Cognitive Radio-Enabled ISAC Systems: Joint Power Allocation and Collaborative Beamforming

Integrated sensing and communication (ISAC) has emerged as a pivotal enabling technology for next-generation wireless systems, offering simultaneous information transmission and target sensing over the shared radio spectrum. Leveraging the dynamic spectrum sharing capabilities of cognitive radio (CR), this paper proposes a novel underlay CR-enabled ISAC framework, where the primary network performs multi-target sensing, whereas the secondary network is tasked with multi-user communication. To enhance spectral efficiency, we develop a joint optimization methodology for adaptive power allocation and collaborative beamforming, applicable to both perfect and imperfect channel state information (CSI) scenarios. In particular, we aim to maximize the sum achievable rate (SAR) of the secondary users under strict sensing protection, by enforcing guaranteed sensing quality-of-service via sensing signal-to-interference-plus-noise ratio constraints, subject to transmit power budgets. Under perfect CSI, we propose a path following-based iterative algorithm capable of obtaining the Karush-Kuhn-Tucker solution to the non-convex SAR maximization problem. For practical implementations with CSI uncertainties, an enhanced robust optimization algorithm combining the generalized Lagrangian dual transformation with cutting-set method is developed to address the worst-case SAR maximization problem. Comprehensive numerical simulations validate the effectiveness of our proposed algorithms and their significant performance advantages over benchmark schemes. Additionally, the performance trade-off between communication and sensing is analyzed, which can provide valuable insights for future ISAC design endeavors.