Joint resource and trajectory optimization in a UAV-enabled dual-function radar-communication network

The resource and trajectory optimization problem is critical in joint radar and communication systems, as it mitigates spectrum interference and enhances resource utilization. This paper proposes a joint power and subchannel allocation with trajectory optimization (JPSATO) strategy for a dual-function radar-communication network that tracks multiple targets while serving multiple users. The predicted-conditional Cramér-Rao lower bound (PC-CRLB) in the clutter domain is derived as the tracking performance metric, quantifying the accuracy loss caused by clutter. The optimization model is formulated as minimizing the sum of weighted PC-CRLBs of multiple targets while adhering to the communication data ratio constraint of each user. It is shown that the continuous power allocation, trajectory optimization, and binary subchannel allocation variables are all coupled in the objective function and constraints, resulting in a mixed integer programming problem. In addition, an information reduction factor is embedded in the PC-CRLB to express the clutter effects, and it destroys the convexity of objective function with respect to the power allocation. A four-layer alternating optimization-based method (FLAOM) is designed for this problem-solving. The radar power allocation and communication power allocation are solved using the sequential optimization method, where the nonconvex sub-problem is transformed into a near convex one in each iteration using the first-order Taylor expansion. Then, the subchannel allocation is solved using a greedy search idea. Finally, the trajectory is optimized by the reformulation and the sequential optimization method. Simulation results confirm the effectiveness and efficiency of proposed FLAOM compared with the state-of-the-art methods. It is also shown that the trajectory optimization plays important roles in the considered JPSATO problem. • A joint power and subchannel allocation with trajectory optimization (JPSATO) strategy for a UAV-enabled DFRC network. • A four-layer alternating optimization-based method for the JPSATO problem solving. • Two propositions and reformulation are utilized to accelerate the trajectory optimization subproblem-solving. • A closed-loop feedback system is built by the tune of resource and trajectory.