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Particle Swarm Optimisation Aided MIMO Transceiver Designs

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Computational Intelligence in Expensive Optimization Problems

Part of the book series: Adaptation Learning and Optimization ((ALO,volume 2))

Abstract

Multiple-input multiple-output (MIMO) technologies are capable of substantially improving the achievable system’s capacity, coverage and/or quality of service. The system’s ability to approach the MIMO capacity depends heavily on the designs of MIMO receiver and/or transmitter, which are generally expensive optimisation tasks. Hence, researchers and engineers have endeavoured to develop efficient optimisation techniques that can solve practical MIMO designs with affordable costs. In this contribution, we demonstrate that particle swarm optimisation (PSO) offers an efficient means for aiding MIMO transceiver designs. Specifically, we consider PSO-aided semi-blind joint maximum likelihood channel estimation and data detection for MIMO receiver, and we investigate PSO-based minimum bit-error-rate multiuser transmission for MIMO systems. In both these two MIMO applications, the PSO-aided approach attains an optimal design solution with a significantly lower complexity than the existing state-of-the-art scheme.

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Author information

Authors and Affiliations

  1. School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK

    S. Chen, W. Yao, H. R. Palally & L. Hanzo

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  1. S. Chen
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  2. W. Yao
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  3. H. R. Palally
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  4. L. Hanzo
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Editors and Affiliations

  1. Department of Mechanical Engineering and Science-Faculty of Engineering, Kyoto University, Yoshida-honmachi, Sakyo-Ku,, 606-8501, Kyoto, Japan

    Yoel Tenne

  2. Advanced Technology Centre, Rolls-Royce Singapore Pte Ltd, 50 Nanyang Avenue, Block N2, Level B3C,Unit 05-08, 639798, Singapore

    Chi-Keong Goh

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Chen, S., Yao, W., Palally, H.R., Hanzo, L. (2010). Particle Swarm Optimisation Aided MIMO Transceiver Designs. In: Tenne, Y., Goh, CK. (eds) Computational Intelligence in Expensive Optimization Problems. Adaptation Learning and Optimization, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10701-6_19

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  • DOI: https://doi.org/10.1007/978-3-642-10701-6_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10700-9

  • Online ISBN: 978-3-642-10701-6

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