Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning
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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.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 1 Feb 2021.
Builds on
6 of 16 references.
References · 6
- 2015Continuous control with deep reinforcement learningcited by 3,290
- 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,525
- 2015Prioritized Experience Replaycited by 1,078
- 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
- 2019Learning latent state representation for speeding up explorationcited by 8
- 2018Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communicationcited by 3
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Citing papers per year: 2022: 2.
2 papers.