Deep Learning-Empowered Secure Predictive Beamforming Design for Integrated Sensing and Communications Systems

In the era of upcoming sixth-generation (6G) wireless systems, the intelligent integrated sensing and communication (ISAC) paradigm has emerged as a pivotal research domain, catalyzing advancement across a wide range of applications. In this paper, we investigate an ISAC-assisted anti-eavesdropping communication system, where an ISAC ground base station exploits its radar function to track potential aerial eavesdroppers and implements predictive beamforming to ensure secure communications with multiple ground users. We harness the powerful capability of the Transformer for time series prediction to establish a novel deep neural network, termed the ISACformer, for constructing predictive beamformers via exploiting previously estimated channel state information in an unsupervised manner. By eliminating the need for explicit channel prediction, our proposed framework effectively reduces signaling overhead and complexity. In addition, by formulating a weighted objective function, our design meticulously balances the trade-off between the ergodic achievable worst-case secrecy rate for ground users and the ergodic Cramér-Rao lower bound for the kinematic parameters of potential aerial eavesdroppers. Simulation results demonstrate that the proposed ISACformer can deliver the desired predictive beamforming for harmonizing radar and communication functionalities effectively. Moreover, our method achieves performance approaching the theoretical upper bound obtained by ignoring multi-user interference, thereby highlighting the robustness of the proposed approach.