可移动天线辅助ISAC系统的鲁棒优化OA

To address the performance degradation of integrated sensing and communication(ISAC)systems caused by imperfect channel state information(CSI),a robust resource optimization method is investigated for movable antenna(MA)-assisted systems.In the presence of channel estimation errors,conventional designs based on perfect CSI suffer from significant performance loss,making it challenging to achieve an effective trade-off between communication and sensing under channel uncertainty.An MA-enabled ISAC system is considered,where movable antennas are deployed at both the base station and communication users.By jointly optimizing the antenna positions at the base station,transmit beamforming,and antenna positions at the user side,the sensing mutual information is maximized while satisfying communication quality-of-service constraints and antenna movement constraints.The resulting problem is highly non-convex with strongly coupled variables,and CSI uncertainty further introduces infinitely many inequality constraints.To tackle these challenges,a robust alternating optimization algorithm is developed by integrating semidefinite relaxation and successive convex approximation to obtain efficient feasible solutions.Simulation results demonstrate that,compared with fixed-position antenna systems,the proposed scheme maintains system stability under severe channel estimation errors and achieves an average improvement of 38.8%in sensing mutual information.