TY - GEN
T1 - Beam-Steering Optimization in Multi-UAVs mmWave Networks
T2 - 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019
AU - Cheng, Qianqian
AU - Li, Lixin
AU - Xue, Kaiyuan
AU - Ren, Huan
AU - Li, Xu
AU - Chen, Wei
AU - Han, Zhu
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
KW - beam-steering
KW - mean field game
KW - mmWave networks
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85077778318&partnerID=8YFLogxK
U2 - 10.1109/WCSP.2019.8927962
DO - 10.1109/WCSP.2019.8927962
M3 - 会议稿件
AN - SCOPUS:85077778318
T3 - 2019 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019
BT - 2019 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 October 2019 through 25 October 2019
ER -