Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems

• URL: http://arxiv.org/abs/2302.06896v2
• Date: Tue, 31 Oct 2023 13:06:24 GMT
• Title: Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems
• Authors: Hengtao He, Xianghao Yu, Jun Zhang, Shenghui Song, Khaled B. Letaief
• Abstract summary: We propose a model-driven deep learning framework, namely the AMP-GNN for massive multiple input multiple output (MIMO) transceiver design. Specifically, the structure of the AMP-GNN network is customized by unfolding the approximate message passing (AMP) algorithm and introducing a graph neural network (GNN) module into it.
• Score: 31.86957709846756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains. When developing efficient physical layer algorithms for massive MIMO systems, message passing is one promising candidate owing to the superior performance. However, as their computational complexity increases dramatically with the problem size, the state-of-the-art message passing algorithms cannot be directly applied to future 6G systems, where an exceedingly large number of antennas are expected to be deployed. To address this issue, we propose a model-driven deep learning (DL) framework, namely the AMP-GNN for massive MIMO transceiver design, by considering the low complexity of the AMP algorithm and adaptability of GNNs. Specifically, the structure of the AMP-GNN network is customized by unfolding the approximate message passing (AMP) algorithm and introducing a graph neural network (GNN) module into it. The permutation equivariance property of AMP-GNN is proved, which enables the AMP-GNN to learn more efficiently and to adapt to different numbers of users. We also reveal the underlying reason why GNNs improve the AMP algorithm from the perspective of expectation propagation, which motivates us to amalgamate various GNNs with different message passing algorithms. In the simulation, we take the massive MIMO detection to exemplify that the proposed AMP-GNN significantly improves the performance of the AMP detector, achieves comparable performance as the state-of-the-art DL-based MIMO detectors, and presents strong robustness to various mismatches.

Related papers

• Energy-Aware FPGA Implementation of Spiking Neural Network with LIF Neurons [0.5243460995467893]
  Spiking Neural Networks (SNNs) stand out as a cutting-edge solution for TinyML. This paper presents a novel SNN architecture based on the 1st Order Leaky Integrate-and-Fire (LIF) neuron model. A hardware-friendly LIF design is also proposed, and implemented on a Xilinx Artix-7 FPGA.
  arXiv  Detail & Related papers  (2024-11-03T16:42:10Z)
• Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding [57.71603937699949]
  We study optimization guarantees, i.e., achieving near-zero training loss with the increase in the number of learning epochs. We show that the threshold on the number of training samples increases with the increase in the network width.
  arXiv  Detail & Related papers  (2023-09-12T13:03:47Z)
• Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks [101.59367762974371]
  In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments. It is difficult to train a deep neural network (DNN) with limited pilot signals, hindering its practical applications. We design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm.
  arXiv  Detail & Related papers  (2022-10-08T04:32:58Z)
• Graph Neural Network Aided MU-MIMO Detectors [31.202097630016286]
  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)
  arXiv  Detail & Related papers  (2022-06-19T11:23:02Z)
• Edge Graph Neural Networks for Massive MIMO Detection [15.970981766599035]
  Massive Multiple-Input Multiple-Out (MIMO) detection is an important problem in modern wireless communication systems. While traditional Belief Propagation (BP) detectors perform poorly on loopy graphs, the recent Graph Neural Networks (GNNs)-based method can overcome the drawbacks of BP and achieve superior performance.
  arXiv  Detail & Related papers  (2022-05-22T08:01:47Z)
• Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays [54.43962058166702]
  millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays have received great attention. In this work, we investigate the joint design of a beam precoding matrix for mmWave MU-MIMO systems with DLA.
  arXiv  Detail & Related papers  (2021-01-05T03:55:04Z)
• Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA [78.60275748518589]
  Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks. A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
  arXiv  Detail & Related papers  (2021-01-02T15:21:08Z)
• Graph Neural Networks for Massive MIMO Detection [8.516590865173407]
  We learn a message-passing solution for the inference task of massive multiple-input multiple-output (MIMO) detection in wireless communication. We adopt a graphical model based on the Markov random field (MRF) where belief propagation (BP) yields poor results when it assumes a uniform prior over the transmitted symbols.
  arXiv  Detail & Related papers  (2020-07-11T07:34:56Z)
• Policy-GNN: Aggregation Optimization for Graph Neural Networks [60.50932472042379]
  Graph neural networks (GNNs) aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors. It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. We propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process.
  arXiv  Detail & Related papers  (2020-06-26T17:03:06Z)
• Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems [59.804810122136345]
  We propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. We show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.
  arXiv  Detail & Related papers  (2020-06-15T02:57:57Z)
• A Modular Neural Network Based Deep Learning Approach for MIMO Signal Detection [12.769554897969307]
  artificial neural network (ANN) assisted multiple-input multiple-output (MIMO) signal detection can be modeled as ANN-assisted lossy vector quantization (VQ) We propose a novel modular neural network based approach, termed MNNet, where the whole network is formed by a set of pre-defined ANN modules. Our simulation results show that the MNNet approach largely improves the deep-learning capacity with near-optimal performance in various cases.
  arXiv  Detail & Related papers  (2020-04-01T12:56:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.