Performance Analysis of Cooperative Integrated Sensing and Communications for 6G Networks

In this work, we aim to effectively characterize the performance of cooperative integrated sensing and communication (ISAC) networks and to reveal how performance metrics relate to network parameters. To this end, we introduce a generalized stochastic geometry framework to model the cooperative ISAC networks, which approximates the spatial randomness of the network deployment. Based on this framework, we derive analytical expressions for key performance metrics in both communication and sensing domains, with a particular focus on communication coverage probability and radar information rate. The analytical expressions derived explicitly highlight how performance metrics depend on network parameters, thereby offering valuable insights into the deployment and design of cooperative ISAC networks. In the end, we validate the theoretical performance analysis through Monte Carlo simulation results. Our results demonstrate that increasing the number of cooperative base stations (BSs) significantly improves both metrics, while increasing the BS deployment density has a limited impact on communication coverage probability but substantially enhances the radar information rate. Additionally, increasing the number of transmit antennas is effective when the total number of transmit antennas is relatively small. The incremental performance gain reduces with the increase of the number of transmit antennas, suggesting that indiscriminately increasing antennas is not an efficient strategy to improve the performance of the system in cooperative ISAC networks.

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.