Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks

• URL: http://arxiv.org/abs/2304.00446v1
• Date: Sun, 2 Apr 2023 04:34:00 GMT
• Title: Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks
• Authors: Arindam Chowdhury, Gunjan Verma, Ananthram Swami, and Santiago Segarra
• Abstract summary: We develop an efficient and near-optimal solution for beamforming in multi-user multiple-input-multiple-output single-hop wireless ad-hoc interference networks. Inspired by the weighted minimum mean squared error (WMMSE) method, a classical approach to solving this problem, and the principle of algorithm unfolding, we present unfolded WMMSE for MU-MIMO.
• Score: 37.32261787567901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop an efficient and near-optimal solution for beamforming in multi-user multiple-input-multiple-output single-hop wireless ad-hoc interference networks. Inspired by the weighted minimum mean squared error (WMMSE) method, a classical approach to solving this problem, and the principle of algorithm unfolding, we present unfolded WMMSE (UWMMSE) for MU-MIMO. This method learns a parameterized functional transformation of key WMMSE parameters using graph neural networks (GNNs), where the channel and interference components of a wireless network constitute the underlying graph. These GNNs are trained through gradient descent on a network utility metric using multiple instances of the beamforming problem. Comprehensive experimental analyses illustrate the superiority of UWMMSE over the classical WMMSE and state-of-the-art learning-based methods in terms of performance, generalizability, and robustness.

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