基于协作感知与状态预测的多基站联合传输设计OA

To address the issues of outdated channel state information(CSI)caused by the high mobility of low-altitude unmanned aerial vehicles(UAVs),as well as the large pilot overhead and limited accuracy of conventional beam training methods,a cooperative sensing-assisted beamforming scheme under joint transmission(JT)is investigated.Based on a distributed space-time block coding(DSTBC)-enabled integrated sensing and communication(ISAC)framework,a system model is established for multiple base stations with multi-antenna system serving multiple UAV users.Zero-forcing(ZF)precoding is adopted to suppress multi-user interference,and a distributed extended Kalman filtering(DEKF)algorithm is improved by incorporating cooperative sensing information and state prediction mechanisms,enabling multi-target tracking and angle prediction.On this basis,the predicted results are utilized to design the beamforming matrix,thereby reducing the dependence on pilot training and feedback while improving CSI timeliness and beam alignment accuracy.Simulation results demonstrate that the proposed method significantly reduces the angle estimation errors under different antenna configurations,user numbers,and observation intervals and achieves higher system throughput compared with single-station transmission.Moreover,the proposed method exhibits strong robustness and scalability in high-mobility multi-user scenarios.