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    MSE based transceiver designs for bi-directional full-duplex MIMO systems

    202681_202681.pdf (222.1Kb)
    Access Status
    Open access
    Authors
    Cirik, A.
    Wang, R.
    Rong, Yue
    Hua, Y.
    Date
    2014
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Cirik, A. and Wang, R. and Rong, Y. and Hua, Y. 2014. MSE based transceiver designs for bi-directional full-duplex MIMO systems, in A. Sayeed (ed), The 15th IEEE Workshop on Signal Processing Advances in Wireless Communications, Jun 22-25 2014. Toronto, Canada: IEEE.
    Source Title
    The procceedings of the 15th IEEE Workshop on Signal Processing Advances in Wireless Communications
    Source Conference
    The 15th IEEE Workshop on Signal Processing Advances in Wireless Communications
    DOI
    10.1109/SPAWC.2014.6941786
    School
    Department of Electrical and Computer Engineering
    Remarks

    Copyright © 2014 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/20.500.11937/37243
    Collection
    • Curtin Research Publications
    Abstract

    We consider a multiple antenna full-duplex (FD) bidirectional (point-to-point) communication system with a limited analog domain self-interference cancellation capability. The effect of the residual self-interference resulting from independent and identically distributed (i.i.d.) channel estimation errors and limited dynamic ranges of the transmitters and receivers is studied in the digital domain. We design transceiver matrices based on the minimization of sum mean-squared error (MSE) and the maximum per-node MSE optimization problems subject to individual power constraints at each node through an iterative alternating algorithm, which is proven to converge to at least a local optimal solution.

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