Artificial Bee Colony and Tabu Search Enhanced TTCM Assisted MMSE Multi-User Detectors for Rank Deficient SDMA-OFDM System

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• 作者：P. A. Haris (1) haris@nitc.ac.in
  E. Gopinathan (1) gopie@nitc.ac.in
  C. K. Ali (1) cka@nitc.ac.in
• 关键词：Artificial bee colony (ABC) &#8211 ; Genetic algorithm (GA) &#8211 ; Tabu search (TS) &#8211 ; Multiple ; input multiple ; output (MIMO) &#8211 ; Multiuser detection/detector (MUD) &#8211 ; Orthogonal frequency division multiplexing (OFDM) &#8211 ; Space division multiple access (SDMA)
• 刊名：Wireless Personal Communications
• 出版年：2012
• 出版时间：July 2012
• 年：2012
• 卷：65
• 期：2
• 页码：425-442
• 全文大小：501.4 KB
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• 作者单位：1. Department of Electronics and Communication Engineering, National Institute of Technology, Calicut, Kerala, India
• 刊物类别：Engineering
• 刊物主题：Electronic and Computer Engineering
  Signal,Image and Speech Processing
  Processor Architectures
  
• 出版者：Springer Netherlands
• ISSN：1572-834X

文摘

In this paper, we propose two novel and computationally efficient metaheuristic algorithms based on Artificial Bee Colony (ABC) and Tabu Search (TS) principles for Multi User Detection (MUD) in Turbo Trellis Coded Modulation based Space Division Multiple Access Orthogonal Frequency Division Multiplexing system. Unlike gradient descent methods, both ABC and TS methods ensure minimization of the objective function without the solution being trapped into local optima. These techniques are capable of achieving excellent performance in the so called overloaded system, where the number of transmit antennas is higher than the number of receiver antennas, in which the known classic MUDs fail. The performance of the proposed algorithms are compared with each other and also against Genetic Algorithm (GA) and K-Best sperical decoding algorithm based MUD. Simulation results establish better performance, computational efficiency and convergence characteristics for ABC and TS methods. It is seen that the proposed detectors achieve similar performance to that of well known optimum Maximum Likelihood Detector (MLD) at a significantly lower computational complexity and outperforms the traditional MMSE MUD.