A CNN modulator jointly trained with a neural receiver spreads information across local time-frequency neighborhoods in OFDM, breaking QAM rotational symmetry to support sparse or zero pilots under high Doppler.
One-bit ofdm receivers via deep learning,
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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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Deep-OFDM: Neural Modulation for High Mobility
A CNN modulator jointly trained with a neural receiver spreads information across local time-frequency neighborhoods in OFDM, breaking QAM rotational symmetry to support sparse or zero pilots under high Doppler.
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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.