Mobini, M., Herceg, M. et Kaddoum, G..
2023.
« Design of an M-Ary DLCSK communication system using deep transfer learning ».
IEEE Open Journal of the Communications Society, vol. 4.
pp. 2318-2342.
Compte des citations dans Scopus : 1.
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Résumé
Conventional coherent chaos-based communication systems require synchronization of chaotic signals, which is still practically unattainable in a noisy environment. Moreover, in non-coherent schemes, a part of the bit duration is spent sending non-information-bearing reference samples, which deteriorates the Bit Error Rate performance (BER) of these systems. To tackle these problems, this paper designs an M-ary Deep Learning Chaos Shift Keying (M-ary DLCSK) system. The proposed receiver uses a Convolutional Neural Network (CNN)-based classifier that recovers M-ary modulated data. The trained NN model grasps different chaotic maps, estimates channels, and classifies the received signals effectively. Moreover, we consider a Transfer Learning (TL) framework that enhances the noise performance and classification results. Due to the generalization capabilities of TL, the trained NN can work in different Signal-to-Noise Ratio (SNR) conditions without the need for re-training. We compare the BER performance, complexity, and bandwidth efficiency of the M-ary DLCSK receiver with existing receivers. The results demonstrate that the M-ary DLCSK receiver is the first practical system that achieves the theoretical BER performance of the coherent CSK systems under Rayleigh fading channels. Moreover, the proposed system provides a considerable performance advantage compared to the existing DL-based receivers under Rayleigh fading channels. For example, the BER performance of 8-ary DLCSK shows a gain of 0.1 over the Long Short-Term Memory (LSTM)-aided DNN systems at the target Eb/N0 = 14dB. These features make M-ary DLCSK an attractive candidate for several applications, such as Massive Multiple-Input Multiple Output (MIMO), Vehicle-to-everything (V2X), Quantum, and optical communication systems.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Kaddoum, Georges |
| Affiliation: | Génie électrique |
| Date de dépôt: | 03 oct. 2023 14:46 |
| Dernière modification: | 23 nov. 2023 15:18 |
| URI: | https://espace2.etsmtl.ca/id/eprint/27889 |
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