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空地一体化网络服务连续性保障的低功耗通信与控制联合优化方法

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空地一体化网络(AGIN)充分利用了空中基站(ABSs)灵活部署的特点,为热点地区提供了按需覆盖与高质量服务.然而,空中基站的高动态性使得网络的服务连续性难以保障.而且,空中基站能量受限,提升服务连续性和降低功耗通常又对应不同的飞行动作,因此,低功耗的服务连续性保障尤为困难.针对上述问题,该文基于联邦深度强化学习(FDRL)提出了一种面向低功耗服务连续性保障的通信与控制联合优化方法.所提方法通过联合优化空中基站的移动控制、用户关联和功率分配来保障网络服务的连续性.针对空中基站的高动态性,通过在所提方法中设计了环境状态经验池来利用信道的时空相关性,并在奖励函数中引入速率方差来保障网络服务连续性.考虑到不同飞行动作的功耗差异,所提方法通过优化空中基站的飞行动作来降低网络功耗.仿真结果说明,该文所提算法在满足用户速率需求和速率方差需求的前提下,能够减小网络功耗,并且所提联邦深度强化学习的性能接近中心式强化学习的性能.
Low-power Communication and Control Joint Optimization for Service Continuity Assurance in Aerial-Ground Integrated Networks
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.

Aerial-Ground Integrated Networks(AGIN)Service continuity assuranceLow-power communication and control joint optimization

蔡自伟、盛敏、刘俊宇、赵晨曦、李建东

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西安电子科技大学空天地一体化综合业务网全国重点实验室 西安 710071

鹏城实验室 深圳 518055

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

国家重点研发计划国家自然科学基金国家自然科学基金国家自然科学基金陕西省重点产业创新链项目陕西省重点产业创新链项目鹏城实验室重点项目

2022YFB29023006212100162341111621713442022ZDLGY05-012022ZDLGY05-06PCL2021A15

2024

10.11999/JEIT231192

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子：1.302
ISSN：1009-5896
年,卷(期)：2024.46(5)