# RSSI-Based Hybrid Beamforming Design with Deep Learning

arXiv: https://arxiv.org/abs/2003.06042 · HTML: https://mlsanity.com/paper/2003.06042/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning](https://mlsanity.com/paper/2106.16194/) (2021)
- [Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming](https://mlsanity.com/paper/2208.05443/) (2022)

## Abstract (arXiv)

Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required.