Mod-CL uses intra-instance modulation consistency to form positive pairs from temporal signal segments in a tailored contrastive objective, outperforming baselines on RadioML datasets especially in low-label regimes.
An introduction to deep learning for the physical layer
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A speculative DL classifier validated by GLRT on spatially robust second-order statistics provides adversarially resilient array processing.
Deep learning receivers enable reliable FTN signaling with up to 75% spectral compression via sliding-window detection while maintaining low latency and robustness to channel variations.
citing papers explorer
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Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification
Mod-CL uses intra-instance modulation consistency to form positive pairs from temporal signal segments in a tailored contrastive objective, outperforming baselines on RadioML datasets especially in low-label regimes.
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A Speculative GLRT-Backed ApproachRobust Deep Learning-Based Array Processing
A speculative DL classifier validated by GLRT on spatially robust second-order statistics provides adversarially resilient array processing.
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Neural Equalisers for Highly Compressed Faster-than-Nyquist Signalling: Design, Performance, Complexity and Robustness
Deep learning receivers enable reliable FTN signaling with up to 75% spectral compression via sliding-window detection while maintaining low latency and robustness to channel variations.