Max-Min Fairness in Active Aerial Reconfigurable Intelligent Surface-Aided ISAC Network

This paper considers an unmanned aerial vehicle (UAV)-assisted sensing and communication framework that integrates an active reconfigurable intelligent surface (RIS). We aim at maximizing the sum of the minimum signal-to-interference-plus-noise ratios (SINRs) of user equipments within the whole integrated sensing and communication period by coordinating the optimization of several components: the transmit beamforming matrix and receive filter at a base station serving dual roles in radar and communication, the reflection coefficients of the active RIS mounted on the UAV, and the UAV’s trajectory. The non-convexity of the objective function, combined with the intricate dependencies among variables, makes the problem highly complex. To address these complexities, a novel alternating optimization approach is introduced, combining techniques such as generalized Rayleigh quotient optimization, semidefinite relaxation, majorization-minimization methods, and sequential quadratic programming. Numerical experiments validate the proposed method, showing substantial SINR improvements compared to benchmark solutions that use fixed UAV trajectories, active RIS, passive RIS, or randomly configured phase shifts.