Cooperative Detection for MEC-Aided Multi-Static ISAC Systems

This paper investigates mobile edge computing (MEC)-aided multi-static integrated sensing and communication (ISAC) systems. In the considered system, each device offloads a task to the MEC server for computation. Meanwhile, a sensing receiver (SR) has to detect a target. To enhance the detection capabilities, the devices and base station (BS) collaboratively transmit radar signals, and the SR detects the target based on the received radar echoes. Specifically, the task execution period is divided into two phases. In the first phase, the devices offload part of their tasks while simultaneously transmitting radar signals for target sensing. In the second phase, the MEC server at the BS processes the offloaded tasks, and meanwhile devices along with the BS transmit radar signals for sensing. Thus, the system entities form a multi-static ISAC framework. Radar signal processing schemes are designed for both scenarios—with and without knowledge of the target response amplitudes. The corresponding cooperative detection probabilities under a constant false alarm probability constraint are derived. To ensure high energy efficiency, optimization problem is formulated to minimize energy consumption across all devices, subject to task computation and detection probability constraints. This is achieved by adjusting beamforming at the devices and BS, as well as task partitioning and phase duration allocation. For scenarios with knowledge of target response amplitudes, the problem is efficiently solved by iteratively optimizing beamforming subproblem, time and task division subproblem using weighted minimum mean square error method, successive convex approximation, and golden section search method. For scenarios without knowledge of target response amplitudes, the formulated bi-level optimization problem is first equivalently transformed into a single-level problem, which is then tackled using alternating optimization method, semidefinite programming, and Lagrangian dual method. Simulations validate the benefits of the proposed MEC-aided cooperative sensing scheme compared with numerous benchmarking schemes including traditional non-cooperative sensing scheme.