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    Superimposed channel training for MIMO relay systems

    187189_187189.pdf (125.5Kb)
    Access Status
    Open access
    Authors
    Rong, Yue
    Date
    2012
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Rong, Yue. 2012. Superimposed channel training for MIMO relay systems, in Jamalipour, Abbas (ed), The 23rd IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Sep 9-12 2012, pp. 2324-2328. Sydney, Australia: IEEE.
    Source Title
    Proceedings of the 23rd IEEE International Symposium on Personal, Indoor and Mobile Communications
    Source Conference
    The 23rd IEEE International Symposium on Personal, Indoor and Mobile Communications
    DOI
    10.1109/PIMRC.2012.6362744
    Remarks

    © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/42354
    Collection
    • Curtin Research Publications
    Abstract

    Based on the knowledge of instantaneous channel state information (CSI), the optimal source and relay pre-coding matrices have been developed recently for multiple-input multiple-output (MIMO) relay communication systems. However, in real communication systems, the instantaneous CSI is unknown and needs to be estimated at the destination node. In this paper, we propose a superimposed channel training method for MIMO relay communication systems. It is shown that to minimize the mean-squared error (MSE) of channel estimation, the optimal training sequence at each node matches the eigenvector matrix of the transmitter correlation matrix of the forward MIMO channel. Then we optimize the power allocation among different streams of the training sequence at the source node and the relay node. Simulation results show that the proposed algorithm leads to a smaller MSE of channel estimation compared with the conventional MIMO relay channel estimation algorithm.

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