Learning Nonlinear Waveform Fusion Across Scales for Integrated Sensing and Communication
TL;DR
This work compares the proposed waveforms with superposition in the time domain and learned spectral linear fusion on 512 independent test images, using a shared fixed UE receiver and a baseline sensing receiver using coherent peak search.
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. At the base station, the realized transmit pulse defines a coherent dictionary for sequential localization with learned local correction, physical refinement, and residual updates. We compare the proposed waveforms with superposition in the time domain and learned spectral linear fusion on 512 independent test images, using a shared fixed UE receiver and a baseline sensing receiver using coherent peak search. Averaged across eleven task weights, the proposed waveforms reduce normalized localization error by 29.0\% and 27.0\%, respectively, while increasing mean peak signal-to-noise ratio by 1.40 and 0.82~dB. With the proposed waveforms fixed, learned local correction reduces mean normalized localization error by 12.2\% relative to the same sequential receiver without learned correction.
