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Multi-Antenna Communication Receivers Using Metaheuristics and Machine Learning Algorithms

Electronic Theses of Indian Institute of Science

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Title Multi-Antenna Communication Receivers Using Metaheuristics and Machine Learning Algorithms
 
Creator Nagaraja, Srinidhi
 
Subject Multi Antenna Communication Receivers
Machine Learning Algorithms
Wireless Communication Systems
Multiple-Input Multiple-Output (MIMO) Systems
Reactive Tabu Search (RTS)
Linear Regression MMSE Residual Recivers (LRR)
Metaheuristics
Tabu Search Algorithm
Multi Antenna Communication Systems
MIMO Systems
MMSE Residual
MMSE Recivers
Minimum Mean Square Error
Communication Engineering
 
Description In this thesis, our focus is on low-complexity, high-performance detection algorithms for multi-antenna communication receivers. A key contribution in this thesis is the demonstration that efficient algorithms from metaheuristics and machine learning can be gainfully adapted for signal detection in multi- antenna communication receivers. We first investigate a popular metaheuristic known as the reactive tabu search (RTS), a combinatorial optimization technique, to decode the transmitted signals in large-dimensional communication systems. A basic version of the RTS algorithm is shown to achieve near-optimal performance for 4-QAM in large dimensions. We then propose a method to obtain a lower bound on the BER performance of the optimal detector. This lower bound is tight at moderate to high SNRs and is useful in situations where the performance of optimal detector is needed for comparison, but cannot be obtained due to very high computational complexity. To improve the performance of the basic RTS algorithm for higher-order modulations, we propose variants of the basic RTS algorithm using layering and multiple explorations. These variants are shown to achieve near-optimal performance in higher-order QAM as well.
Next, we propose a new receiver called linear regression of minimum mean square error (MMSE) residual receiver (referred to as LRR receiver). The proposed LRR receiver improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel) to find the linear regression parameters. The LRR receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs well. Finally, we propose a receiver that uses a committee of linear receivers, whose parameters are estimated from training data using a variant of the AdaBoost algorithm, a celebrated supervised classification algorithm in ma- chine learning. We call our receiver boosted MMSE (B-MMSE) receiver. We demonstrate that the performance and complexity of the proposed B-MMSE receiver are quite attractive for multi-antenna communication receivers.
 
Contributor Chockalingam, A
 
Date 2018-04-23T15:53:14Z
2018-04-23T15:53:14Z
2018-04-23
2013
 
Type Thesis
 
Identifier http://etd.iisc.ernet.in/2005/3442
http://etd.iisc.ernet.in/abstracts/4309/G25963-Abs.pdf
 
Language en_US
 
Relation G25963