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Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks
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  • ARINDAM CHOWDHURY ,
  • Gunjan Verma ,
  • Ananthram Swami ,
  • Santiago Segarra
ARINDAM CHOWDHURY
Rice University

Corresponding Author:[email protected]

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Gunjan Verma
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Ananthram Swami
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Santiago Segarra
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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.
2023Published in IEEE Transactions on Wireless Communications on pages 1-1. 10.1109/TWC.2023.3323207