Low-complexity approximate iterative LMMSE detection for large-scale MIMO systems

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• 作者：Xiaochen HeAuthor Vitae ; Qinghua Guo ; ^{qguo@uow.edu.au" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jun TongAuthor Vitae ; Jiangtao XiAuthor Vitae ; Yanguang YuAuthor Vitae
• 关键词：Large-scale MIMO ; Linear minimum mean squared error (LMMSE) ; Singular value decomposition (SVD) ; Bi-diagonalization ; Iterative detection and decoding
• 刊名：Digital Signal Processing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：60
• 期：Complete
• 页码：134-139
• 全文大小：916 K

文摘

This paper deals with iterative detection for uplink large-scale MIMO systems. The well-known iterative linear minimum mean squared error (LMMSE) detector requires quadratic complexity (per symbol per iteration) with the number of antennas, which may be a concern in large-scale MIMO. In this work, we develop approximate iterative LMMSE detectors based on transformed system models where the transformation matrices are obtained through channel matrix decompositions. It is shown that, with quasi-linear complexity (per symbol per iteration), the proposed detectors can achieve almost the same performance as the conventional LMMSE detector. It is worth mentioning that the linear transformations are also useful to reduce the complexity of downlink precoding, so the relevant computational complexity can be shared by both uplink and downlink.