Rapid and Power-Aware Learned Optimization for Modular Receive Beamforming

2024-08-01

arXiv.org

AI Summary

This work addresses the challenge of achieving low-power, rapid solution of constrained optimization problems for modular hybrid MIMO uplink receive beamforming within channel coherence time. Method: We propose a data-driven, physics-informed learned optimizer that uniquely embeds projected gradient ascent with momentum into a differentiable, learnable framework—preserving algorithmic interpretability with minimal iterations. For the first time, we explicitly learn to jointly optimize low-resolution phase shifts and analog component switching-off, enabling co-optimization of power consumption and throughput. Contribution/Results: Compared to conventional methods, our approach significantly reduces iteration count and computational latency. Experimental results demonstrate Pareto-optimal trade-offs between power consumption and spectral efficiency, validating its effectiveness for low-power hardware deployment in practical systems.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Multiple-input multiple-output (MIMO) systems play a key role in wireless communication technologies. A widely considered approach to realize scalable MIMO systems involves architectures comprised of multiple separate modules, each with its own beamforming capability. Such models accommodate cell-free massive MIMO and partially connected hybrid MIMO architectures. A core issue with the implementation of modular MIMO arises from the need to rapidly set the beampatterns of the modules, while maintaining their power efficiency. This leads to challenging constrained optimization that should be repeatedly solved on each coherence duration. In this work, we propose a power-oriented optimization algorithm for beamforming in uplink modular hybrid MIMO systems, which learns from data to operate rapidly. We derive our learned optimizer by tackling 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. We show how power efficient beamforming can be encouraged by the learned optimizer, via boosting architectures with low-resolution phase shifts and with deactivated analog components. 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, and that it allows obtaining desirable balances between power efficient designs and throughput.

Problem

• Modular MIMO Receiver
• Direction Optimization
• Power Efficiency

Innovation

• Modular MIMO Receiver
• Direction Learning Optimization
• Energy Efficiency