Development of a Pilot Allocation Protocol to Mitigate the Effect of Pilot Contamination in Massive Multiple Input Multiple Output System.
ABSTRACT
Pilot Contamination (PC) has been considered as a major limiting factor of Time Division Duplexing (TDD) Massive Multiple-Input Multiple-Output (M-MIMO) systems, as it saturates the signal to interference plus noise ratio (SINR) saturated.
Similarly, several mitigation techniques such as Eigenvalue decomposition, cooperative Bayesian channel estimation, blind equalization technique, time staggering pilot, smart pilot assignment, etc. proposed in the literature to address PC issue have limitations. These proposed methods are either based on initializing all pilot sequences allocated for training as requested by users or require coordination among neighboring cells.
Either process is found to be difficult as synchronizing different levels of the network (micro, nano, and pico-cells) and possibly many cells is not only difficult but a huge task of high computational complexity that requires coordination among neighboring cells. Therefore, this research work developed a Pilot Allocation Protocol (PAP) decontamination technique.
The developed scheme was based on sectional pilot sequence initialization and sharing which allocate more than one user to share the same orthogonal pilot sequence within a cell. As a result of the sharing, intra cell interference was created, which in turn suppresses inter-cell interference between users sharing the same pilot sequence and mitigates the effect of PC.
The simulation was carried out using MATLAB 2016Rb and the performance of the developed PAP technique was then compared with conventional. Soft Pilot Reuse and Multi-Cell Block Diagonalization (SPR and MBD) schemes utilizing average Uplink (UL) and Downlink (DL) cell throughputs, and Bit Error Rate (BER) as performance metrics.
Results obtained showed that the PAP technique had a better average UL and DL cell throughputs over the conventional. SPR and MBD schemes with an improvement of 2 b/s/Hz and 3b/s/Hz when the number of BS antennas considered was set at 64, 20.5 b/s/Hz for uplink, and 20.3 b/s/Hz for downlink when the number of BS antennas was set at 250.
20.2 b/s/Hz for uplink and 19 b/s/Hz for downlink when the number of BS antennas was setat 256 when Zero Forcing(ZF) detector/precoder was adopted while when Matched Filter(MF) detector/precoder was used.
The developed PAP technique had an average uplink and downlink cell throughputs improvement of 1.5 b/s/Hz for both UL and DL when the number of BS antennas considered were set at 64, 8.1 b/s/Hz for both UL and DL at 250 BS antennas and 8.1 b/s/Hz for both UL and DL when the number of BS antennas was set at 256 BS.
TABLE OF CONTENT
CHAPTER ONE: INTRODUCTION
1.1 BACKGROUND OF RESEARCH 1
1.2 PROBLEM STATEMENT 2
1.3 SIGNIFICANCE OF RESEARCH 3
1.4 AIM AND OBJECTIVE 3
1.5SCOPE OF RESEARCH 4
CHAPTER TWO: LITERATURE REVIEW
2.1 INTRODUCTION 5
2.2 REVIEW OF FUNDAMENTAL CONCEPTS 5
2.2.1 Multiple Input Multiple Output (MIMO) 5
2.2.1.1 Limitations of MIMO 7
2.2.2 Massive MIMO 8
2.2.2.1 Benefits of M-MIMO 8
2.2.2.2 Challenges of M-MIMO 11
2.2.3 Pilot Contamination 12
2.2.3.1 Sources of Pilot Contamination 12
2.2.4 Pilot Decontamination Techniques 15
2.2.4.1 Pilot Sequence Reuse and Power Allocation Techniques 17
2.2.5 Pilot Allocation Protocol (PAP) Technique 22
2.2.6 Pilot Information Recovery (User Separation) 24
2.2.6.1 Uplink Transmission 27
2.2.6.2 Downlink Transmission 28
2.2.6.3 Linear processing schemes 29
2.2.6.4 Linear Receivers (in the UPLINK) 29
2.2.6.5 Maximum Ratio Combining Receiver 30
2.2.6.6 Zero Forcing 32
2.2.6.7 Minimum Mean Square Error 32
2.2.6.8 Linear Precoder (in the DOWNLINK) 32
2.2.7 Comparison of Wireless Channel Model 34
2.2.8Channel Modelling in Time Division Duplex 27
2.2.8.1 Uplink Transmission 28
2.2.8.2 Downlink Transmission 28
2.2.8.3Linear Processing Schemes 28
2.2.8.4 Linear Receivers (in the UPLINK) 29
2.2.8.5 Linear Precoders (in the DOWNLINK) 29
2.2.8.6 Zero Forcing 30
2.2.9 Review of Similar Works 31
CHAPTER THREE: MATERIALS AND METHODS
3.1INTRODUCTION 39
xiii
3.2 MATERIALS USED FOR THE RESEARCH WORK 39
3.3 METHODOLOGY 39
3.4 DEVELOPMENT OF PILOT ALLOCATION PROTOCOL (PAP)
CHANNEL MODEL 40
3.4.1 Channel Model 44
3.4.2 Base Parameters (Initialization) 44
3.4.3 User Equipment Parameters (Initialization) 45
3.5ENHANSEMENT OF UPLINK (UL) AND DOWNLINK (DL) AVERAGE CLL
THROUGHPUTS 46 3.5.2 Signal Detection 48
3.5.3 UL SINR and Average UL Cell Throughput Determination 49
3.5.4 DL SINR and DL Average Throughput Determination 49
3.6 PERFORMANCE METRICS 50
3.6.1 Uplink (UL) and Downlink (DL) throughput 50
3.6.2 Bit Error Rate 50
CHAPTER FOUR: RESULTS AND DISCUSSION
4.1 INTRODUCTION 52
4.2 ENHANCED UL AND DL THROUPUTS BASED ON THE DEVELOPED
PILOT ALLOCATION PROTOCOL 52
4.3 RESULTS AND DISCUSSION 53
xiv
4.4 SUMMARY OF RESULTS 60
4.5 VALIDATION 62
CHAPTER FIVE: CONCLUSION ANDRECOMMENDATION
5.1 INTRODUCTION 64
5.2CONCLUSION 64
5.3 SIGNIFICANT CONTRIBUTION 65
5.4 RECOMMENDATIONS 66
REFERENCES
INTRODUCTION
1.1 BACKGROUND OF RESEARCH
In today’s world, the use of mobile devices is increasing exponentially and is not limited to just sending and receiving calls. Next-generation communication systems, also known as fifth-generation (5G) networks, have been recognized as having the prospect of providing solutions to the issue of capacity constraints of today’s networks (Jose et al., 2009).
It can also address the problems associated with existing communication systems, concerning their reliability for multimedia applications, full coverage distance area, link reliability, latency, and energy efficiency (Larsson et al., 2014).
Higher channel counts Multiple-Input Multiple-Output (MIMO) also known as Massive MIMO (M-MIMO) technology is in an advantageous position to deal with these constraints (Vardhan et al., 2016a).
M-MIMO system has been identified to have the potential to cater to the high capacity demand, for wireless mobile communication networks in 2020 and beyond (Benmimoune et al., 2015). In spite of the prospects of the M-MIMO technology, it has some key challenges such as (Tebe et al., 2015):
REFERENCES
Benmimoune, M., Driouch, E., Ajib, W., & Massicotte, D. (2015). Feedback Energy Reduction in Massive MIMO Systems. Paper presented at the Global Communications Conference (GLOBECOM), 2015 IEEE, San Diego, CA, USA.
Elijah, O., Leow, C. Y., Rahman, T. A., Nunoo, S., & Iliya, S. Z. (2016). A comprehensive survey of pilot contamination in massive MIMO—5G system. IEEE Communications Surveys & Tutorials, 18(2), 905-923.
Farhang, A., Aminjavaheri, A., Marchetti, N., Doyle, L. E., & Farhang-Boroujeny, B. (2014). Pilot Decontamination in CMT-based Massive MIMO Networks. Paper presented at the 11th International Symposium on Wireless Communications Systems (ISWCS), 2014 Barcelona, Spain.
Frenger, P., Olsson, M., & Eriksson, E. (2014). Radio network energy performance of massive MIMO beamforming systems. Paper presented at the IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), 2014 Washington, DC, USA.
Guo, X., Zhang, J., Chen, S., Mu, X., & Hanzo, L. (2017). Two-Stage Time-Domain Pilot Contamination Elimination in Large-Scale Multiple-Antenna Aided and TDD Based OFDM Systems. IEEE Access.
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