Adaptive Waveform Design for Cognitive MIMO ISAC

This work addresses the problem of cognitive integrated sensing and communications (ISAC) waveform design, which presents a challenging task due to the need to balance different optimization criteria for communications and sensing performances. In the proposed cognitive approach, the transmit beamforming is dynamically adapted based on past measurements, in order to minimize the Bayesian Cramér- Rao bound (BCRB) on target parameters, while satisfying the minimum required communication channel capacity. Simulations demonstrate that the proposed adaptive waveform design achieves high estimation performance while satisfying the communication capacity requirements, effectively addressing the inherent ISAC trade-offs.