Robust Power Allocation for UAV-Aided ISAC Systems With Uncertain Location Sensing Errors

Le résumé fourni par la source

Integrated sensing and communication (ISAC) enables simultaneous sensing and data transmission with the assistance of unmanned aerial vehicles (UAVs) in emergency disaster relief and inspects scenarios. However, the impact of sensing uncertainty on communication performance has not been systematically investigated. In this paper, we propose a novel UAV-aided ISAC framework that explicitly accounts for the uncertainty location sensing error (LSE). To characterize LSE more realistically, we derive the Cramér-Rao bound (CRB) and use it as the variance parameter for the considered uncertainty LSE models, instead of adopting the conventional unit-variance assumption. Then, we analytically reveal the inherent coupling relationship between LSE and achievable communication rate. Considering three practical LSE distributions, namely, ellipsoidal, Gaussian, and arbitrary distributions, we formulate three robust communication and sensing power allocation problems and develop tractable solutions using the${\cal {S}}$-Procedure with alternating optimization (${\cal {S}}$-AO) method, Bernstein-type inequality with successive convex approximation (BI-SCA) method, and conditional value-at-risk (CVaR) with AO (CVaR-AO) method. Simulation results validate the theoretical coupling, demonstrate the robustness of the proposed schemes, and reveal sensing-communication trade-offs, providing valuable insights for robust UAV-aided ISAC system design.

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