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    Analysis of the RLMS Adaptive Beamforming Algorithm Implemented with Finite Precision

    155589_155589.pdf (369.1Kb)
    Access Status
    Open access
    Authors
    Srar, Jalal Abdulsayed
    Chung, Kah-Seng
    Mansour, Ali
    Date
    2010
    Type
    Conference Paper
    
    Metadata
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    Citation
    Srar, Jalal Abdulsayed and Chung, Kah-Seng and Mansour, Ali. 2010. Analysis of the RLMS Adaptive Beamforming Algorithm Implemented with Finite Precision, The 16th Asia Pacific Conference on Communications APCC 2010, Oct 31 2010, pp. 258-263. Auckland, New Zealand: IEEE.
    Source Title
    Proceedings of the 16th Asia Pacific Conference on Communications
    Source Conference
    The 16th Asia Pacific Conference on Communications APCC 2010
    ISBN
    978-1-4244-8127-9
    School
    Department of Electrical and Computer Engineering
    Remarks

    Copyright © 2010 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/13115
    Collection
    • Curtin Research Publications
    Abstract

    This paper studies the influence of the use of finite wordlength on the operation of the RLMS adaptive beamformingalgorithm. The convergence behavior of RLMS, based on the minimum mean square error (MSE), is analyzed for operation with finite precision. Computer simulation results verify that a wordlength of nine bits is sufficient for the RLMS algorithm to achieve performance close to that provided by full precision. The performance measures used include residual MSE, rate of convergence, error vector magnitude (EVM), and beam pattern. Based on all these measures, it is shown that the RLMS algorithm outperforms other earlier algorithms, such as least mean square (LMS), recursive least square (RLS), modified robust variable step size (MRVSS) and constrained stability LMS (CSLMS).

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