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Near-optimal MIMO multiuser detection using hybrid immune clonal selection algorithm

  • Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We propose in this paper an hybrid immune clonal selection algorithm (ICSA) to approach near-optimal performance for multiple-input multiple-output (MIMO) multiuser detection. The ICSA which guide the heuristic search by imitating the evolutionary mechanism of antibodies, is shown to approach the performance of maximum-likelihood (ML) detector. The Hybrid ICSA multiuser detection (MUD) approach, which introduce embedded Hopfield neural networks (HNN) to accelerate the search convergence and improve local search capability, is further proposed. The simulation results show that it is feasible to achieve near-optimal bit-error-rate (BER) performance with a lower complexity using the proposed algorithm.

Original languageEnglish
Title of host publication3rd International Conference on Communications and Networking in China, ChinaCom 2008
Pages983-986
Number of pages4
DOIs
Publication statusPublished - 2008
Event3rd International Conference on Communications and Networking in China, ChinaCom 2008 - Hangzhou, China
Duration: 25 Aug 2008 → 27 Aug 2008

Publication series

Name3rd International Conference on Communications and Networking in China, ChinaCom 2008

Conference

Conference3rd International Conference on Communications and Networking in China, ChinaCom 2008
Country/TerritoryChina
CityHangzhou
Period25/08/08 → 27/08/08

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