Novel Training Methodology to Enhance Deep Learning Based Modulation Classification

Sathyanarayanan, Venkatesh; Jolly, A.J.; Gerstoft, Peter

doi:10.1109/ieeeconf53345.2021.9723186

Cited by 4 publications

(1 citation statement)

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“…To recognize transmission type precisely, the modulation of OFDM signals should be determined. However, the modulation classifier for single-carrier signals [5,6] than taking raw time IQ samples as an input for proper modulation classification of OFDM signals.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Modulation Classification for OFDM Systems Without Symbol-Level Synchronization

Kim

Sathyanarayanan

Mecklenbräuker

et al. 2023

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)

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Deep learning (DL)-based modulation classification of incoherently received orthogonal frequency division multiplexing (OFDM) signals is studied. We propose a novel preprocessing algorithm to build features characterizing the modulation of OFDM signals, which are insensitive to synchronization error. With obtained features, pilot subcarrier indices used for CFO correction may also be estimated. The features obtained with the proposed algorithm are classified with a convolutional neural network (CNN)-based classifier. We have evaluated classification performance with simulated and hardwaregenerated data. Using these features, the modulation classifier outperforms existing DL-based classifiers which assume symbol-level synchronization with up to 25% classification accuracy performance gain.

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Section: Introductionmentioning

confidence: 99%