
Coordinated Multi-point
DL
Downlink
DPS-COMP
Dynamic Point Selection Comp
EPC
Evolved packet core
ENODEB
Evolved Node B
EUFS
Edge User-Friendly Scheduler
E-UTRAN
Evolved universal terrestrial radio access network
1G
First Generation
2G
Second Generation
3G
Third generation
4G
Fourth Generation
3GPP
3rd Generation Partnership Project
HSPA
High-Speed Packet Access
HETNETS
Heterogenous Networks
IMT-A
International Mobile Telecommunication-Advanced
ITU
International Telecommunication Union
IRAT
Interrelated Radio Access Technologies
IDW
Inverse Distance Weighted
JP-COMP Joint
Processing Coordinated Multipoint
ii
JT-COMP
Joint Transmission Coordinated Multipoint
KPI
Key Performance Indicators
LTE
Long-Term Evolution
LTE-A
Long-Term Evolution Advanced
MIMO
Multiple Input Multiple Output
MME
OK
Mobility Management Entity
Ordinary Kriging
PF
Proportional Fair
QOS
Quality of Service
RF
Radio Frequency
RAT
Radio Access Technology
RSRP
Received Signal Received Power
SAE
System Architecture Evolution
S-GW
Serving Gateway
SSPS
Semi-Static Point Selection
SINR
Signal to Interference Noise ratio
SISO
Single Input Single Output
UL
Uplink
UMTS
Universal Mobile Telecommunication System
iii
Table of contents
List of acronyms ............................................................................................................................................ ii
Table of contents ......................................................................................................................................... iv
Abstract ......................................................................................................................................................... v
1. Introduction .............................................................................................................................................. 1
1.1Background .......................................................................................................................................... 1
1.2 Coverage prediction and cell edge throughput.................................................................................... 3
1.3 MIMO ................................................................................................................................................. 5
1.4 Coordinated multi-point(comp) .......................................................................................................... 6
1.4.1 Downlink comp transmission....................................................................................................... 8
1.4.1.1 Coordinated scheduling/beamforming ...................................................................................... 8
1.4.1.2 Joint processing comp ............................................................................................................... 8
2. Literature review ....................................................................................................................................... 9
3. Problem of Statement............................................................................................................................. 12
4. Objectives................................................................................................................................................ 12
4.1 General objective .............................................................................................................................. 12
4.2. Specific Objectives .......................................................................................................................... 12
5. Methodology ........................................................................................................................................... 13
6. Scope of the Study ................................................................................................................................. 14
7. Significance of the Study ......................................................................................................................... 15
8. Work Plan ................................................................................................................................................ 16
9. Budget Plan ............................................................................................................................................. 17
References .................................................................................................................................................. 18
iv
Abstract
Due to the advancement of telecommunications platforms, users are now demanding new
applications such as gaming online, video streaming as well as mobile television. In long-term
evolution focus is on higher capacity, higher spectral efficiency, increased peak data rate, increased
number of simultaneously active subscribers, and improved performance at the cell edge. Due to
these requirements, predicting coverage accurately and improving cell edge throughput has
become extremely important. The received signal in a wireless cellular network degrades as the
propagation distance increases. Consequently, the cell edge user experiences poor quality of
service (QOS) specifically throughput, where the power efficiency and spectral efficiency are very
low due to the low received signal and high fading effects. However, the cell-centric user
experiences a good quality of service.
In Somalia, there are several telecom service providers, whose deployed cellular networks like
2G,3G, and LTE. Preliminary survey and discussion show that the networks facing poor coverage
and limited cell edge throughput in some areas. In this thesis, we will analyze the performance of
coverage prediction and cell edge throughput improvement techniques in LTE to come up with
effective and reliable coverage, as well as improved throughput. Particularly this work will be
based on a selected area from Garowe city. Which is the capital city of Puntland state of Somalia.
Similarly, the input data will be collected from AMTEL Telecom company which is one of the
private service providers of Somalia. The main goal of this work is to analyze the performance of
coverage prediction and cell edge throughput improvement techniques and then propose the most
promising technique that help to avoid coverage related problems and met the demand of
subscriber.
Keywords: Coverage prediction, cell edge throughput, Comp and long-term evolution (LTE).
v
1. Introduction
1.1 Background
Wireless mobile communication is a rapidly growing technology to satisfy the increased demand
of subscribers such as data and voice. Considering this, the network operators try to deploy new
radio access technology (RAT), but as far as new technology is deployed the complexity of
coverage prediction in the mobile cellular network will increase. Therefore, proper network design
and planning is an obligation process to do. This is because the sophisticated planning tools used
by the operators can not accurately predict the coverage of the desired area. Meanwhile, the cellular
coverage estimation needs to be considered various network parameters and environmental-based
conditions.
In its easiest form, the LTE coverage area can be forecasted by using path loss prediction models,
although this method is limited as it can not show the effect of traffic type distribution on the
coverage. Nevertheless, it gives an adequate good first-hand estimate about the whole coverage
which can be achieved with certain offered average capacity and quality. In the initial phase of the
planning, a generalized link budget calculation can be produced to estimate the rough, average
LTE cell range per area type (dense urban, urban, suburban, rural, and open area). By assuming a
certain overlapping percentage for the adjacent cells, this exercise provides an initial estimate of
the required number of the eNodeB sites over the planned area. However, in terms of coverage
and capacity performance, the network subscribers may encounter some problems. These problems
may include the cell edge problem which directly degrades the throughput of the cell edge
subscribers. This is because coverage and throughput are two inter-related terms, whenever the
required cell edge rate increased, the smaller cell coverage radius. Likewise, the smaller the cell
edge rate, the higher the cell coverage radius. In addition, the cell edge user is always suffering
from low signal-to-noise-plus interference -ratio (SINR) levels due to the farness from the base
station (BS). Multipath fading, propagation distance, and path loss factors also cause the
degradation and attenuation of the received signal in the cell boundaries. Increasing the power of
the transmitter could not be suitable always, while it also increases intercell interference.
Due to this, long-term evolution technology (LTE) is enabled by new technologies and features,
and enhancements to existing technologies, like carrier aggregation, MIMO (multiple input,
multiple output), beamforming, Coordinated Multi-Point (Comp), in-band Relaying (relay nodes),
1
and HetNets (heterogeneous networks) [1]. In addition, as the area becomes very interesting for
research to enhance both the throughput and coverage to identify new improvement techniques.
researchers proposed numerous techniques that can improve the performance of quality of service
in general and specifically throughput for cell-edge users in a cellular network. But still, some
challenges are not solved by the previous researchers and this is our motivation behind this
research. therefore, in this thesis, we will analyse the performance of coverage and cell-edge
throughput improvement techniques to enhance both coverage and throughput together.
The target of the radio network planning could be a compromise between coverage, capacity,
quality, and cost. Here are the steps of the planning and rollout of the LTE network.
Preparation phase: coverage and capacity requirements are identified. Like traffic profile, cell
edge throughput, and indoor and outdoor coverage probability.
Nominal planning phase: link budget, Capacity dimensioning, and radio frequency prediction are
conducted.
Detailed planning: it verifies nominal planning by identifying site coordinates, conducting site
surveys, and selecting a proper candidate to meet coverage targets.
Rollout phase: based on the detailed planning the network rollout and site construction are
conducted.
Pre-optimization: the network is pre-optimized by validating the cell parameters, coverage target,
and throughput.
Soft launch: this is the final phase when the network has passed all required KPIs and service
level agreements. The figure below shows end-to-end network deployment phases.
2
Figure1: End to end network deployment phases
1.2 Coverage prediction and cell edge throughput
Analysing and discussing coverage holes in mobile networks is still an important problem that
needs to be addressed, both deployed and future networks. However, each type of coverage hole
has to be handled depending on the effect they have on the users. In particular, they are
characterized by resulting abnormal disconnections or inter-Radio Access Technology (RAT)
handovers when there is an underlying RAT available to maintain the connection released by the
LTE network at the cost of reducing the service performance.
The most susceptible system of coverage problems is the wireless communication system, due to
channel fading characteristics which are decided based on the environment where radio
propagation occurs. Radio propagation is the behaviour in which radio wave encounters while
signals are transmitted from transmitter to receiver. In addition, many constraints like scattering,
reflection, refraction, absorption, and among others will affect the radio wave. These problems can
be reduced using a set of mathematical expressions and algorithms, known as propagation models
(path loss models) which estimate the strength of the transmitted signal. Pathloss is the reduction
of an electromagnetic wave power density. The following equation shows a free-space path loss
3
model. In [2] comprehensive survey of the various network coverage prediction techniques has
been conducted.
PL(dB) = 10Log
Pt
Pr
Gt Gr ƛ^2
= −10Log[(4π)^2
d^2
]
Where
Pt is the transmitted power
Pr is the received power.
Gt, Gr: antenna gains; λ: wavelength in meters
Figure 2: The propagation phenomena.
Similar to the other wireless communication system such as WiMAX and HSPA the LTE features,
a rate layering feature that is the inter-relation of coverage radius and cell edge rate. This is due to
the fixed power offered by user equipment which is normally 23dbm.
The downlink cell edge rate can be calculated as follows:
4
Cell edge rate: NDS * NRBU * NAT * R * MML / TS.
where
NDS, is the number of a different data streams transmitted
NRBU, is the number of resource blocks assigned to the user per frame
NAT, is the number of available traffic carrying resource element per resource block
R, is the coding rate, MML, is the modulation model level
TS, is the Duration of each frame.
In addition, depending on, the user mobility, location in the active cell, and cell load variety, the
real throughput data is highly variable. To solve this problem, estimating and analysing cell load
and cell throughput in a certain bandwidth and inter-cell distance is required. As stated by [3].
1.3 MIMO
Modern wireless communication networks use MIMO (multiple input multiple output) technology
to improve high data rates and achieved throughput as a special MIMO technique, beamforming
also permits targeted illumination of specific areas, making it possible to improve transmission to
users at the far reaches of cell coverage [4].
MIMO communication sends the same data at several signals instantaneously through multiple
antennas, while still using a single radio channel. This is a kind of antenna diversity that uses
multiple antennas to develop signal quality and strength of radio frequency communication links.
The data is divided into multiple streams at the transmitter side and gathered on the receiver side
by another MIMO radio managed with the same number of antennas. In MIMO the receiver is
designed to consider the very small-time difference between the reception of each signal,
additional noise or interference, and lost signals.
Multi-user-MIMO has an attractive feature that significantly improves the spectral efficiency of
the system and the reliability of the communication link that is utilizing multiple antennas in both
receiver and transmitter ends.
Multiple MIMO have several advantages over single input single output (SISO).
5
1. MIMO radios can use the bounced and reflected RF transmissions (known as multipath
propagation) due to various obstacles to improve signal strength with line-of-sight and, even
without clear line-of-sight,
2. Total throughput can be increased, allocating for greater quality and quantity of video, voice or
other data to be sent over the network.
3. By using multiple data streams, problems such as fading caused lost or dropped data packets
can be degraded, resulting in better video or audio quality.
Figure 3: 4X4 MIMO system, where four antennas from the transmit radio communicate with four
antennas on the received radio to improve link connection strength and bandwidth.
1.4 Coordinated multi-point(comp)
With the higher increase of user data rate and application demands, mobile operators and
researchers have been aiming for a high quality of service in the last decade. Comp technology
operation gives a significant solution to improved throughput and enhanced coverage performance
by reducing interference, especially for cell-edge users [5]. In Comp operation, multiple Base
Stations (BS) coordinate with each other in such a manner that the user's information signal from
neighbouring evolved Node B (eNB) reduces interference or even can be combined to improve
received signal quality. Comp transmission is relying on, the sharing of coordination information
6
via backhaul links, which usually, consists of the user's feedback that explains the channel
condition.
The fundamental principle of Comp is utilizing multiple transmit and receive antennas from
multiple antenna site locations, which may or may not belong to the same physical cell, to enhance
the received signal quality as well as to reduce interference, improve spectrum efficiency and
enhance effective coverage area by exploiting the co-channel interferences. Comp mainly has been
targeted to improve cell-edge UE experience, but regardless of the location, it is also used to
enhance the system throughput to UEs those experience strong signals of different BSs/cells.
Comp is mainly categorized as inter-site Comp and intra-site Comp. In inter-site Comp, the
coordination is performed between BSs located in separated geographical areas. On the other hand,
intra-site Comp enables the coordination between sectors of the same BS, where the coordination
is performed through multiple Antenna Units (AUs) that allow coordination between the sectors.
[ 3] the figure below illustrates both Comp categories.
Figure4: Basic Comp Technology; Inter-site Comp and intra-site Comp
7
1.4.1 Downlink comp transmission
Base station(BS) cooperation in the downlink can highly improve the average throughput in
general and cell-edge throughput in particular. The third-generation partnership project (3 GPP)
has divided the downlink coordinated multi-point into two categories. [6]
1.4.1.1 Coordinated scheduling/beamforming
The data is only available in a serving cell while user beamforming and scheduling decisions are
made with the coordination of the sectors. Semi-static point selection (SSPS) is used to make
transmission decisions. Coordinated scheduling/beamforming gives fast strict coordination. but, at
the same time, it uses the MIMO antenna capabilities through beamforming in a coordinated
manner.
1.4.1.2 Joint processing comp
This is the most advanced comp method which improves the spectral efficiency, as a whole and
cell-edge user spectral efficiency in particular. In this mechanism, the user data to be transmitted
is available in multiple sectors of the network.
In terms of cooperation mechanism, JP-COMP is sub-divided into two main types, named joint
transmission comp (JT-COMP) and dynamic point selection comp (DPS-COMP)
.
8
Figure5: Downlink comp transmission
2. Literature review
With continuous of mobile communication technology, wireless mobile network frame is more
and more complicated, therefore, reasonable coverage and throughput enhancement techniques are
the key to survival for the network.
For this research, several kinds of literature on coverage prediction and cell edge throughput
enhancement techniques for mobile networks were revied. To begin with [7], the author predicted
the UMTS coverage network of a selected area in Addis Ababa city based on two spatial
interpolation techniques, named IDW and OK.
9
In addition, the two methods were evaluated and selected the better one, taking CPCHRSCP
collected from the drive test as a metric. The performance estimation of those algorithms was
evaluated through the cross-validation, coefficient of determination(r^2), mean absolute error
(MAE) and root mean square error (RMSE). Results declared that the two coverage prediction
methods can predict coverage holes, but, based on the exponential model of semi variogram with
an optimal of neighbours showed that the OK method estimated less error and accurate value than
the IDW method. According [8], the authors innovated and developed a new scheduling technique
named edge user-friendly scheduler (EUFS) and they aimed to increase the throughput
experienced by the cell edge users by increasing the probability of assigned resource blocks
without impacting of cell centric users. After setting up several experiments comparing the
performance of the proposed new scheduling technique and several other methods. particularly,
the proportional fair (pf) method it’s proven that the proposed algorithm has achieved better
performance for cell-edge users than the other methods.
In [9], an approach that detects cells with coverage holes and diagnoses their type severity was
proposed. Furthermore, the author presented another method which can analyse the impact of each
coverage hole has on the users both LTE and underlying RAT at the same time. As stated by the
author, this was carried out by introducing a new inter-technology PI based on mobile traces which
allows analysing the behaviour of the users who leave LTE by means of IRAT handovers.
The proposed approach was evaluated in a live LTE network with assets of real data collected from
the same network.
According [10], disclosed, the possibility and accuracy of real-time mobile bandwidth and handoff
predictions in 4G/LTE and 5G networks. It collected long series traces with rich bandwidth,
channel, and context information from the public transportation systems. Furthermore, it
developed recurrent neural network models to maintain temporal patterns of bandwidth evaluation
in fixed-route mobility scenarios. For 4G and 5G co-existence networks, a new problem of handoff
prediction between 4Gand 5G was proposed, which is important for low latency applications like
self-driving strategy in realistic 5G scenarios. A classification and regression-based prediction
model, which improve more than 80% of accuracy in predicting 4G and5G handoff in the recent
5G dataset were achieved.
10
In [11], propagation coverage prediction system based on the inversion method was presented.
The method was suitable for most wireless environments and better than the traditional empirical
prediction model as the authors stated in their work.
In [12], the network coverage and network quality of coordinated LTE small cell radio points were
compared to that of a cluster of independent standalone small cells. the conducted measurements
were compared to theoretical predictions as measured by RSRP, SINR, and CQI. And it’s shown
that the coordinated small cell provides significant improvement in the overall quality of LTE
coverage. In [13], a 3D LTE signal coverage in a residential district were predicted, the 3D
environment model of the target scenario is constructed. It’s used a ray-tracing model which have
the ability to accurately estimate 3D signal coverage for urban scenarios. As result showed the
transmission loss of the walls and windows decreased the indoor received power compared to the
surrounded outdoor locations.
In [14], a review on techniques to improve cell edge performance were conducted. The study was
categorised into two parts which are techniques to improve cell edge performance over cellular
networks and over wireless local area networks (WLAN) respectively. The work was defined that,
there are existed several techniques to improve cell edge performance over cellular networks,
proposed by different researchers. But, in contrast, its cleared the shortage of techniques to enhance
cell edge performance over wireless local area networks. Due to this, the authors presented
adaptive modulation and coding scheme technique which improves cell edge performance over
wireless local area networks.
The development of long-term evolution (LTE) with self-organizing network (SON) functions has
been considered an effective way to overcome radio coverage problems and expenditure of
operational tasks, as stated by [15].
11
3. Problem of Statement
The estimation of coverage and throughput in cellular network technologies is very challenged.
Due to sophisticated planning tools used by the network operators and the limitation of coverage
prediction path loss models. In particular, AMTEL Telecom which is our case study is facing many
challenges including coverage holes, handover failure, sectors with the same physical cell identity,
circuit-switched fallback problem, and limited throughput specifically for cell-edge users.
Therefore, analysing the performance of coverage and throughput improvement techniques are
extremely important to come up with good coverage and achieved throughput. Moreover, this work
aims to propose valuable techniques which optimize the network quality of service.
4. Objectives
4.1 General objective
The general objective of this thesis is to study and analyse the performance of coverage
prediction and cell edge throughput improvement techniques and propose a promising technique
that helps to avoid coverage-related problems and met the demand of subscribers.
4.2. Specific Objectives
The specific objectives of this study are:
•
Collecting the data of the study case (AMTEL) by both drive test and walk test
•
Taking measurements of different LTE parameters
•
Solving cell edge problem using LTE coverage prediction improvement techniques
•
To take digital elevation model of the target area as an input and to construct coverage
map
•
Improving cell edge throughput by enhancing whole throughput
•
To evaluate the performance of LTE enhancement techniques
•
Maintaining cell centric throughput to permanently achieve an acceptable level of
performance
12
5. Methodology
To address enough/efficient coverage and cell-edge throughput by analysing the performance of
coverage prediction and cell-edge throughput improvement techniques in the LTE cellular
network. This thesis will follow the following procedures.
1. Literature review: various literature were reviewed.
2. Data collection: the data used in this thesis will be based on AMTEL telecom LTE,
specifically target area (Garowe city), and will be collected by drive test method using
TEMS Pocket and TEMS investigation software while LTE (long-term evolution) key
performance indicators are taken as metrices.
3. Analysing the performance of coverage and throughput improvement techniques with
consideration of LTE key performance indicators.
4. Selecting the best promising technique to improve coverage, quality of service (QOS),
and cell-edge throughput.
The following figure illustrates the methodology used.
13
start
Literature and
Data Collection
Input data to the
Simulation
Software with LTE
Parameters Like
SINR and RSRP
Evaluate
Coverage/throughput
Improvement
Techniques
NO
Throughput
Improved
NO
Choose
best
Technique
Coverage
Improved
Generate Coverage Map
Map Classification
Coverage Holes
detected
Figure6: Methodology
14
6. Scope of the Study
The scope of this thesis is restricted to the performance analysis of coverage prediction and cell
edge throughput improvement techniques in the LTE cellular network. Moreover, this study is
performed by taking AMTEL telecom Somalia as a case study.
7. Significance of the Study
The importance of this proposal is to fill an essential gap which is necessarily required to be
addressed. That is analysing the performance of coverage prediction and cell-edge throughput
improvement techniques aiming at the acceptable level of performance. The AMTEL telecom
and its subscribers will benefit from this work.
15
8. Work Plan
Month
Tasks
Week
Februa March
ry
1
2
3
4
1
2
3
April
4
1
2
3
May
4
1
2
July
June
3
4
1
2
3
4
1
2
August
3
4
1
2
3
4
Septem
ber
Octobe
r
1
1
2
3
4
1. Literature
Review
2. Data
Collection
3. Theoretical
Analysis
4. Data Analysis
5.
Documentation
6. Draft Report
Submission
7. Final Report
Submission
16
2
3
4
9. Budget Plan
Number
Category
Quantity
Unit cost
Cost (birr)
1
Software’s for
1
7,000
7000
LS
6000
6000
5000
5000
LS
3000
3000
Communication LS
4000
4000
data analysis
and simulation
purpose
2
Transportation
cost
3
Data collection
cost
4
Thesis
documentation
cost
5
expenditure
Total cost
25000
17
References
[1] J. wiley, LTE backhaul planning and optimization, united kingdom, 2016.
[2] E. O. Oluwafemi, A. M. Zungeru, C. K. Lebekwe, and J. M. Chuma, ""Cellular communications
coverage prediction techniques: A survey and comparison."," IEEE Access, pp. 113052-113077, 8
(2020).
[3] A. G. Flattie, "LTE Cell Load Analysis Using Live Network Data," J. Commun, vol. 14, pp. 580-586,
7(2019).
[4] R. Schwarz, "LTE Transmission Modes and Beamforming," white paper, 2015.
[5] M. S. Ali, "On the evolution of coordinated multi-point (CoMP) transmission in LTE-advanced,"
International Journal of Future Generation Communication and Networking, Vols. Vol.7, No.4, pp.
pp.91-102, 2014.
[6] R. Irmer, p. marsch and m. grieger, "Coordinated multipoint: Concepts, performance, and field trial
results," IEEE Communications Magazine, pp. 102-111, 2011.
[7] Z. Kassaw, Coverage Prediction Based on Spatial Interpolation Techniques: The Case of UMTS
Network in Addis Ababa, Ethiopia, addiss ababa, 2020.
[8] b. A. A. E.-M. c. M. S. c. S. M. N. a. H. M. E.-H. d. Wael S. Afifi a, "A novel scheduling technique for
improving cell-edge performance in," Ain Shams Engineering Journal, p. 487–495, 2021.
[9] G. Andrades, A. Raquel Barco and I. Serrano, "A method of assessment of LTE coverage holes,"
EURASIP Journal on Wireless Communications and Networking , pp. 1-12, 2016.
[10] M. L, G. J, C. Y, C. H and . L. Y., "Realtime Mobile Bandwidth and Handoff Predictions in 4G/5G,"
Computer Networks, p. 108736, 27/4/2021.
[11] B. L. a. B. K. Dakun Bao, "The Inversion-based Coverage Prediction Tool for Cellular Networks - Real
City Experiments," JOURNAL OF NETWORKS, Vols. VOL. 8, NO. 8,, 2013.
[12] W. W. R. Jay and E. Webster, "Comparing RSRP, CQI, and SINR measurements with Predictions for
Coordinated and Uncoordinated LTE small cell Networks," International Conference on
Microwaves, Communications, Antennas and Electronic Systems (COMCAS), pp. 1-5, 2015.
[13] W. Wanqiao, K. Guan, D. He, Z. Zhong, L. Zhu, T. Shui and C. Liu, "3D LTE Coverage Prediction for
Residential District by Ray Tracing Simulation," International Symposium on Antennas and
Propagation & USNC/URSI National Radio Science Meeting, pp. 81-82, 2018.
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[14] W. N. W. M. 2. A. S. M. A. 3. Nur Syazwani Mustaffa 1, "A Review on Techniques to Improve the
Cell Edge," International Journal of Advanced Trends in Computer Science and Engineering, Vols.
Volume 9, No.1.4,, 2020.
[15] K. L. P. Jiang, L. Kejiong, P. Jiang and J. Bigham, "Partitioning the wireless environment for
determining radio coverage and traffic distribution with user feedback," National Conference on
Communications (NCC), pp. 1-5, 2011.
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