A joint resource allocation strategy in a radar-communication coexistence network for target tracking and user serving

• JBCAPSA strategy for an RCC network. • TASOM for problem solving. • CUA, SCA, BTA and power allocation are adaptively tuned. • Superior performance compared with state-of-the-art methods. With the rapid development of commercial communications, the research on Radar-Communication Coexistence (RCC) systems is becoming a hot spot. The resource allocation techniques play a crucial role in the RCC systems. A performance-driven Joint Radar-target and Communication-user Assignment, along with Power and Subchannel Allocation (JRCAPSA) strategy, is proposed for an RCC network. The optimization model aims to minimize the sum of weighted Bayesian Cramér-Rao Lower Bounds (BCRLBs) of target state estimates for radar purpose. This is subject to constraints such as the Communication Data Rate (CDR) for communication purpose, the total power budget in each RCC system, assignment relationships, and the number of available subchannels. Considering that such a problem falls into the realm of Mixed Integer Programming (MIP), a Three-stage Iteratively Augment-based Optimization Method (TIAOM) is developed. The Communication-User Assignment (CUA), Communication Subchannel Allocation (SCA), and Radar-Target Assignment (RTA) feasible solution domains are iteratively expanded based on their importance, leading to the efficient acquisition of a suboptimal solution. Simulation results show the outperformance of the proposed JRCAPSA strategy, compared to the other benchmarks and the OPTI toolbox. The results also imply that the Bayesian Cramér-Rao Lower Bound (BCRLB) is a more stringent optimization metric for the achieved Mean Square Error (MSE), compared to Mutual Information (MI) and Signal-to-Interference-Noise Ratio (SINR).