# Unsupervised Learning for Passive Beamforming

arXiv: https://arxiv.org/abs/2001.02348 · HTML: https://mlsanity.com/paper/2001.02348/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2020.

## Built on by (most cited first, 11 of 11)

- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design](https://mlsanity.com/paper/2101.09137/) (2021)
- [A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints](https://mlsanity.com/paper/2111.06555/) (2021)
- [Ultra-Reliable Indoor Millimeter Wave Communications using Multiple Artificial Intelligence-Powered Intelligent Surfaces](https://mlsanity.com/paper/2104.00075/) (2021)
- [Federated Distributionally Robust Optimization for Phase Configuration of RISs](https://mlsanity.com/paper/2108.09026/) (2021)
- [Channel Estimation and Hybrid Architectures for RIS-Assisted Communications](https://mlsanity.com/paper/2104.07115/) (2021)
- [Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations](https://mlsanity.com/paper/2105.01771/) (2021)
- [Unsupervised Learning-Based Joint Resource Allocation and Beamforming Design for RIS-Assisted MISO-OFDMA Systems](https://mlsanity.com/paper/2506.22448/) (2025)
- [Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods](https://mlsanity.com/paper/2204.13372/) (2022)
- [Machine Learning for Metasurfaces Design and Their Applications](https://mlsanity.com/paper/2211.01296/) (2022)
- [A CNN-based End-to-End Learning for RIS-assisted Communication System](https://mlsanity.com/paper/2503.13976/) (2025)

## Abstract (arXiv)

Reconfigurable intelligent surface (RIS) has recently emerged as a promising candidate to improve the energy and spectral efficiency of wireless communication systems. However, the unit modulus constraint on the phase shift of reflecting elements makes the design of optimal passive beamforming solution a challenging issue. The conventional approach is to find a suboptimal solution using the semi-definite relaxation (SDR) technique, yet the resultant suboptimal iterative algorithm usually incurs high complexity, hence is not amenable for real-time implementation. Motivated by this, we propose a deep learning approach for passive beamforming design in RIS-assisted systems. In particular, a customized deep neural network is trained offline using the unsupervised learning mechanism, which is able to make real-time prediction when deployed online. Simulation results show that the proposed approach maintains most of the performance while significantly reduces computation complexity when compared with SDR-based approach.