System Utility Function Optimization-Based Flight Trajectory and Resource Allocation for UAV-Assisted Integrated Sensing and Communication Systems

In this paper, we consider an uncrewed aerial vehicle-assisted integrated sensing and communication system, consisting of an aerial base station (ABS) and a mobile radar (MR) equipped with a radar receiver. The ABS hovers at a specific position to provide communication services to user clusters, while the MR flies along a predefined trajectory to receive communication reflection signals for target sensing. By taking into account user communication and target sensing performance, as well as flight energy consumption of the MR, the system utility function is modeled as a weighted sum of the minimum average rate of user clusters, sensing channel gain, and flight energy consumption of the MR. The joint communication precoding design, ABS deployment, MR trajectory planning, and communication sensing scheduling problem is modeled as a constrained system utility function maximization problem. Given that the modeled optimization problem is a highly coupled and non-convex mixed-integer optimization problem, it is challenging to solve directly. To tackle this problem, we decompose the original problem into four subproblems and sequentially solve each subproblem using an alternating iteration algorithm. Specifically, for the communication precoding design subproblem, the zero-forcing algorithm is used to eliminate the interference among users and the original problem is transformed into a semi-definite programming problem. For the ABS deployment subproblem and the MR trajectory planning subproblem, the Taylor expansion and the successive convex approximation are employed and slack variables are introduced to convert the original problems into convex optimization problems. For the communication sensing scheduling subproblem, the variable relaxation method is adopted to relax the binary variables into continuous variables, and the optimization tool is used to obtain the solution. Then, two heuristic algorithms are proposed to restore the communication and sensing scheduling variables. Finally, the effectiveness of the proposed algorithms is verified through simulations.