Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/64586
Title: Graph Neural Network Aided MU-MIMO Detectors
Contributor(s): Kosasih, Alva (author); Onasis, Vincent (author); Miloslavskaya, Vera  (author)orcid ; Hardjawana, Wibowo (author); Andrean, Victor (author); Vucetic, Branka (author)
Publication Date: 2022-09
Early Online Version: 2022-07-18
DOI: 10.1109/JSAC.2022.3191344
Handle Link: https://hdl.handle.net/1959.11/64586
Abstract: 

Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. A base station serves many users in an uplink MU-MIMO system, leading to a substantial multi-user interference (MUI). Designing a high-performance detector for dealing with a strong MUI is challenging. This paper analyses the performance degradation caused by the posterior distribution approximation used in the state-of-the-art message passing (MP) detectors in the presence of high MUI. We develop a graph neural network based framework to fine-tune the MP detectors’ cavity distributions and thus improve the posterior distribution approximation in the MP detectors. We then propose two novel neural network based detectors which rely on the expectation propagation (EP) and Bayesian parallel interference cancellation (BPIC), referred to as the GEPNet and GPICNet detectors, respectively. The GEPNet detector maximizes detection performance, while GPICNet detector balances the performance and complexity. We provide proof of the permutation equivariance property, allowing the detectors to be trained only once, even in the systems with dynamic changes of the number of users. The simulation results show that the proposed GEPNet detector performance approaches maximum likelihood performance in various configurations and GPICNet detector doubles the multiplexing gain of BPIC detector.

Publication Type: Journal Article
Grant Details: ARC/DP210103410
ARC/FL160100032
Source of Publication: IEEE Journal on Selected Areas in Communications, 40(9), p. 2540-2555
Publisher: Institute of Electrical and Electronics Engineers
Place of Publication: United States of America
ISSN: 1558-0008
0733-8716
Fields of Research (FoR) 2020: 461301 Coding, information theory and compression
460199 Applied computing not elsewhere classified
Socio-Economic Objective (SEO) 2020: 220107 Wireless technologies, networks and services
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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