面向低空无人机数据卸载与波束跟踪的具身智能电磁频谱资源分配技术OA

With the rapid development of the low-altitude economy,unmanned aerial vehicles(UAVs)have been in-creasingly applied in scenarios such as environmental sensing,intelligent inspection,emergency rescue,and intelligent transportation.However,due to the lack of continuous environmental perception and real-time decision-making capabilities,traditional communication systems find it difficult to simultaneously achieve efficient beam tracking and low data transmis-sion latency in highly mobile and dynamically changing environments.To address the demanding data offloading and beam tracking requirements in high-dynamic low-altitude UAV scenarios,this paper proposes an embodied-intelligence-driven electromagnetic spectrum resource allocation method.Specifically,by leveraging integrated sensing and communication(ISAC)technology,an embodied intelligent base station(BS)architecture with a closed-loop"perception-cognition-execu-tion(PCE)"capability is constructed,where the BS is modeled as an intelligent agent capable of continuously interacting with the environment and evolving online.The proposed architecture utilizes sensing functions to obtain UAV location in-formation in real time and assists beam prediction and resource allocation based on sensing results,thereby effectively re-ducing the beam alignment overhead caused by conventional pilot-based searching.Furthermore,this paper establishes a quantitative mapping relationship between sensing accuracy and sector width in the beam alignment process,enabling uni-fied modeling of the impact of sensing performance on communication processes.The spectrum resource allocation prob-lem in low-altitude UAV data offloading is then formulated as a multi-objective markov decision process(MOMDP)to jointly optimize sensing accuracy and data offloading latency.On this basis,the performance tradeoff caused by spectrum sharing between sensing and communication is further analyzed,enabling the resource allocation strategy to dynamically ad-just the proportion of sensing and communication resources according to environmental variations,thereby improving over-all system efficiency and task completion timeliness.To solve this problem,a multi-objective reinforcement-supervised hy-brid online learning(MO-RSOL)algorithm is proposed.The proposed algorithm generates multi-objective experience data through environmental interaction,combines supervised learning mechanisms to achieve stable policy network updates,and continuously expands the policy performance boundary through a Pareto-coverage-oriented dataset maintenance mecha-nism,thereby enhancing the online adaptability and continuous evolution capability of the system in dynamic environments.Simulation results demonstrate that the proposed embodied intelligent BS architecture can reduce data offloading latency by approximately 21.7% compared with traditional non-embodied intelligent BS designs.Moreover,compared with existing baseline algorithms,the proposed MO-RSOL algorithm improves system latency and sensing performance by approximately 21% and 69%,respectively,while effectively adapting to complex scenarios involving UAV quantity variations,traffic load fluctuations,and dynamic environmental changes.These results validate the effectiveness and superiority of the proposed method in low-altitude intelligent spectrum management and high-dynamic wireless resource allocation,while also provid-ing new research insights and theoretical references for the design of embodied intelligent communication systems in future 6G low-altitude intelligent networks.