# Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment

arXiv: https://arxiv.org/abs/2001.09251 · HTML: https://mlsanity.com/paper/2001.09251/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2020.

## Built on by (most cited first, 8 of 8)

- [Generating High Dimensional User-Specific Wireless Channels using Diffusion Models](https://mlsanity.com/paper/2409.03924/) (2024)
- [UB3: Best Beam Identification in Millimeter Wave Systems via Pure Exploration Unimodal Bandits](https://mlsanity.com/paper/2301.03456/) (2022)
- [Learning Robust Representations for Communications over Noisy Channels](https://mlsanity.com/paper/2409.01129/) (2024)
- [Deep Reinforcement Learning for mmWave Initial Beam Alignment](https://mlsanity.com/paper/2302.08969/) (2023)
- [DRL-AdaPart: DRL-Driven Adaptive STAR-RIS Partitioning for Fair and Efficient Resource Utilization](https://mlsanity.com/paper/2407.06868/) (2024)
- [DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users](https://mlsanity.com/paper/2502.01278/) (2025)
- [CAR-BRAINet: Sub-6GHz Aided Spatial Adaptive Beam Prediction with Multi Head Attention for Heterogeneous Vehicular Networks](https://mlsanity.com/paper/2509.10508/) (2025)
- [Meta-Learning Multi-armed Bandits for Beam Tracking in 5G and 6G Networks](https://mlsanity.com/paper/2512.05680/) (2025)

## Abstract (arXiv)

Directional beamforming is a crucial component for realizing robust wireless communication systems using millimeter wave (mmWave) technology. Beam alignment using brute-force search of the space introduces time overhead while location aided blind beam alignment adds additional hardware requirements to the system. In this paper, we introduce a method for blind beam alignment based on the RF fingerprints of user equipment obtained by the base stations. The proposed system performs blind beam alignment on a multiple base station cellular environment with multiple mobile users using deep reinforcement learning. We present a novel neural network architecture that can handle a mix of both continuous and discrete actions and use policy gradient methods to train the model. Our results show that the proposed method can achieve a data rate of up to four times the traditional method without any overheads.