UAV-RIS-Assisted Covert ISCC System for Near-Field Low-Latency Scenarios: System Design and Performance Analysis

Multifunctional integration in near-field systems challenges the timeliness and covertness of integrated sensing, communication, and computing (ISCC), particularly in ultra-reliable low-latency scenarios. We investigate an unmanned aerial vehicle-reconfigurable intelligent surface (UAV-RIS)-assisted covert ISCC system exploiting near-field spherical wave propagation. Unlike far-field approaches, our framework achieves precise distance-dependent beam focusing, shielding signals from angle-aligned wardens while performing sensing and offloading. We derive a closed-form expression for the covert age of information to quantify timeliness. We aim to maximize the covert ISCC rate constrained by covertness, block error probability, and UAV-RIS settings. The resulting non-convex, high-dimensional problem challenges traditional optimization in dynamic environments. To address this, we propose a quantum multi-agent meta-reinforcement learning algorithm with meta-training and local adaptation phases. This algorithm achieves faster convergence and higher robustness than conventional baselines. Simulations validate its effectiveness in balancing sensing accuracy, covertness, and timeliness.