Waveform design is a critical technology for orthogonal time-frequency space (OTFS) integrated sens-ing and communication (ISAC) systems. Existing research mainly focuses on the design of multiple-input multiple-output (MIMO)-OTFS communication waveforms or the design of dual-functional OTFS waveforms for single-input single-output (SISO) systems, which results in degraded per-formance in dynamic, multi-user ISAC scenarios. To address these limitations, the paper mainly investigates waveform op-timization for MIMO-OTFS ISAC systems, which formulates the problem as minimizing the weighted integrated sidelobe level (WISL) under peak-to-average power ratio (PAPR) and multi-user interference (MUI) energy constraints. Due to its non-convex nature with multiple constraints, the problem is challenging to solve. To address the complicated optimization problem, an adaptive penalty analytic subproblem decompo-sition (APASD) method is proposed. First, auxiliary variables are introduced to decompose the original problem into several subproblems with analytic solutions. Then, leveraging the structure of the subproblems, we derive the corresponding analytic solutions. Finally, the penalty parameters are adap-tively updated based on the residuals, and the overall problem is iteratively refined until convergence. Simulation results demonstrate that the proposed method can achieve a lower sidelobe level while ensuring the achievable sum-rate, and shows better performance than existing methods.

The design of orthogonal multiple-input multiple-output (MIMO) radar waveforms under spectral constraints is of great importance. Most existing methods focus on optimiz-ing the integrated sidelobe level (ISL), which often results in waveforms with high peak sidelobes. We observe that by approximating the peak sidelobe level (PSL) through a softmax-based differentiable formulation and leveraging the powerful nonlinear modeling ability of deep learning (DL), effective PSL optimization can be achieved. Moreover, we observe that the powerful nonlinear fitting capability of DL enables parallel training for multi-scenario waveform genera-tion. Based on these insights, we propose the cross-attention residual enhancement network (CARE-Net), a network that in-tegrates residual structures with cross-attention mechanisms. The proposed CARE-Net is designed to jointly minimize range-PSL & ISL under spectral constraints and is flexible to support waveform design in various scenarios. Simulation results demonstrate that (1) the proposed method achieves approximately 2–5 dB peak sidelobe reduction compared to existing methods; (2) existing methods can only design a single waveform for a specific scenario, whereas our proposed method enables the simultaneous design of waveform sets for multiple scenarios.