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Design of an M-Ary DLCSK communication system using deep transfer learning


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Mobini, M., Herceg, M. and 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.

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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.

Item Type: Peer reviewed article published in a journal
Kaddoum, Georges
Affiliation: Génie électrique
Date Deposited: 03 Oct 2023 14:46
Last Modified: 23 Nov 2023 15:18

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