Learning Nonlinear Waveform Fusion Across Scales for Integrated Sensing and Communication

arXiv:2610.32099v1 Announce Type: new Abstract: Fixed source superposition and frequency partitioning constrain how communication content interacts with a probing reference in integrated sensing and communication (ISAC). We learn nonlinear waveform fusion across scales for image transmission and localization of multiple targets in range and angle. Reception uses the information available at each end. Adaptive temporal feature weighting and spectral updates combine image features and a deterministic linearly frequency-modulated reference in a shared complex state, generating a waveform with fixed total energy over the array frame. At the user equipment (UE), the reference and channel state information condition a neural demixer for image reconstruction.