most citedDeep Reinforcement Learning-based Radio Resource Allocation and Beam Management under Location Uncertainty in 5G mmWave Networks

2 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

eess.SY2022★ 1 cited

Joint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G

Yujie Yao, Hao Zhou, Melike Erol-Kantarci

User location is a piece of critical information for network management and control. However, location uncertainty is unavoidable in certain settings leading to localization errors…

eess.SY2022★ 2 cited

Deep Reinforcement Learning-based Radio Resource Allocation and Beam Management under Location Uncertainty in 5G mmWave Networks

Yujie Yao, Hao Zhou, Melike Erol-Kantarci

Millimeter Wave (mmWave) is an important part of 5G new radio (NR), in which highly directional beams are adapted to compensate for the substantial propagation loss based on UE loc…