Model-Based Online Learning for Joint Radar-Communication Systems Operating in Dynamic Interference

This paper addresses the problems of co-design and cooperation among radar and communication systems operating in a shared spectrum scenario. Online learning facilitates using the spectrum flexibly while managing and mitigating rapidly time-frequency-space varying interference. We extend the previously proposed Model-Based Online Learning (MBOL) algorithm [1] to allocate frequency and power resources among co-designed and collaborating sensing and communication systems in dynamic interference scenarios. The proposed MBOL algorithm learns a predictive spectrum model using online convex optimization (OCO), assigns sub-bands between sensing and communications tasks, and optimizes their power for the tasks at hand. The performance of the proposed MBOL method is evaluated in simulations using the proposed constrained regret criterion and shown to improve the sensing and communications performance compared to the baseline method in terms of lower and sub-linear constrained regret.