# Rapid and Power-Aware Learned Optimization for Modular Receive Beamforming

arXiv: https://arxiv.org/abs/2408.00439 · HTML: https://mlsanity.com/paper/2408.00439/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 57 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 30 of 31)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Learning to Optimize: A Primer and A Benchmark](https://mlsanity.com/paper/2103.12828/) (2021)
- [Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations](https://mlsanity.com/paper/1704.00648/) (2017)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization](https://mlsanity.com/paper/2205.02640/) (2022)
- [Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?](https://mlsanity.com/paper/1902.02647/) (2019)
- [A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications](https://mlsanity.com/paper/2307.07340/) (2023)
- [Scalable Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/1908.03119/) (2019)
- [Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays](https://mlsanity.com/paper/1902.07678/) (2019)
- [Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications](https://mlsanity.com/paper/2006.07838/) (2020)
- [Discriminative and Generative Learning for Linear Estimation of Random Signals [Lecture Notes]](https://mlsanity.com/paper/2206.04432/) (2022)
- [Learn to Rapidly and Robustly Optimize Hybrid Precoding](https://mlsanity.com/paper/2301.00369/) (2023)
- [Hybrid Precoding for Multi-User Millimeter Wave Massive MIMO Systems: A Deep Learning Approach](https://mlsanity.com/paper/1911.04239/) (2019)
- [Deep Task-Based Analog-to-Digital Conversion](https://mlsanity.com/paper/2201.12634/) (2022)
- [Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques](https://mlsanity.com/paper/2211.02165/) (2022)
- [RF Chain Reduction for MIMO Systems: A Hardware Prototype](https://mlsanity.com/paper/1905.05315/) (2019)
- [Deep-Unfolding Neural-Network Aided Hybrid Beamforming Based on Symbol-Error Probability Minimization](https://mlsanity.com/paper/2109.06574/) (2021)
- [Framework on Deep Learning Based Joint Hybrid Processing for mmWave Massive MIMO Systems](https://mlsanity.com/paper/2006.03215/) (2020)
- [Deep Unfolding Hybrid Beamforming Designs for THz Massive MIMO Systems](https://mlsanity.com/paper/2302.12041/) (2023)
- [Multi-User Modular XL-MIMO Communications: Near-Field Beam Focusing Pattern and User Grouping](https://mlsanity.com/paper/2308.11289/) (2023)
- [Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM](https://mlsanity.com/paper/2309.14353/) (2023)
- [Joint Phase-Time Arrays: A Paradigm for Frequency-Dependent Analog Beamforming in 6G](https://mlsanity.com/paper/2312.11682/) (2023)
- [Towards 6G MIMO: Massive Spatial Multiplexing, Dense Arrays, and Interplay Between Electromagnetics and Processing](https://mlsanity.com/paper/2401.02844/) (2024)
- [Rapid Optimization of Superposition Codes for Multi-Hop NOMA MANETs via Deep Unfolding](https://mlsanity.com/paper/2406.05747/) (2024)
- [Trade-offs in Decentralized Multi-Antenna Architectures: The WAX Decomposition](https://mlsanity.com/paper/2012.00572/) (2020)
- [Massive MIMO with Radio Stripes for Indoor Wireless Energy Transfer](https://mlsanity.com/paper/2106.12188/) (2021)
- [Rainbow-link: Beam-Alignment-Free and Grant-Free mmW Multiple Access using True-Time-Delay Array](https://mlsanity.com/paper/2109.04357/) (2021)
- [Robust Task-Specific Beamforming with Low-Resolution ADCs for Power-Efficient Hybrid MIMO Receivers](https://mlsanity.com/paper/2212.00107/) (2022)
- [AI-Empowered Hybrid MIMO Beamforming](https://mlsanity.com/paper/2303.01723/) (2023)

## Abstract (arXiv)

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.