most citedHybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning

22 citations · 22 across the 1 of their papers we have counts for

collaborators
Showing eess.SPShow all

2 papers · 1 filter

eess.SP2021★ 22 cited

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

Qisheng Wang, Xiao Li, Shi Jin +1

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) sy…

eess.SP2019

PrecoderNet: Hybrid Beamforming for Millimeter Wave Systems with Deep Reinforcement Learning

Qisheng Wang, Keming Feng, Xiao Li +1

In this letter, we investigate the hybrid beamforming for millimeter wave massive multiple-input multiple-output (MIMO) system based on deep reinforcement learning (DRL). Imperfect…