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Title of Thesis

Symbol Detection Techniques in a Spatial Multiplexing System

Author(s)

Adnan Ahmed Khan

Institute/University/Department Details
Department Of Electrical And Computer Engineering, Centre For Advanced Studies In Engineering / University Of Engineering And Technology, Taxila
Session
2009
Subject
Electrical and Computer Engineering
Number of Pages
171
Keywords (Extracted from title, table of contents and abstract of thesis)
System, Nonlinear, Achievable, Spatial, Optimization, Symbol, Communication, Method, Complexity, Multiplexing, Techniques, Detection

Abstract
Significant performance gains are achievable in wireless communication systems using aMulti-Input Multi-Output (MIMO) communications system employing multiple antennas. This architecture is suitable for higher data rate multimedia communications. One of the challenges in building a MIMO system is the tremendous processing power required at the receiver side. MIMO Symbol detection involves detecting symbol from a complex signal at the receiver. The existing MIMO detection techniques can be broadly divided into linear, non-linear and exact detection methods. Linear methods like Zero-Forcing offer low complexity with degraded Bit Error Rate (BER) performance as compared to non-linear methods like VBLAST. Non-linear detectors are computationaly not very expansive with acceptable performance. Exact solutions like Sphere Decoder provide optimal performance however it suffers from exponentional complexity under certain conditions. The focus in the early part of this thesis is on non-linear approximate MIMO detectors and an effort has been made to develop a low complexity near-optimal MIMO detector. Computational Swarm Intelligence based Meta-heuristics are applied for Symbol detection in a MIMO system. This approach is particularly attractive as Swarm Intelligence (SI) is well suited for physically realizable, real-time applications, where low complexity and fast convergence is of absolute importance. Application of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms is studied. While an optimal Maximum Likelihood (ML) detection using an exhaustive search method is prohibitively complex, we show that the Swarm Intelligence optimized MIMO detection algorithms gives near-optimal Bit Error Rate (BER) performance  fewer iterations, thereby reducing the ML computational complexity significantly. In the thesis novel non-conventional MIMO detection approaches based on Swarm-Intelligence  techniques have been presented.
An effective and practical way to enhance the capacity of MIMO wireless channels is to employ space-time (ST) coding. Space-time block coding (STBC) is a transmit diversity technique in which the data stream to be transmitted is encoded in blocks, which are distributed among multiple antennas and across time. Alamouti’s simple STBC scheme for wireless communication systems uses two transmit antennas and linear maximum-likelihood (ML) decoder. This system was generalized by Tarokh to an arbitrary number of transmit antennas by applying the theory of orthogonal designs. In the later part of this thesis a simple multi-step constellation reduction technique based decoding algorithm that further simplifies the linear ML detection in Orthogonal Space-Time Block Coded systems is proposed This approach reduces the computational complexity of these schemes while presenting the ML performance.In addition, Spatial Multiplexing systems using Orthogonal Walsh codes are also studied. This approach has a potential to reduce the search space to allow efficient symbols detection in Spatial Multiplexing systems.

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1,394 KB
S.No. Chapter Title of the Chapters Page Size (KB)
1 0 CONTENTS

 

viii
65 KB
2

1

INTRODUCTION

1.1 Challenges in uncoded Multi-Antenna Systems
1.2 Approach to Optimize MIMO Detection
1.3 Exact Solution in a Coded Multi-Antenna System
1.4 Orthogonal Coded Spatial Multiplexing System
1.5 Organization of Thesis

1
30 KB
3 2 MULTIPATH FADING CHANNELS, MODULATION TECHNIQUES AND ANTENNA DIVERSITY

2.1 Introduction
2.2 Channel Characterizations
2.3 Modulation Techniques
2.4 Diversity
2.5 Summary

8
305 KB
4 3 MULTIPLE ANTENNAS COMMUNICATION SYSTEMS

3.1 Introduction
3.2 Narrowband MIMO
3.3 MIMO Channel Decomposition
3.4 MIMO Channel Capacity
3.5 Space-Time Coding
3.6 Frequency-Selective MIMO Channels
3.7 Summary

37
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5

4

SYMBOL DETECTION IN MIMO SYSTEM

4.1 Introduction
4.2 MIMO System Model
4.3 MIMO Detection Problem Formulation
4.4 Existing MIMO Detection Algorithms
4.5 Summary

57
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6

5

META-HEURISTIC TECHNIQUES

5.1 Introduction
5.2 Meta-heuristics and heuristics
5.3 Natural Optimization by Ants
5.4 Ant Colony Optimization Algorithm
5.5 Binary Ant System (BAS)
5.6 Natural Optimization by Swarm
5.7 Particle Swarm Optimization Algorithm
5.8 Summary

69
126 KB
7

6

SWARM INTELLIGENCE META-HEURISTICS FOR SYMBOL DETECTION IN MIMO SYSTEM

6.1 Introduction
6.2 BA-MIMO Detection Algorithm
6.3 Performance analysis of BA-MIMO Detection
6.4 Computational Complexity Comparison
6.5 Performance-Complexity trade-off
6.6 Discussion
6.7 PSO-MIMO Detection Algorithm
6.8 SPSO-MIMO Detection Algorithm
6.9 MPSO-MIMO Detection Algorithm
6.10 BPSO-MIMO Detection Algorithm
6.11 PSO Parameter Control
6.12 PSO-MIMO Detection Algorithm’s Relationship
6.13 Simulation Results and Performance Analysis
6.14 Fitness Landscape Analysis of MIMO Detection Problem
6.15 Conclusions

81
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8

7

SYMBOL DETECTION IN CODED MULTI-ANTENNA SYSTEMS

7.1 Introduction
7.2 Space-Time Block Codes
7.3 Space-Time Block Codes with time-selective channels
7.4 Conclusion

106
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9

8

ORTHOGONAL CODED MIMO SYSTEMS

8.1 Introduction
8.2 System Model
8.3 Detection in the proposed system
8.4 Performance Results
8.5 Summary

126
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10

9

CONCLUSIONS

9.1 Contribution to Knowledge
9.2 Future Work

134
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11

10

REFERENCES

 

140
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