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A COMPARATIVE PERFORMANCE ANALYSIS OF INTERFERENCE CANCELLATION TECHNIQUES FOR NON-ORTHOGONAL MULTIPLE ACCESS (NOMA) FOR CELLULAR NETWORK.

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MEng (Prof) RESEARCH PROPOSAL
Submitted By:
ABRAHAM S. MARTEY
1921552
Submitted To:
THE FACULT OF ENGINEERING AND THE BUILT ENVIRONMENT
SCHOOL OF ELECTRICAL AND INFORMATION ENGINEERING
UNIVERSITY OF WITWATERSRAND, JOHANNESBURG
RESEARCH SUPERVISOR: DR. ASAD MAHMOOD
MARCH 29, 2018ABSTRACTInterference cancellation is a very important approach in any network. Needless to say, every network is deemed to have much interference such as self-interference, multiple access interference, co-channel interference, as well as adjacent channel interference. The increasing demand of mobile Internet and the Internet of Things poses challenging requirements for 5G wireless communications, such as high spectral efficiency and massive connectivity. Non-orthogonal multiple access (NOMA), constitutes a promising technology of addressing the above-mentioned challenges in 5G networks by accommodating several users within the same orthogonal resource block,  such as a time slot, subcarrier, or spreading code. This research will seek to determine the comparative performance analysis of existing interference cancellation techniques in NOMA for cellular networks. Cellular networks are bound to get interferences from gadgets in the same or adjusting cells. Researchers have spent a lot of resources finding a way to minimize interference to negligible values in a system. The aim is to lower the distortions by eliminating the interference so as to enhance the throughput and in turn the performance of the system. The use of an adaptive filter in the interference cancellation block allows for cancellation by choosing the filter of choice depending on the amount of noise signal measured. Two potential interference cancellation schemes are compared for the application to a non-orthogonal multiple access in Cellular Network. One is the conventional hard successive interference cancellation (SIC) scheme based on independent single-user decoding and the second, used in this paper, is a soft-in soft-out parallel interference cancellation (SISO-PIC). A pair of users can be served by NOMA when their channel gains are considerably similar. The power allocation strategies play a pivotal role in capacity enhancement. The cancellation order at every receiver is always to decode the stronger users before decoding its own data. Any network improvement seeks to achieve greater system efficiency.

LIST OF TABLES:
Table 2.1: Summary of various Interference Management techniques……………9
Table 5.1: Comparison of Interference Cancellation Schemes…………………….16
Table 6.1: Research Scheduling……………………………………………………17ABBREVIATIONS:
NOMA: Non-Orthogonal Multiple Access
WIFI: Wireless Fidelity
2G: Second – Generation
3G: Third – Generation
4G: Fourth – Generation
5G: Fifth – Generation
IC: Interference Cancellation
LTE: Long Term Evolution
RAN: Radio Access Network
TDMA: Time Division Multiple Access
OFDMA: Orthogonal Frequency – Division Multiple Access
UWB: Ultra-Wide Band
SC: Superposition Coding
AMPS: Advanced Mobile Phone Service
DTX: Discontinuous Transmission
FDMA: Frequency Division Multiple Access
CDMA: Code Division Multiple Access
PAM: Pulse Amplitude Modulation
MATLAB: Matrix Laboratory
WCDMA: Wideband Code Division Multiple Access
LMS: Least Mean Square
RFC: Radio Frequency Channel
IOT: Internet Of Things
HSPDA: High-Speed Downlink Packet Access
PIC: Parallel Interference Cancellation
TABLE OF CONTENTS
Abstract……………………………………………………………………………i
List of Tables………………………………………………………………………ii
Abbreviations………………………………………………………………………iii
Introduction……………………………………………………………………….1
Background of the Study………………………………………………………1
Non-Orthogonal Multiple Access (NOMA)…………………………………..2
1.2.1 Interference in NOMA…………………………………………………………3
Literature Review………………………………………………………………….5
Research Details…………………………………………………………………….10
Research Questions…………………………………………………………….10
Aim of the Research…………………………………………………………..10
Objective of the Research…………………………………………………….10
Problem Statement……………………………………………………………..11
3.4.1 Sub-Problem Statement………………………………………………………..11
Methodology (Investigational Method)………………………………………….12
Scheme of the Investigation…………………………………………………..12
Goal of the Investigation……………………………………………………..13
Importance of the Investigation……………………………………………….13
Assumptions of the Investigation……………………………………………..13
Simulation………………………………………………………………………….15
Simulation Procedure…………………………………………………………15
Goal of the Simulation………………………………………………………..15
Interference Cancellation Techniques………………………………………..16
5.3.1 Comparison of Interference Cancellation Techniques Schemes………..16
Scheduling………………………………………………………………………….17
Conclusion…………………………………………………………………………18
Bibliography…………………………………………………………………………..19
CHAPTER 1: INTRODUCTION1.1 Background of the Study
A cellular network is a mobile network where the last link is wireless. This is to say that in any cellular network, it must be conceptualized that the goal is to ensure that cellular networks are connected in wireless manner. In order for a cellular network to exist, (Viswanath and Tse 123) stipulates that there are some conditions which must be met. To begin with, in any cellular network, there are some land areas which are referred to as cells. These are the main factors that differentiate cellular network from other networks like WIFI networks. These cells that are contained in a cellular network are required to have location transceivers which are also referred to as cell sites or base stations. Basically, a cellular network is a decentralized network where operations are made at different bases instead of conducting them from a common base. The main advantages of this decentralization are stability and strength of network among others. As (Saito and Kishiyama 567) observes, when a signal is brought closer to its end user, it becomes stronger and more stable. This is what has led to emergence of strong network as opposed to those that were there in the past. These include 2G, 3G, 4G and the 5G that is in the pipeline and expected to be there in the year 2020. Cellular concept is the broad term that is used to refer to the technologies that are being adopted in this study. This concept refers to a technology that replaces a single transmitter that was centrally placed to send all signals. The centrally placed transmitter in a cellular network is replaced many transmitters that are decentralized and operate in low power. These small transmitters are assigned to smaller cells hence making them more effective and close to the end user. Advantages of this concept are decentralization, distribution of cellular network, prevention of impact in case of breakage, signal stability, signal management and customer access and identification. When using this technology, it is possible to identify individual users through the location signals.
1.2 Non-orthogonal Multiple Access (NOMA)
In this chapter, we explore the concept of non-orthogonal multiple access (NOMA) method for the upcoming 5G networks. All of the current cellular networks implement orthogonal multiple access (OMA) techniques such as time division multiple access (TDMA), frequency division multiple access (FDMA) or code division multiple access (CDMA) together. However, none of these techniques can meet the high demands of future radio access systems. Non-orthogonal multiple access (NOMA) is a radio access avenue that has been recently instituted to take care of the dramatically increasing demand for user access required for the Internet of Things (IoT), since the fifth generation (5G) networks face challenges in terms of supporting large-scale heterogeneous data traffic. The real key to NOMA, is usually to have signals that possess significant differences in power levels. It’s then feasible to completely isolate the high level signal at the receiver after which cancel it out to keep just the reduced level signal. This way, NOMA exploits the path loss differences amongst users, though it needs additional processing power in the receiver.Looking at NOMA in a bit more detail, non orthogonality is intentionally introduced either in time, frequency or code. Then as the signal is received demultiplexing is obtained as a result of the massive power difference between the two drivers. In order to extract the signal, successive interference cancellation is used within the receiver. The channel gain consisting of elements including the path loss and received signal to noise ratio difference between users is translated into multiplexing gains. Although power sharing reduces the power allocated to each user, both users – all those with good channel gains and those with low channel gains benefit when it is scheduled often and by being assigned more bandwidth. What this means is that NOMA enables system capacity and fairness of allocations to be considerably improved for almost all users.

Along with this NOMA, Non Orthogonal Multiple Access can support a lot more connections than some other systems and this will be especially useful in view of the massive projected increase in connectivity for 5G materialises. One of the reasons why this technology has been designed and adopted is the need to have fast, reliable and secured networks as the world has been converted into a global village. According to (Viswanath and Tse 128), in an era where all global issues like economies, sports, businesses etc are being controlled by the communication systems and technology, there has been need to come up with a better system that will be assigned to cellular networks to ensure that they are effective in driving the world. A research that was done by (Saito and Kishiyama 544) indicated that globally, cellphone use has grown by close to 68% in the last two decades. This means that with this trend, close to 95 percent of the global population will own cellphones by the year 2025.
1.2.1 Interference in NOMA:
Since the principle of NOMA allows multiple users to be superimposed on the same resource, this leads to interference for such systems, which is the major challenge that is associated with NOMA. Interference in a network is anything that prevents encoded information to reach the decoder. There is much interference that can be associated with radio signal. In order to ensure that these interferences are controlled at all costs, technological thinkers have come up with many techniques that can be used to control or limit interference. These are referred to as interference cancellation techniques (Viswanath and Tse 1144).

We are going to use very simple successive interference cancellation (SIC) scheme based on independent single-user decoding to deal with this problem as well as the SISO-PIC which is an improved joint iterative multi-user detection scheme, which has lower complexity than the prevalent multi-user detection. For the SIC, the receiver first decodes the messages for strong users with high power level, then remove their effects from the receiver to decode weak users. SIC has no any sum rate loss in information point of view, but if a non-ideal channel code is employed for each user there will be an error propagation problem, i.e., decoding errors of strong users will affect the decoding for weak users. Another problem in SIC is “user delay” since weak users should wait for the decoding of strong users. These two problems become more serious as the users for SIC increase.

This research is specifically interested in coming up with a comparative analysis of the techniques that are applied in interference cancellation for NOMA. This research will look critically at two major IC techniques with regard to NOMA and compare their performance and complexity.
CHAPTER 2: LITERATURE REVIEWThe cellular network has evolved all the way from 1G to 4G or LTE network. The 5G network is still under research and is expected to roll out by the year 2020. Some of the improvements the network seeks to have are based on the significant reduction of the end-to-end latencyCITATION Kis12 p 475 l 1033 (Kishiyama, Benjebbour and Ishii 475). The innovations in the industry have led to a great need for high data rate applications due to the use of IoT and the mobile internet. For instance, the 5G network is expected to provide speeds of up to ten to twenty times the peak data rate in 4G networks CITATION Ria17 l 1033 (Riazul Islam, Avazov and Dobre). The latency that is attributed to the end-to-end round-trip delay is expected to reduce to about 1 millisecond. When compared to the 4G network, it rates at about one-fifth of the LTE latencyCITATION Kis13 p 477 l 1033 (Kishiyama, Saito and Benjebbour 477). The cellular network is founded on the radio access technology which simulates a radio access network. The RAN uses channels in providing the mobile terminals with a connection to the core network. The system capacity needs to be improved by designing and implementing suitable multiple access techniques. These techniques can be stated under two different approaches as orthogonal multiple access and non-orthogonal multiple accessCITATION Hac15 p 5 l 1033 (Haci, Wang and Zhu 5). The orthogonal technique has different basis functions that enable the receiver to separate, in entirety, the wanted signals from the unwanted signals. It is easier explained that when different users send their message signals, these signals tend to be orthogonal to each otherCITATION Sai13 p 772 l 1033 (Saito, Benjebbour and Kishiyama 772). Some of the common schemes implemented under this approach include the Time Division Multiple Access and the Orthogonal Frequency Division Multiple Access. The TDMA allows several users to share the same frequency channel by sharing the assigned time slotsCITATION Ada16 p 550 l 1033 (Adachi, Ding and Poor 550). The OFDMA is a multiple access scheme that allows the different sub-carrier frequencies to serve the subscribers in an orthogonal manner.
Multiple Access schemes are used to allow many users, stationary or mobile, to share simultaneously a finite amount of wireless frequency spectrum. The sharing of spectrum is require to achieve a high capacity by simultaneously allocating the available bandwidth or available amount of channels to multiple users. Sharing of sources between any users can be done by using different kinds of resources: a single resource is to use a control channel; many sources are the voice or digital channels.
The non-orthogonal multiple access approach, on the other hand, is known to allow multiple users within the same cell to access one frequency channel at the same time. There are a number of merits that come from doing so such as improving the spectral efficiency, the higher cell-edge throughput, relaxed channel feedback and the low transmission latencyCITATION Mad06 p 570 l 1033 (Madihian, Gitlin and Sang 570). The approach ensures that only the received signal strength is required and that no scheduling requests from the user to the base station are required. There are two techniques that employ the NOMA approach namely, the power domain and the code domain NOMA. The non-orthogonal multiple access (NOMA) tends to exploit the power domain multiplexing. It performs superposition coding at transmission and successive interference cancellation at receptionCITATION Wun14 p 102 l 1033 (Wunder, Jung and Kasparick 102). Research studies show that a pair of users can be served by NOMA when their channel gains are considerably different. The power allocation strategies play a pivotal role in capacity enhancement. The cancellation order at every receiver is always to decode the weaker users before decoding its own data. Any network improvement seeks to achieve greater system efficiencyCITATION Vak13 p 225 l 1033 (Vakilian, Wild and Schaich 225). The LTE researchers have reviewed the NOMA as a promising multiple access scheme for the future radio access. The users with better channel conditions decode the messages for the users with poor connections to the base station. Strong users to weak user’s communications can be implemented by the UWB and the BT CITATION Poo l 1033 (Poor, Tsiropoulos and Dobre).
The cellular networks’ performance is largely limited by interference. Interference is human-designed and it can be separated from noise as it is caused by gadgets or devices within the same network. With the increase accessibility to mobile devices due to their affordability, there is more interference within the cellular network. Some methods have been devised to reduce interference, for instance, the designers of the base station tend to achieve a minimum required transmission power rate so that the same low power can be transmitted or received by the different gadgets within a cellCITATION Don16 p 3 l 1033 (Dong, Dong and Feng 3). As a result, there is less interference caused to other devices in the networkCITATION Cre11 l 1033 (Creative World 9). The use of NOMA is a progressive move from the time, frequency and code domains. In the current technologies, the receiver uses rake receiver to detect and minimize interference. The use of NOMA approach has led researchers to attempt enhancements on the performance of other technologies in the cellular network family. These networks are such as the multiple-input multiple-output networks, cooperative communication networks, light communications as well as the relay communication networks. Some of the common schemes under NOMA are as shown below,

Figure 2. SEQ Figure * ARABIC 1: A simple classification of NOMA techniques
There are two basic techniques that play an important role in the comprehension of the class of NOMA, specifically the power domain NOMA. The superposition coding is a technique of simultaneously communicating information to several receivers by a single source. The SC allows the transmitter to send information initiated my multiple users at the same time. This is achieved by setting up the transmitter to encode information relevant to each user. The superposition coding transmitter needs to have two point-to-point encoders that map their respective inputs to complex-valued sequences of the two-user signal CITATION Nik13 l 1033 (Nikopour and Baligh).
Interference is affected by many factors. One of the main factors that affect interference is power level of the signal. According to (Viswanath and Tse 154), signals that have high power are likely to suppress interference compared to signals with low power. An AMPS terminal can transmit at six to eight diverse power levels while increased in steps of 4dB. The message from the Base Station controls the power level of the dynamic terminal. The power in this case stays the same during the conversion. The DTX where the power varies depends upon the speech activityCITATION AlI12 p 385 l 1033 (Al-Imari, Imran and Tafaxolli 385). This is what determines the expected level of interference.
Interference Management Techniques Strength Limitation
Intermodulation Solution
Knowledge-based filtering method Appreciable level of interference reduction was achieved Had extra filter complexities
Interference signal scanning and removal with FFT and notch filter respectively Appreciable level of interference reduction was achieved Had extra filter complexities and RIM distortions are not mitigated
Placement of Frequency agile band pass filters in front of receiver and after transmitter Appreciable level of interference reduction was achieved Commercially unfeasible as frequency agility adds another dimension of complexities and overall cost
Adaptive Noise/interference Cancellation Appreciable level of interference reduction was achieved Requires high level of cooperation between transmitter operators
Frequency Planning
Genetic Algorithm Appreciable level of interference reduction was achieved Excessive time is expended in finding a solution
Simulated annealing Appreciable level of interference reduction was achieved Introduction of dummy frequency increases violation of traffic demand
Ordering heuristic It offers advantages in dynamic cell allocation schemes where cell demand varies in real time. Less considered for static channel allocation schemes.

Ant-colony and multi-agent Optimization It is more effective at handling electromagnetic compatibility constraints and helps improve the generated solutions Optimal solutions are difficult in some instances
Artificial neural networks Appreciable level of interference reduction was achieved The technique may involve high computational complexity
Source: Saito and Kishiyama
Table 2. SEQ Table * ARABIC 1: Summary of various interference management techniques
CHAPTER 3: RESEARCH DETAILS3.1 Research Questions
There are three research questions that will be used in this study. These are as follows:
Q1. What is the performance of the different methods of interference cancellation in NOMA is as assessed in this study?
Q2. What is the complexity of different methods of interference cancellation in NOMA is as assessed in this study?
Q3. Is there any significant difference between the methods of interference cancellation in NOMA assessed in this study?
3.2 Aim of the Research
This study aims at analyzing different techniques as applied in interference cancellation for NOMA cellular networks. The specific aims of the study are to look at the effectiveness of these techniques with respect to NOMA cellular networks, to assess any difference between the techniques in terms of effectiveness, affordability, sustainability and other factors and to make a recommendation on which technique/s can be best suited for NOMA cellular networks.
3.3 Objective of the StudyThe general objective of the study is to make a comparison between the techniques that can be applies in IC for NOMA in cellular networks. Specific objectives of the study are to determine the differences or similarities that exist between those techniques and to make recommendations on the best technique that can be applied considering such factors as performance of the specific techniques and their complexity.

3.4 Problem Statement
The cellular network has had a great problem trying to eliminate the latency in the systems it operates. The latency is projected to reduce to 1 millisecond in the 5G network. There is a lot of interference during reception for the multiple accesses by subscribers. The drawback caused by the interference is that it decreases the sensitivity at the receiver end. The lower the sensitivity, the more the system puts high demands on the linearity in the Radio Frequency and in the base band receiver blocks. There are no setup techniques or schemes to enable the interference cancellation in the current networks. This study was developed in order to look at different techniques that are available for IC in NOMA cellular networks. After making a thorough analysis, the study will differentiate between the techniques and present the most applicable technique/s that can be employed to a cellular network. This analysis will be based on performance and complexity of the techniques.
Sub-Problem Statement
The cellular networks’ performance is largely limited by interference. Interference is a controllable factor and can be controlled using available methods. This research is supposed to come up with the most appropriate technique that can be used for IC. The goal is to determine the technique/s that can be best implemented to ensure the interference cancellation at the receiver end.
CHAPTER 4: METHODOLOGY (INVESTIGATIONAL PROCESS)4.1 Scheme of the Investigation
The investigation of the operation on the current networks is done on the TDMA, FDMA, and CDMA. The three techniques are reviewed to determine their role in the self-interference or the co-channel interferenceCITATION Sai132 p 773 l 1033 (A, Saito and Kishiyama 773). A existing technique will be used as the coding and modulation of NOMA to ensure that the interference from cell to cell rates are cancelled as predicted by the theory and can be actualized in the real world. The coding and modulation is achieved by the pulse amplitude modulation technique. The technique uses the PAM together with gray labeling and turbo codes as applied to NOMA. The MATLAB Simulink R2017a software is used as the simulation implementation tool.
To perform the investigation, MATLAB Simulink will be use to perform tests and create models:
An existing technique is used as the coding and modulation of NOMA to ensure that the interference from cell to cell rates are cancelled as predicted by the theory and can be actualized in the real world.
Addition of imperfections to the model which may allow the designer to test the system for any noise, disturbances, or interference. The imperfections, in this case, ought to be adjustable and easy to disable.
Select the two major techniques such as SIC and PIC and compare their performance and complexity.

Select the best IC techniques and create a Module.

Interference cancellation is achieved by using an adaptive filter. The LMS algorithm is applied in this case and it works by minimizing the mean square error of the output signal. The adaptive weights are obtained by minimizing the mean squared error between the received signal and its estimate through a least mean square (LMS) algorithm. It is shown that the adaptive multi-stage PIC receiver achieves smaller BER than the matched filter receiver.

Use the simulation implementation tool; MATLAB Simulink R2017a software and conclude.
Goal of the InvestigationThis research seeks to analyze the available interference cancellation techniques and come up with the most appropriate technique that can be applied. These techniques can use this information along with channel estimates to cancel received interference from the received signal. The research seeks to find a solution to the interference and ensure the cancellation of the same without affecting the system performance. Another key focus is to increase on the throughput enhancement by performing interference cancellation.
Importance of the InvestigationIt is important to perform the given investigation since; researchers have been looking for ways to minimize interference in the power domain by testing the power used at transmission. The investigation aims at arriving at a solution that yields interference cancellation without degrading the cellular network performance CITATION SuX16 p 7 l 1033 (Su, Yu and Kim 7). The investigation results will enable the researchers to make better recommendations to the RFC for the 5G design and LTE network improvements.
Assumptions of the InvestigationIt is assumed that there is constant flow of information in the NOMA systems. The redundant information can be used in the cooperative communication or transmission.
The assumption is that there are multi-users in the given adjacent cells hence interference is bound to occur.

CHAPTER 5: SIMULATION5.1 Simulation Procedure
In this study, simulations will be done using MATLAB Simulink simulator. In order for the simulation process to be successful, all simulation properties should be established. The simulations performed in the paper will have one main parameter. The reason behind this is to ensure that the results obtained are easy to understand and form conclusions regarding the study. The goal of this study is to compare IC techniques as far as suppression of interference in NOMA cellular networks is concerned. The main parameter in this study will be time interval when a cell station instructs user terminals to quit transmitting signals so that the only signals that will be received will be interfering signal and noise.
5.2 Goal of the Simulation
The goal of the simulations will be to assess whether adaptive filters used by SIC and PIC are able to update their coefficients in order to suppress interference. One of the key variables that will be used in the simulations will be the error. The formula below will be used to calculate error in the simulation:
(en=dn-WnTxn)Usually, the goal of every technique is to place the above defined error to zero. However, there are inefficiencies in every technique that prevents the value of the error from being zero. There inefficiencies are what cause the differences that exist between these techniques from achieving the same results. The nature of adaptive algorithm for every technique is what causes the differences in performance for the techniques. The magnitude of error will be established and the main simulation will be based on relative power against taps number. Another key variable in the simulation will be interference to noise ratio calculated as follows:
Interference-to-noise ratio=INMain dB5.3 Interference Cancellation Techniques
There are two main IC techniques that will be considered in the simulation. One is the conventional hard successive interference cancellation (SIC) and the second, is a soft-in soft-out parallel interference cancellation (SISO-PIC). Different simulations will be made for the two techniques in a common. The reason for this is to ensure that all techniques are assessed under same condition in order to ensure that the results obtained are free from bias.

5.3.1 Comparison of the Interference Cancellation Schemes.

Schemes Error Propagation User Delay Complexity
SIC Yes High Low
SISO-PIC No Low High
Table 5.1: Comparison of Interference Cancellation Schemes.

One issue that may be concerned for the application of SIC to practical wireless system is “user delay” since user-k’s decoding should wait for the decoding of users 1, …, k ? 1. User delay becomes more serious when the user number is large. Another problem is that SIC suffers serious error propagation. In practical communication, there is always a probability that decoding error occurs. Since each user performs a full decoding as a one-off, hard decision errors of user k permanently exists as residual noise in the subsequent decoding for users k, k + 1, …, K. The decoding errors are accumulated and propagated as the decoding proceeds, so the problem becomes more significant when the user number is large.
CHAPTER 6: SCHEDULINGThe work to be done in this research is as detailed in the work breakdown structure below,
Activities Milestones
Research on NOMA 12th March 2018
Interference Cancellation techniques 21st April 2018
Research Proposal Write-up 29th April 2018
MATLAB Simulation 30th June 2018
Successive interference cancellation: Superposition coding and decoding 10th July 2018
Research Project completion ; Documentation 30th July 2018
Review and correction 10th August 2018
Presentation 30th August 2018
Table 6.1: Research Schedule
CHAPTER 7: CONCLUSIONIn a nutshell, the proposal demonstrates the implementation of NOMA as the solution to the throughput performance flaws of the former systems. The interference cancellation is accomplished using an interference cancellation block in the cellular system networkCITATION Wan15 p 3 l 1033 (Wang, Wang and Lu 3). The cancellation is achieved by implementing an adaptive filter which can be a low pass filter or a band pass filter depending on the measure of the noise signal CITATION Vis05 l 1033 (Viswanath and Tse).
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