Low-Complexity SSOR-Based Precoding for Massive MIMO Systems

• Published in: IEEE communications letters Vol. 20; no. 4; pp. 744 - 747
• Main Authors: Xie, Tian, Dai, Linglong, Gao, Xinyu, Dai, Xiaoming, Zhao, Youping
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Antennas; Approximation; Asymptotic properties; Channels; Complexity; Complexity theory; Covariance matrices; Fading channels; linear precoding; Massive multiple-input multiple-output (MIMO); MIMO; MIMO (control systems); Optimization; Orthogonality; SSOR method; Symmetric matrices; linear precoding; Massive multiple-input multiple-output (MIMO); SSOR method
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2016.2525807

With the increase of the number of base station (BS) antennas in massive multiple-input multiple-output (MIMO) systems, linear precoding schemes are able to achieve the near-optimal performance, and thus are more attractive than nonlinear precoding techniques. However, conventional linear precoding schemes such as zero-forcing (ZF) precoding involve the matrix inversion of large size with high computational complexity, especially in massive MIMO systems. To reduce the complexity, in this letter, we propose a low-complexity linear precoding scheme based on the symmetric successive over relaxation (SSOR) method. Moreover, we propose a simple way to approximate the optimal relaxation parameter of the SSOR-based precoding by exploiting the channel property of asymptotical orthogonality in massive MIMO systems. We show that the proposed SSOR-based precoding can reduce the complexity of the classical ZF precoding by about one order of magnitude without performance loss, and it also outperforms the recently proposed linear approximate precoding schemes in typical fading channels.