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    Joint Transceiver Optimization for Wireless Information and Energy Transfer in Non-Regenerative MIMO Relay Systems

    69964.pdf (336.6Kb)
    Access Status
    Open access
    Authors
    Li, B.
    Rong, Yue
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Li, B. and Rong, Y. 2018. Joint Transceiver Optimization for Wireless Information and Energy Transfer in Non-Regenerative MIMO Relay Systems. IEEE Transactions on Vehicular Technology. 67 (9): pp. 8348-8362.
    Source Title
    IEEE Transactions on Vehicular Technology
    DOI
    10.1109/TVT.2018.2846556
    ISSN
    0018-9545
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP140102131
    URI
    http://hdl.handle.net/20.500.11937/69752
    Collection
    • Curtin Research Publications
    Abstract

    IEEE In this paper, a two-hop non-regenerative multiple-input multiple-output (MIMO) relay system is investigated, where the relay node relies on harvesting the radio frequency energy transferred from the source node to forward information from source to destination. We consider the time switching (TS) protocol between wireless information and energy transfer. In particular, we propose a more general energy consumption constraint at the source node during the information and energy transfer, which includes the constant power constraints used in existing works as special cases. We study the joint optimization of the source precoding matrices, the relay amplifying matrix, and the TS factor to maximize the source-destination mutual information (MI). The optimal structure of the source and relay matrices is derived, which reduces the original transceiver optimization problem to a simpler power allocation problem. We propose a primal decomposition based algorithm and an upper bound based approach to efficiently solve the power allocation problem. The first algorithm achieves the global optimum, whereas the latter one has a lower computational complexity. Numerical simulations show that both proposed algorithms yield much higher system MI and better rate-energy tradeoff than existing approaches.

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