Vol.70, No.1, 2022, pp.491-504, doi:10.32604/cmc.2022.019397
OPEN ACCESS
ARTICLE
Efficient Gauss-Seidel Precoding with Parallel Calculation in Massive MIMO Systems
  • Hyun-Sun Hwang1, Jae-Hyun Ro2, Chan-Yeob Park1, Young-Hwan You3, Hyoung-Kyu Song1,*
1 Department of Information and Communication Engineering, Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
2 Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
3 Department of Computer Engineering, Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
* Corresponding Author: Hyoung-Kyu Song. Email:
Received 12 April 2021; Accepted 18 May 2021; Issue published 07 September 2021
Abstract
A number of requirements for 5G mobile communication are satisfied by adopting multiple input multiple output (MIMO) systems. The inter user interference (IUI) which is an inevitable problem in MIMO systems becomes controllable when the precoding scheme is used. In this paper, the horizontal Gauss-Seidel (HGS) method is proposed as precoding scheme in massive MIMO systems. In massive MIMO systems, the exact inversion of channel matrix is impractical due to the severe computational complexity. Therefore, the conventional Gauss-Seidel (GS) method is used to approximate the inversion of channel matrix. The GS has good performance by using previous calculation results as feedback. However, the required time for obtaining the precoding symbols is too long due to the sequential process of GS. Therefore, the HGS with parallel calculation is proposed in this paper to reduce the required time. The rows of channel matrix are eliminated for parallel calculation in HGS method. In addition, HGS uses the ordered channel matrix to prevent performance degradation which is occurred by parallel calculation. The HGS with proper number of parallelly computed symbols has better performance and reduced required time compared to the traditional GS.
Keywords
Massive MIMO; GS; matrix inversion; linear precoding
Cite This Article
Hwang, H., Ro, J., Park, C., You, Y., Song, H. (2022). Efficient Gauss-Seidel Precoding with Parallel Calculation in Massive MIMO Systems. CMC-Computers, Materials & Continua, 70(1), 491–504.
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