# Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2102.00735 · HTML: https://mlsanity.com/paper/2102.00735/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Learning latent state representation for speeding up exploration](https://mlsanity.com/paper/1905.12621/) (2019)
- [Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication](https://mlsanity.com/paper/1801.04541/) (2018)

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

- [Joint Power Allocation and Beamformer for mmW-NOMA Downlink Systems by Deep Reinforcement Learning](https://mlsanity.com/paper/2205.06489/) (2022)
- [Deep Reinforcement Learning in mmW-NOMA: Joint Power Allocation and Hybrid Beamforming](https://mlsanity.com/paper/2205.06814/) (2022)

## Abstract (arXiv)

In this letter, we investigate the hybrid beamforming based on deep reinforcement learning (DRL) for millimeter Wave (mmWave) multi-user (MU) multiple-input-single-output (MISO) system. A multi-agent DRL method is proposed to solve the exploration efficiency problem in DRL. In the proposed method, prioritized replay buffer and more informative reward are applied to accelerate the convergence. Simulation results show that the proposed architecture achieves higher spectral efficiency and less time consumption than the benchmarks, thus is more suitable for practical applications.