Robust Waveform Design for Integrated Communication and Sensing

Integrated Sensing and Communications (ISAC), leveraging the inherent commonalities between communication and sensing technologies as well as the advantage of efficient spectrum resource utilization, has become a core key technology for next-generation wireless networks and radar systems. This paper proposes a robust dual-function waveform design method for ISAC. Aiming at the problem of channel uncertainty, an optimization problem is established by maximizing multi-user interference (MUI), and a robust channel model that covers the worst-case scenario is obtained by solving this problem, providing a reliable channel foundation for subsequent waveform design. On this basis, a multi-objective optimization framework is established, with the objective function of minimizing the weighted combination of MUI, waveform similarity, and Cramér-Rao Bound (CRB). Simultaneously, signal energy constraints are incorporated to meet the hardware transmit power limitation, realizing the coordinated optimization of communication and sensing performance. To tackle the two types of non-convex problems in the model, namely robust channel solution and waveform parameter optimization, a solution algorithm integrating the alternating optimization idea and Semi-Definite Relaxation (SDR) is proposed: the complex problem is decomposed through alternating iteration, and the SDR technology is used to handle non-convex constraints to reduce the solution complexity. Simulation results show that the dynamic trade-off between communication and sensing performance can be flexibly achieved by adjusting the weight factors, and the proposed method exhibits excellent performance under both robust channels and real channels, fully verifying its effectiveness and robustness.