Sensing Capacity for Integrated Sensing and Communication Systems in Low-Altitude Economy

The burgeoning significance of the low-altitude economy (LAE) has garnered considerable interest, largely fuelled by the widespread deployment of unmanned aerial vehicles (UAVs). To tackle the challenges associated with the detection of unauthorized UAVs and the efficient scheduling of authorized UAVs, this letter introduces a novel performance metric, termed sensing capacity, for integrated sensing and communication (ISAC) systems. This metric, which quantifies the capability of a base station (BS) to detect multiple UAVs simultaneously, leverages signal-to-noise ratio (SNR) and probability of detection (PD) as key intermediate variables. Through mathematical derivations, we can derive a closed-form solution for the maximum number of UAVs that can be detected by the BS while adhering to a specific SNR constraint. Furthermore, an approximate solution based on PD constraints is proposed to facilitate the efficient determination of the threshold for the maximum number of detectable UAVs. The accuracy of this analytical approach is verified through extensive simulation results.

Reconfigurable Intelligent Surface assisted Integrated Sensing, Communication and Computation Systems

This paper investigates a reconfigurable intelligent surface (RIS)-assisted integrated sensing, communication, and computation (ISCC) system. In this paradigm, the integrated sensing and communication (ISAC)-enabled user equipments (UEs) simultaneously detect the target and offload the computational tasks of radar sensing to the edge computing server (ECS) through their communication functionality. To enhance the efficiency of computation offloading, we deploy an RIS to mitigate the high attenuation between UEs and the ECS. A latency minimization problem is investigated with constraints on UE's transmit power, radar signal-to-interference-plus-noise ratio (SINR), RIS phase shift, and computation capability. We propose an algorithm based on the block coordinate descent (BCD) method to decouple the original problem into two subproblems, and then the computational and beamforming variables are optimized alternately utilizing efficient iterative algorithms. Simulation results demonstrate the effectiveness of our proposed algorithm.