The Aerial-Ground Integrated Networks(AGIN)take full advantage of the flexible deployment of Aerial Base Stations(ABSs)to provide on-demand coverage and high-quality services in hotspot areas.However,the high dynamics of ABSs pose a great challenge to service continuity assurance in AGIN.Furthermore,given the energy constraints of ABSs,ensuring service continuity with low power consumption becomes an increasingly formidable challenge.This is attributed to the inherent contradiction between enhancing service continuity and reducing power consumption,which typically necessitates distinct flight actions.Focusing on the problem mentioned above,a communication and control joint optimization approach based on Federated Deep Reinforcement Learning(FDRL)is proposed to obtain low-power service continuity assurance in AGIN.The proposed approach ensures service continuity by jointly optimizing the flight actions of ABSs,user associations,and power allocation.To cope with the high dynamics of ABSs,an environmental state experience pool is designed to capture the spatiotemporal correlation of channels,and the rate variance is introduced into the reward function to ensure service continuity.Taking into account the power consumption differences associated with various flight actions,the proposed approach optimizes the flight actions of ABSs to reduce their power consumption.Simulation results demonstrate that,under the premise of satisfying requirements for user rate and rate variance,the proposed approach can effectively reduce network power consumption.Additionally,the performance of FDRL is close to that of centralized reinforcement learning.
关键词
空地一体化网络/服务连续性保障/低功耗通信与控制联合优化
Key words
Aerial-Ground Integrated Networks(AGIN)/Service continuity assurance/Low-power communication and control joint optimization
维普
万方数据