Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach

@article{Xue2022BeamMI,
  title={Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach},
  author={Qing Xue and Yijing Liu and Yao Sun and Jian Wang and Lili Yan and Gang Feng and Shaodan Ma},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.01307}
}
—Deploying ultra-dense networks that operate on millimeter wave (mmWave) band is a promising way to address the tremendous growth on mobile data traffic. However, one key challenge of ultra-dense mmWave network (UDmmN) is beam management due to the high propagation delay, limited beam coverage as well as numerous beams and users. In this paper, a novel systematic beam control scheme is presented to tackle the beam management problem which is difficult due to the non- convex objective function. We…