Rapid Hybrid Modular Receive Beamforming Via Learned Optimization

Various multiple-input multiple-output (MIMO) systems, including cell-free massive MIMO and partially connected hybrid MIMO architectures, beamform using multiple similar multi-antenna modules. While this operation enables implementing scalable MIMO in a power and cost effective manner, the setting of the beampattern involves challenging constrained optimization that should be repeatedly solved on each coherence duration. In this work we propose a rapid optimization algorithm for beamforming in uplink modular hybrid MIMO system based on learn-to-optimize methodology. We tackle the rate maximization objective using projected gradient ascent steps with momentum. We then leverage data to tune the hyperparameters of the optimizer, allowing it to operate reliably in a fixed and small number of iterations while completely preserving its interpretable operation. Numerical results show that our learn-to-optimize method notably reduces the number of iterations and computation latency required to reliably tune modular MIMO receivers.