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Communication Dans Un Congrès Année : 2022

Achievable Rates and Machine Learning Detection of Faster than Nyquist Spectrally Efficient FDM

Résumé

In this work we compare the capacity and achievable rate of uncoded faster than Nyquist (FTN) signalling in the frequency domain, also referred to as spectrally efficient FDM (SEFDM). We propose a deep residual convolutional neural network detector for SEFDM signals in additive white Gaussian noise channels, that allows to approach the Mazo limit in systems with up to 60 subcarriers. Notably, the deep detectors achieve a loss less than 0.4 − 0.7 dB for uncoded QPSK SEFDM systems of 12 to 60 subcarriers at a 15% spectral compression.
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Dates et versions

hal-04273917 , version 1 (07-11-2023)

Identifiants

  • HAL Id : hal-04273917 , version 1

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Arsenia Chorti, David Picard. Achievable Rates and Machine Learning Detection of Faster than Nyquist Spectrally Efficient FDM. XXVIIIème Colloque Francophone de Traitement du Signal et des Images -GRETSI 2022, Sep 2022, Nancy, France. ⟨hal-04273917⟩
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