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    Two-Hop AF MIMO Relay System Optimization With Own Information From the Relay Node

    74192.pdf (353.6Kb)
    Access Status
    Open access
    Authors
    Su, Q.
    Rong, Yue
    Date
    2019
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Su, Q. and Rong, Y. 2019. Two-Hop AF MIMO Relay System Optimization With Own Information From the Relay Node. IEEE Transactions on Signal Processing. 67 (4): pp. 930-945.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2018.2885497
    ISSN
    1053-587X
    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/20.500.11937/73907
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we consider precoding matrices optimization for a new two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system, where in addition to forwarding the source signals, the relay node concurrently transmits its own signals to the destination node. Compared with conventional AF MIMO relay systems where the relay node only forwards the source signals, the transceiver optimization problem in the new system is more challenging to solve. We prove that for all Schur-concave objective functions, the optimal source and relay matrices jointly diagonalize the source-relay-destination and relay-destination channels, which simplifies the matrices optimization problem to a joint subchannel and power allocation problem with scalar variables. It is shown that to achieve a maximal sum mutual information (MI) of both the source and relay links, the strongest subchannels of the second-hop channel should be allocated for transmitting signals from the relay node. With additional quality-of-service constraints in terms of the lower bounds of the MI of both links, the optimal subchannel allocation problem is NP-hard. In this case, we propose a suboptimal channel allocation algorithm with a low computational complexity. For a given subchannel allocation, we develop a primal decomposition based algorithm to efficiently solve the power allocation problem. Simulation results show that compared with the exhaustive search based channel allocation approach and the general nonlinear programming based power allocation algorithm, the proposed subchannel and power allocation algorithms have a much lower computational complexity with only a small performance loss.

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