main.pdf (755.37 kB)

Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks

Download (755.37 kB)
posted on 2023-04-07, 20:35 authored by ARINDAM CHOWDHURYARINDAM CHOWDHURY, Gunjan Verma, Ananthram Swami, Santiago Segarra

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.


Army Research Office Cooperative Agreement Number W911NF-19-2-0269


Email Address of Submitting Author

Submitting Author's Institution

Rice University

Submitting Author's Country

  • United States of America

Usage metrics