GNN-Enhanced Approximate Message Passing for Massive/Ultra-Massive MIMO Detection

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

2 Scopus Citations
View graph of relations

Author(s)

  • Hengtao He
  • Alva Kosasih
  • Jun Zhang
  • S. H. Song
  • Wibowo Hardjawana
  • Khaled B. Letaief

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publication2023 IEEE Wireless Communications and Networking Conference (WCNC)
Subtitle of host publicationProceedings
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Number of pages6
ISBN (electronic)9781665491228
ISBN (print)978-1-6654-9123-5
Publication statusPublished - 2023

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
ISSN (Print)1525-3511
ISSN (electronic)1558-2612

Conference

Title2023 IEEE Wireless Communications and Networking Conference (WCNC 2023)
PlaceUnited Kingdom
CityGlasgow
Period26 - 29 March 2023

Abstract

Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput and ultra-low latency requirements in 5G and beyond communications, given the extremely large number of antennas. In this paper, we propose a low complexity graph neural network (GNN) enhanced approximate message passing (AMP) algorithm, AMP-GNN, for massive/ultra-massive MIMO detection. The structure of the neural network is customized by unfolding the AMP algorithm and introducing the GNN module for multiuser interference cancellation. Numerical results will show that the proposed AMP-GNN significantly improves the performance of the AMP detector and achieves comparable performance as the state-of-the-art deep learning-based MIMO detectors but with reduced computational complexity. Furthermore, it presents strong robustness to the change of the number of users. © 2023 IEEE.

Citation Format(s)

GNN-Enhanced Approximate Message Passing for Massive/Ultra-Massive MIMO Detection. / He, Hengtao; Kosasih, Alva; Yu, Xianghao et al.
2023 IEEE Wireless Communications and Networking Conference (WCNC): Proceedings. Institute of Electrical and Electronics Engineers, Inc., 2023. (IEEE Wireless Communications and Networking Conference, WCNC).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review