Joint Topology Construction and Power Adjustment for UAV Networks:A Deep Reinforcement Learning Based Approach  

作　　者：Wenjun Xu Huangchun Lei Jin Shang 

机构地区：[1]The Key Laboratory of Universal Wireless Communications,Ministry of Education,Beijing University of Posts and Telecommunications,Beijing,China [2]Frontier Research Center,the Peng Cheng Laboratory,Shenzhen,China

出　　处：《China Communications》2021年第7期265-283,共19页中国通信（英文版）

摘　　要：In this paper,we investigate a backhaul framework jointly considering topology construction and power adjustment for self-organizing UAV networks.To enhance the backhaul rate with limited information exchange and avoid malicious power competition,we propose a deep reinforcement learning(DRL)based method to construct the backhaul framework where each UAV distributedly makes decisions.First,we decompose the backhaul framework into three submodules,i.e.,transmission target selection(TS),total power control(PC),and multi-channel power allocation(PA).Then,the three submodules are solved by heterogeneous DRL algorithms with tailored rewards to regulate UAVs’behaviors.In particular,TS is solved by deep-Q learning to construct topology with less relay and guarantee the backhaul rate.PC and PA are solved by deep deterministic policy gradient to match the traffic requirement with proper finegrained transmission power.As a result,the malicious power competition is alleviated,and the backhaul rate is further enhanced.Simulation results show that the proposed framework effectively achieves system-level and all-around performance gain compared with DQL and max-min method,i.e.,higher backhaul rate,lower transmission power,and fewer hop.

关 键 词：UAV networks target selection power control power allocation deep reinforcement learning 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TP18[自动化与计算机技术—控制理论与控制工程]