@inproceedings{db23bca1ea374a1aac96054e2eb7ffaf,
title = "Beam-Steering Optimization in Multi-UAVs mmWave Networks: A Mean Field Game Approach",
abstract = "In unmanned aerial vehicle (UAV)-assisted mmWave networks, the beam-steering issue is a significant challenge to establish the reliable and steady connection between flying base stations and ground users. In this paper, we investigate the optimization problem of beam-steering in the multi-UAVs and multi-antennas mmWave (MUMA) networks to maximize the system sum-rate by adjusting each beam-steering angle of departure. In order to solve this problem, we propose a novel mean field game (MFG)-based massive multi-input multi-output (MIMO) angle control algorithm to obtain the optimal mmWave channel allocation between UAVs and ground users. In addition, when dealing with the problem of initial sensitivity and difficulty in solving the partial differential equations in the MFG, we utilize reinforcement learning to achieve the mean field equilibrium. Simulation results show the proposed algorithm can improve the system sum-rate with a faster convergence, verifying the efficiency of the proposed algorithm.",
keywords = "beam-steering, mean field game, mmWave networks, reinforcement learning",
author = "Qianqian Cheng and Lixin Li and Kaiyuan Xue and Huan Ren and Xu Li and Wei Chen and Zhu Han",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019 ; Conference date: 23-10-2019 Through 25-10-2019",
year = "2019",
month = oct,
doi = "10.1109/WCSP.2019.8927962",
language = "英语",
series = "2019 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019",
}