Design of an M-Ary DLCSK Communication System Using Deep Transfer Learning

被引:2
作者
Mobini, Majid [1 ]
Herceg, Marijan [2 ]
Kaddoum, Georges [3 ,4 ]
机构
[1] Babol Noshirvani Univ Technol, Dept Elect & Comp Engn, Babol 4714871167, Iran
[2] Univ Osijek, Dept Commun, Fac Elect Engn Comp Sci & Informat Technol, Osijek 31000, Croatia
[3] Univ Quebec, Ecole Technol Super, Dept Elect Engn, Montreal, PQ H3C 1K3, Canada
[4] Lebanese Amer Univ, Cyber Secur Syst & Appl AI Res Ctr, Beirut 135053, Lebanon
来源
IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY | 2023年 / 4卷
关键词
Receivers; Chaotic communication; Synchronization; Artificial neural networks; Modulation; Bit error rate; Channel estimation; Chaos-based communications; computational complexity; constellation diagram; deep transfer learning; PHYSICAL-LAYER SECURITY; SIGNAL-DETECTION; MIMO; PERFORMANCE; CLASSIFICATION; APPROXIMATION; DEMODULATION; MODULATION; CHANNEL;
D O I
10.1109/OJCOMS.2023.3312050
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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 EbN0=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.
引用
收藏
页码:2318 / 2342
页数:25
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