ABSTRACT
Soft frequency reuse (SFR) techniques have been deployed to address the problem of interference experienced by users in cellular networks. In some of these techniques, resources allocations are based on the assumption that users are uniformly distributed.However, in a real network scenario where SFR is deployed for resource allocation, the distribution of users in the network regions is random. Analysis of the impact of random deployment of users in such network scenarios is essential for designing efficient networks. This research proposes a SFR algorithm (User-SFR), which intelligently adjusts resource allocation parameters according to the load distribution in the network. When compared with several results of a fixed SFR algorithm, the results for the proposed User- SFR outperforms the fixed SFR.The Signal to interference plus noise ratio (SINR) of the users at the edge region improved by about 3.2% and the Capacity improved by over 202%.This implies that a more realistic and enhanced network is achieved when random distribution of the users in a network is considered against the assumed uniform distribution of users.
CHAPTER ONE
1.0 INTRODUCTION
1.1 Background to the study
Cellular communication has evolved tremendously and consistently for over five decades (Lopa, 2015).The use of large and cumbersome radios for communication was replaced with more portable but fixed devices. This was to be further replaced with compact, hand-held, wireless and mobile devices for the transmission and reception of speech, texts and media (Michael, 2019). The development of a more portable technology and better interconnection system came with notable advances in both the networking of wireless communication and sustenance in its usage.
Recently there has been a characteristic growth in the number of connected devices with the smartphone taking the centre stage. In order to differentiate advances made in network architecture as well as device hardware, the revolutions have been christened as Generations (G). From the first generation to the fifth generation of cellular communication technologies, specified band of frequencies and frequency reuse is employed (Mutabazi, 2019). This enables the provision of a service to a larger number of subscribers while increasing the effective use of the available bandwidth.
More so, the creation of a variety of communication networks is enabled by fully integrating the emerging capabilities of the mobile phone. The unprecedented increase in demand and consumption, as well as the development of different types of services accelerated the rapid technological expansion of advanced cellular communication networks, together with unceasing improvement of the cellular devices themselves ( Zeng et al.,2018).
The development of the 5G network is to provide a solution to the high demand for more data rates occasioned by the exponential rise in the number of user equipment in modern cellular networks. Such demand is beyond the theoretical upper cap of 200Mbps presently achieved by the 4G networks. The 5G network is envisaged to achieve a data rate of around 100 Gbps and a minimum latency of 1 ms as well an enhanced user Capacity ( Qamar et al., 2019). According to Mumtaz et al. (2017), among the peculiar characteristics of 5G, flexibility is inherent. It supports several use cases in optimal manner using a spectrum < 6GHZ and > 6GHZ.
Among the many techniques that can be deployed to achieve high data rate and capacity in 5G is network densification. Densification is achieved by deploying more macro base stations (Macro Cellular densification).The goal is to increase user capacity and coverage. It can also be expanded to include the use of small cells with reduced coverage footprint. These small cells are easier to install and less costly to obtain. The deployment of more small (micro) base stations could be termed (Micro cellular densification) (Romanous et al., 2015).
However, increasing the number of randomly deployed cells in the network consequently leads to more challenges including inter-cell interference (ICI). ICI can be mitigated by intelligent spectrum allocation using frequency reuse techniques. Spectrum allocation has frequency reuse-1(FR-1), frequency reuse-3(FR-3), and Fractional frequency reuse (FFR).The FFR is usually considered under Strict Frequency reuse and Soft Frequency reuse(SFR), where the coverage region is divided into two parts ( Adejo and Boussakta, 2016). Soft frequency reuse is selected because it gives a better relationship between interference management and bandwidth utilization ( Elfadil et al., 2015).
1.2 Statement of the Research Problem
As Cellular communication continues to evolve from one generation to another, new mobile terminals have also continued to emerge (Michael, 2019). The recent adoption of smart phones and other mobile Internet devices has boosted the cellular communication revolution resulting in enhanced user experience. This can be seen in the invention of high-bandwidth- consuming applications such as video streaming and mobile cloud. These have caused an exponential growth in data traffic requirement, exceeding the theoretical limits of network capacity and spectral efficiency of existing cellular systems ( Qamar et al., 2019).
The launch of 4G cellular networks was thought to have the architecture to address this rise in data rate demand. This is achieved through massive deployment of base stations to form a dense network scenario. Coverage is improved as the number of base stations increase and the distance from users to the base station reduces (Adejo and Boussakta, 2016). However, the amount of interference in the network significantly increases and this causes serious degradation in the quality of service available to the user. Therefore, managing ICI has remained one of the major factors that limit the performance of current wireless cellular network systems.
Fractional frequency reuse (FFR) and Soft frequency reuse (SFR), with several of their variants have been introduced and are being improved as effective ways to optimize spectrum and control the ICI in developing 5G networks (AboulHassan et al., 2015). However, most of the previous works studied have not considered network load (the effect of the number, location and demand of users) in their frequency reuse algorithms. An improved Soft frequency reuse algorithm that takes into consideration the user‟s load demand in the network is hereby proposed.
1.3 Aim and Objectives of the Study
The aim of this research work is to develop a new resource allocation technique that adequately caters for varying load distribution in typical 5G networks.
This is achieved through set objectives. The objectives includes to:
i. develop an improved Soft frequency reuse algorithm that intelligently allocates resources according to load distribution (user demand) in a network.
ii. vary the number of users in various regions of the network adopted and modify it to include downlink interference probabilities under soft frequency reuse.
iii. implement the algorithm developed in (1) through simulations using Matlab.
iv. test the performance of the algorithm using cellular network performance metrics such as Signal-to-interference-plus-noise ratio (SINR) and Capacity.
1.4 Justification of the Study
It is impossible to have a cellular network that does not suffer interference. One of the most effective ways of managing interference is to effectively deploy frequency reuse in cellular networks. It has been widely researched leading to variants and modifications with accompanying reduction in interference levels in networks. However, several of these studies assumed uniform distribution of users in the network. This assumption is made for simplicity and does not depict a real network scenario. In this study a non-uniform distribution of users in various network regions is considered. This approach is novel and has not been seen in pervious works studied and is therefore justifiable as an area of interest for study.
1.5 Scope of the Study
Resource allocation for interference management in Cellular networks is a broad research area. It encompasses several models, innovations and technologies that drive mobile communication. For the purpose of this research, a single tier network of macro base stations is adopted. The base stations are restricted to the popular hexagonal network arrangement and the orthogonal frequency division multiple access (OFDMA) scheme is employed under the Long term evaluation (LTE) networks. Analysis is carried out by considering sectors of neighbouring base stations as they are arranged in clusters of three. Bandwidth allocation has different methods under the broad area of frequency reuse including full frequency reuse, frequency reuse-3, strict frequency reuse and soft frequency reuse where the coverage region is divided into two parts. Soft frequency reuse is selected because it guarantees a balance between interference management and bandwidth utilization. This work considers that the SINR depends on the distance between users and the base stations, without detailed analysis of the channel conditions. In the area of scheduling, it is therefore assumed that the network resources are uniformly assigned to users without any preference, as against several other existing scheduling methods.
1.6 Organisation of Thesis
The thesis is organised into five chapters. The remaining content is structured as Literature review and is contained in chapter two. In chapter three, the methods followed to achieve the results are logically presented while the discussions on the results obtained are done in chapter four. Chapter five is made up of the conclusion, recommendations as well as the contributions to knowledge.
This material content is developed to serve as a GUIDE for students to conduct academic research
USER DENSITY BASED SOFT FREQUENCY REUSE ALGORITHM FOR 5G CELLULAR NETWORKS
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