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Title: Gaussian message passing for overloaded massive MIMO-NOMA
Authors: Liu, Lei
Yuen, Chau
Guan, Yong Liang
Li, Ying
Huang, Chongwen
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Liu, L., Yuen, C., Guan, Y. L., Li, Y., & Huang, C. (2019). Gaussian message passing for overloaded massive MIMO-NOMA. IEEE Transactions on Wireless Communications, 18(1), 210-226. doi:10.1109/twc.2018.2878720
Journal: IEEE Transactions on Wireless Communications
Abstract: This paper considers a low-complexity Gaussian message passing (GMP) Multi-User Detection (MUD) scheme for a coded massive multiple-input multiple-output (MIMO) system with non-orthogonal multiple access (massive MIMO-NOMA), in which a base station with Ns antennas serves Nu sources simultaneously in the same frequency. Both Nu and Ns are large numbers, and we consider the overloaded cases with Nu>Ns. The GMP for MIMO-NOMA is a message passing algorithm operating on a fully-connected loopy factor graph, which is well understood to fail to converge due to the correlation problem. The GMP is attractive as its complexity order is only linearly dependent on the number of users, compared to the cubic complexity order of linear minimum mean square error (LMMSE) MUD. In this paper, we utilize the large-scale property of the system to simplify the convergence analysis of the GMP under the overloaded condition. We prove that the variances of the GMP definitely converge to the mean square error (MSE) of the LMMSE multi-user detection. Second, the means of the traditional GMP will fail to converge when Nu/Ns (2-1-25.83. Therefore, we propose and derive a new convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the LMMSE multi-user detection performance for any Nu/Ns>1, and show that it has a faster convergence speed than the traditional GMP with the same complexity. Finally, the numerical results are provided to verify the validity and accuracy of the theoretical results presented.
ISSN: 1536-1276
DOI: 10.1109/twc.2018.2878720
Schools: School of Electrical and Electronic Engineering 
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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