应急场景下空地基站协同部署与资源调度方法OA

Natural disasters can cause large-scale damage to terrestrial base stations,leading to mobile network ser-vice disruptions and severely degrading the efficiency of emergency rescue operations.To rapidly restore post-disaster mobile network performance,this paper proposes an optimization method for coordinated air-ground base-station de-ployment and resource scheduling in emergency mobile communication networks.First,a post-disaster terrestrial base-station damage model is developed,which jointly considers earthquake intensity,base-station distribution,and structural characteristics of base-station buildings,enabling accurate characterization of the survival probability of ter-restrial base stations.Meanwhile,a dynamic user aggregation model is designed to capture post-disaster user mobility patterns,yielding accurate post-disaster user distribution features.On this basis,considering practical constraints such as limited emergency equipment availability and finite UAV endurance in disaster areas,the total energy con-sumption of aerial base stations is minimized by jointly optimizing the transmit power of air-ground base stations,the placement of aerial base stations,and resource allocation strategies.To meet the requirement of rapid post-disaster deployment,the original non-convex mixed-integer programming problem is decomposed into a terrestrial-side sub-problem and an aerial-side subproblem,and a two-stage collaborative algorithm is devised by nesting numerical opti-mization within reinforcement learning.By embedding numerical optimization into the feedback learning process of deep neural networks,the proposed algorithm reduces the complexity of conventional numerical optimization and sig-nificantly accelerates reinforcement learning training.Simulation results show that,compared with baseline algo-rithms,in high-magnitude earthquake scenarios,the proposed method restores communication coverage using only 68%of the total energy consumption while improving the average user rate by approximately 29%.