ABSTRACT
With the deployment of 5G wireless communication networks to handle the increasing number and requirements of users, the need for high energy efficiency and throughput is of utmost importance as more devices and people connect to the wireless communication network (internet) daily. Massive multiple-input multiple-output (MIMO) which has to do with the theory of using several hundreds of antennas serving several users with lower power consumption, less delay and high throughput was used by this research work as a solution to the above concern. The work modeled a massive MIMO network which serves as the technology for an energy efficient 5G wireless communication network and to bring it home to Nigeria, there was a comparison of the existing Wireless metropolitan area Network (WMAN) and 4G LTE in Nigeria which showed the massive MIMO to have better performance and Energy Efficiency. The design and comparison of the WMAN, 4G (Traditional MIMO) and 5G (Massive MIMO) network models were done in Simulink environment of Matlab using the knowledge of the design and operation of the massive MIMO system which was employed in the OFDM modulators and demodulators. For the analysis and comparison of the various networks, the transmission data rate, receiver power spectrum, receiver constellation, signal-to-noise ratio, effects of SNR on throughput of signals, power density spectrum of antenna, signal-to-noise ratio against packet error rate, the energy efficiency and capacity and the transmission speed were considered for each of the networks. From the simulation results, there was a clear indication that the massive MIMO network had better performance with reference to the above key performance indicators considered when compared to the WMAN and LTE. The energy efficiency for LTE which is at less than 2 bits/J was improved to almost 10 bits/J using the massive MIMO system. Finally in terms of energy efficiency, the simulation results also showed that the massive MIMO system has higher energy efficiency which serves greatly to solve the concerns raised by the increasing number of connections.
TABLE OF CONTENTS
Title Page i
Declaration ii
Certification iii
Dedication iv
Acknowledgement v
Table of Contents vi
List of Tables x
List of Figures xi
List of Abbreviations xiii
Abstract xiv
CHAPTER 1: INTRODUCTION
1.1 Background of the Study 1
1.2 Problem Statement 3
1.3 Aim and Objectives 5
1.4 Significance of Study 6
1.5 Scope of the Study 6
1.6 Justification of the Study 6
CHAPTER 2: LITERATURE REVIEW
2.1 Historical Background of 5G Wireless Networks 7
2.1.1 Limitations of 4G 9
2.1.2 Overview of 5G and system requirement 11
2.1.3 The need for 5G technology 13
2.1.4 Network architecture of 5G 14
2.2 Theoretical Background on Energy Efficiency and KPI for Energy Efficiency 15
2.2.1 Energy efficiency metrics 18
2.3 Overview of massive MIMO echnology 20
2.3.1 Time division duplex 22
2.3.2 Linear processing 22
2.3.3 Favourable propagation 23
2.3.4 Array size 23
2.3.5 Scalability 23
2.4 Working Principle of Massive MIMO 24
2.4.1 Channel estimation 26
2.4.2 Uplink data transmission 27
2.4.3 Downlink data transmission 27
2.5 Review of Some Related Works 27
2.6 Research Gap 38
CHAPTER 3: MATERIALS AND METHODS
3.1 Materials 39
3.2 Methods 39
3.2.1 Massive MIMO systems 39
3.3 Modelling of the Energy Efficiency 41
3.3.1 Area spectral efficiency (ASE) 42
3.3.2 Spectral efficiency (SE) 42
3.3.3 Signal to interference and noise ratio (SINR) 43
3.3.4 Area power consumption (APC) 44
3.3.5 Circuit power (P¬cp) 45
3.3.5.1 Transceiver chains (PTC) 45
3.3.5.2 Channel estimation (PCE) 46
3.3.5.3 Coding, decoding and backhauling (PC-BH) 46
3.3.5.4 Linear processing (PLP) 47
3.4 System Channel Model Matrices 49
3.5 Design of 5G Massive MIMO Model 52
3.5.1 Design of traditional MIMO model (4G LTE) 56
3.6 Transmission Delay and Latency 56
3.6.1 Massive MIMO delays 57
3.6.2 Transmission speed of network systems 59
3.7 Spectral Efficiency and Energy Efficiency of MIMO Systems 60
3.7.1 Improving spectral capacity of MIMO networks 61
3.8 Theory of Optimal Networks: Spectral and Energy Efficiency 63
3.9 Massive MIMO Capacity and Throughput Calculations 63
CHAPTER 4: RESULTS AND DISCUSSIONS
4.1 Results Comparison 66
4.2 Data Transmission 66
4.2 Receiver Power Spectrum 67
4.3 Receiver Constellation 68
4.4 Signal to Noise Ratio (SNR) 69
4.5 Effect of SNR on Throughput of Signals 70
4.6 Power Density Spectrum of Antenna 72
4.7 Signal to Noise Ratio against Packet Error Rate 73
4.8 Energy Efficiency and Capacity 74
4.9 Transmission Speed of the 4G and 5G Model 75
CHAPTER 5: CONCLUSION AND RECOMMENDATION
5.1 Conclusion 76
5.2 Recommendation 76
5.3 Contribution to Knowledge 77
References
Appendices
LIST OF TABLES
Table 2.1: Comparing traditional MIMO and massive MIMO 21
Table 3.1: Network simulation parameters 40
Table 3.2 Network simulation parameters 41
Table 3.3: Comparison for WLAN, 4G LTE and the 5G wireless networks 60
Table 3.4: SNR values of 4G and 5G for percentage values of BER 62
Table 3.5: 5G and 4G throughput table calculation 65
LIST OF FIGURES
2.1: Functional architecture for 5G network. 14
2.2: Uplink and downlink operations of a massive MIMO system 24
2.3: TDD protocol of massive MIMO transmission 26
3.1: Simulation flow chat 51
3.2: Simulink model showing physical layer WMAN OFDM connection 52
3.3: Simulink model of 5G transmitter and receiver with MIMO modulated and demodulated OFDM 53
3.4: concatenated modulator bank of 5G TX and RX 54
3.5: concatenated demodulator bank of 5G TX and RX 55
3.6: Simulink model showing traditional MIMO TX and RX, designed without the massive MIMO modulated and demodulated OFDM 56
4.1: Transmission data rate by the two models 66
4.2: Receiver power spectrum for 4G LTE and 5G 67
4.3: RX post constellation plot for 4G LTE and 5G 68
4.4: RX pre constellation plot for 4G LTE and 5G 69
4.5: Signal to noise ratio comparison of 4G and 5G 70
4.6: Effect of SNR on system throughput and bit error rate BER for 4G and 5G 71
4.7: Power density spectrum of TX and RX antenna of both 4G and 5G 72
4.8: Plot of signal to noise ratio against packet error rate 73
4.9: Energy capacity and efficiency of 4 different network systems 74
4.10: Plots comparing the transmission speed of the 4G LTE and 5G networks 75
LIST OF SOME ABBREVIATIONS USED
ABBREVIATION DEFINITION
4G Fourth Generations
5G Fifth Generations
AI Artificial Intelligence
BER Bit Error Rate
BS Base Station
CSI Channel State Information
D2D Direct-to-Direct
DL Downlink
ECR Energy consumption rate
EE Energy Efficiency
LTE Long Term Evolution
MIMO Multiple Input Multiple Output
OFDM Orthogonal Frequency Division Multiplexing
PER Packet Error Rate
PSK Phase Shift Key
SE Spectral Efficiency
SNR Signal to Noise Ratio
TCM Trellis-coded modulation
TDD Time Division Duplexing
TX and RX Transceiver and Receiver
UE User Equipment
UL Uplink
WMAN Wireless Metropolitan Area Network
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND OF STUDY
The 4G wireless communication networks has become well established and deployed in a lot of countries making researchers to divert their attention and research into the next generation of wireless communication network, 5G which is viewed as a generation that will come with very large bandwidth, massive connectivity, throughput, high speed, capacity and low latency. The network is expected to provide as much as 1000 times more capacity than the existing 4G. Andrews et. al, (2014). The fast increase in cellular networks has resulted in more number of subscribers thereby resulting to increased demand for cellular traffic in the past few years. Predictions related to the massive development of the telecommunication industry shows that data rate per user will witness a much increase. As a result of this, there has been an increased growth in energy consumption of the networks which have resulted to a rise in carbon dioxide (CO2) emission worldwide. In addition to this; there has also been a rise in the operational cost for operators. This has increased the drive for the requirement of new ideas in the area of energy efficient communications. Under this circumstance, a very important role is played by energy efficient wireless networks in the advancement of the reduction of global warming and its side effects. Therefore, energy efficiency has become a major issue for cellular and wireless networks because of the very large necessity of energy for designing and operating the different generations (2G, 3G, 4G, and 5G) of wireless communication systems. It is a seemingly difficult thing to do trying to reduce the energy consumption and also at the same time maintaining a good quality of service. It has been seen in several reports that the energy consumption of cellular wireless communication networks' infrastructure, the internet, and wired communication networks take up to 3% of electric energy consumption throughout the world and it is expected to increase speedily in future. Hasan et al., (2011).
Andrae and Edler, (2015) in their research predicted that the electricity consumption in communication technology was expected to increase by more than 3 times within 3 decades. Additionally, mobile terminals in wireless communication systems need more energy saving since the evolution of battery technology is much slower when viewed side by side with the growth rate of energy consumption.
In light of the above, seeking high Energy Efficiency (EE) has become the greatest challenging trend for the design of wireless communications networks in future. During the past decades, a whole lot of attempts were made to enhance network throughput. Several network deployments have been well studied to enhance area spectral efficiency. These include the optimization of the number of base stations in cellular networks and also the use of relay nodes in relay systems. Different schemes for resource allocation have been proposed to ensure Quality of Service (QoS) of users. In order to provide spectral efficiency, different advanced techniques such as orthogonal frequency division multiple access (OFDMA), Multiple Input Multiple Output (MIMO) techniques and relay transmission have been used extensively. However, high network throughput normally entails that there is a high level of energy consumption which is the case with 5G wireless communication networks. Therefore it is a very crucial task to look for ways or approaches to ensure the energy consumption in wireless communication network is reduced while at the same time achieving a high energy efficiency and throughput.
A very well known technique for enhancing reliable communication is by the use of multiple input multiple output antenna. Research has shown that deploying so many antennas at both the receiving and transmitting end as in MIMO technology increases the amount of data that can be received and transmitted via a certain frequency band. The fifth generation of wireless communication (5G) is expected to have much higher speed and capacity under limited spectrum as compared to the existing 4G systems. Massive MIMO is a strong technology for an efficient 5G system. This technology came into limelight when Tom Marzetta studying at bell labs published his paper on “Noncooperative Cellular Wireless with Unlimited Number of Base Station Antennas”. From that time, more contributions have been made by him and his colleagues. Ngo et al., (2013). Massive MIMO technology proposes new schemes to practically implement ideas from multi user MIMO, where non cooperative single antenna users, K are served at the same time via a base station with a very large number of antenna elements, M Ngo et al., (2013). Significant improvements in channel capacity and the radiated energy can be achieved using massive MIMO. Ngo et al., (2013). Hence massive MIMO can be seen as a gold mine because of the huge advantages it will bring to 5G networks such as the ability to accommodate high number of users with very high data rates and reliability with very low power consumption.
1.2 PROBLEM STATEMENT
The wireless communication network (internet) links up more and more people from different parts of the world. As a result, network requirement and content are drastically increasing with the fast developmental speed of the current information society. The start up of each successive generation of wireless technology has introduced new services that need extended coverage for a larger number of people and places. Simultaneously, growing networks that support this demand for fresh services have caused a corresponding rise in energy consumption. With the deployment of 5G, the challenge is to make sure that the network’s total energy performance is secured in as much as new capabilities like increased gigabit speeds and low latency are being introduced.
There is a steady increase in the world’s annual electricity use for the telecommunication industries. This increase is expected to reach 51% of global electricity if nothing is done to sufficiently improve on energy efficiency of wireless communication network and access/data center networks. Andrae and Edler, (2015). A major contributor to the appreciable increase in energy usage in the telecommunication sector has been attributed to the wireless communication networks, and the major consumer of energy in wireless communication networks are the base stations which takes up to the 57% of the overall energy consumed. Piovesan et al., (2018). There has been an increase in the number of base stations worldwide which has made operating expenses to increase, and as operating cost was increasing, so also was electricity bills increasing.
In addition, emissions of greenhouse gas are significantly contributed by wireless communication network operations. According to Suarez et al., (2012) the amount of energy emitted by wireless communication networks was expected to increase drastically by 2020 and should account for up to 51% of the information and telecommunication technology sector’s carbon footprint. With the deployment of the fifth generation of wireless technology, there is every need to seek for ways to enhance energy efficiency which has become a major concern to the industry. The massive MIMO technology used by this research work where the base station is equipped with a massive number of both collocated and distributed antennas serving many users in the same time-frequency resource can help to mitigate this problem. With Massive MIMO, more antennas are deployed in a base station and used to serve users rather than deploying more base stations which has been shown to be the major consumer of energy in wireless communication networks.
1.3 AIM AND OBJECTIVES
The aim of this research is to model and simulate a massive MIMO system for enhancing the energy efficiency of the 5G wireless communication network.
The specific objectives are to:
i. Review existing literatures on energy efficiency and power consumption in wireless networks.
ii. Characterize the wireless communication network with massive MIMO and identify the different key performance indicators for energy efficiency.
iii. Develop a massive MIMO model and solution algorithm for the 5G wireless communication network.
iv. Design and simulate a massive MIMO system for enhancing energy efficiency in 5G wireless network.
v. Validate the enhancement of the network model using a specified network data
vi. Compare the energy efficiency of the massive MIMO model and the existing traditional MIMO and comparison of the energy efficiency of the massive MIMO model with the traditional MIMO model.
1.4 SIGNIFICANCE OF STUDY
With 5G being the most recent trend in the wireless market, researchers have dived into looking for technologies to enhance the energy efficiency which will greatly help to harness wasted power, reducing power consumption and at the same time help to ensuring a greener communications. This research used the massive MIMO as a technology that can greatly enhance energy and spectral efficiencies.
1.5 SCOPE OF THE STUDY
This research covers the modeling and simulation of a massive MIMO system for enhancing energy efficiency in 5G wireless communication network.
1.6 JUSTIFICATION OF STUDY
This research was carried out in an attempt to find a solution for the energy efficiency issues of the 5G wireless communication networks. This is because as more devices and people get connected, it is normal for the power consumption to increase, thereby necessitating the need for an energy efficient network. technologies such as D2D communication, use of sleep mode, mmWaves, small cell and Ultra dense network has not been able to tackle the issue hence the proposal of massive MIMO by this research as a more efficient method which will deploy more antennas at the existing base stations rather than deploying more base stations which are the major sources of energy consumption.
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