ASTRA-Net: Adaptive Spectro-Temporal Robust Architecture for Spectrum Sensing

The letter proposes an adaptive spectro-temporal robust architecture network (ASTRA-Net), a cutting-edge deep framework for spectrum sensing (SS) in radar and next-generation wireless networks. To overcome the limitations of existing methods in spectrally congested and low signal-to-noise ratio (SNR) environments, ASTRA-Net introduces a dual-path design combining a Dynamic Selective Spectrum Layer (DSSL) for adaptive multi-scale receptive fields, a Pathwise Spatial Layer (PSL) for robust context-aware feature extraction, a Perceptive Spectrum Manifold (PSM) for spectro-temporal decomposition, and an Adaptive Spectrum Distiller (ASD) to enrich global context. Through the coordinated integration of these modules, ASTRA-Net enables precise segmentation of fifth-generation new radio (5G NR), long-term evolution (LTE), and radar signals across diverse noisy environments. Extensive experiments demonstrate that ASTRA-Net achieves 98.65% global accuracy and 97.52% mean IoU with only 6.6M parameters and 2.36 ms inference latency, significantly outperforming recent deep models in both efficiency and robustness.