Deep Chaos Synchronization

Mobini, Majid; Kaddoum, Georges

doi:10.1109/ojcoms.2020.3028554

Cited by 9 publications

(10 citation statements)

References 78 publications

(91 reference statements)

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“…As a consequence, ref. [ 34 ] recovered the image signals through stability analyses and deep learning methods. From the perspective of synchronization, adaptive synchronization [ 35 ] and impulsive synchronization [ 36 ] were applied to the security of networks and image encryption, respectively.…”

Section: Introductionmentioning

confidence: 99%

Projective Synchronization of Delayed Uncertain Coupled Memristive Neural Networks and Their Application

Han,

Chen,

Wei

et al. 2023

Entropy

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In this article, the authors analyzed the nonlinear effects of projective synchronization between coupled memristive neural networks (MNNs) and their applications. Since the complete signal transmission is difficult under parameter mismatch and different projective factors, the delays, which are time-varying, and uncertainties have been taken to realize the projective synchronization of MNNs with multi-links under the nonlinear control method. Through the extended comparison principle and a new approach to dealing with the mismatched parameters, sufficient criteria have been determined under different types of projective factors and the framework of the Lyapunov–Krasovskii functional (LKF) for projective convergence of the coupled MNNs. Instead of the classical treatment for secure communication, the concept of error of synchronization between the drive and response systems has been applied to solve the signal encryption/decryption problem. Finally, the simulations in numerical form have been demonstrated graphically to confirm the adaptability of the theoretical results.

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Section: Introductionmentioning

confidence: 99%

Projective Synchronization of Delayed Uncertain Coupled Memristive Neural Networks and Their Application

Han,

Chen,

Wei

et al. 2023

Entropy

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“…To improve the energy efficiency, the differentially DCSK (DDCSK) and the short reference DCSK (SR-DCSK) have been investigated in [21], [22], respectively. Moreover, [23], a deep learning (DL)-based demodulation was proposed for DCSK systems, which is efficient for applications with problematic channel estimation. For secure communications, a reconfigurable intelligent surface (RIS)-aided M -ary D-CSK with block interleaving (RIS-MDCSK-BI) system is proposed, where a pair of block interleaving patterns are used to enhance security performance [24].…”

Section: Introductionmentioning

confidence: 99%

Design of Permutation Index DCSK With Noise Reduction for Short-Range IoT Communications

Wang,

Chen,

Xie

et al. 2023

IEEE Access

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In this paper, we propose a permutation index differential chaos shift keying with the noise reduction (NR-PI-DCSK) to alleviate the negative effect of the additive noise and fading interference. Instead of sending a β-length chaotic sequence as the reference in the conventional PI-DCSK, the transmitter of NR-PI-DCSK generates β/R chaotic samples and repeats R times, which serve as the reference sequence. The permuted copy of the reference is used to carry the data given the predefined permutation index. After the correlation and averaging of these R chaotic segments at the receiver, the variance of noise and fading can be greatly reduced and the performance is improved. Then, we derive the analytical BER expressions for the multipath fading channels, which are verified by the consistent simulations. The results show that the proposed NR-PI-DCSK can achieve up to 3 dB performance gain over the conventional PI-DCSK in different wireless fading channels. In addition, the performance advantage of the proposed NR-PI-DCSK is also demonstrated in a typical UWB channel by simulations. More importantly, the proposed scheme maintains almost the same low transceiver complexity as the conventional ones, since it only requires the correlators. Thus, the NR-PI-DCSK can be seen as a promising candidate for low-cost short-range IoT communications.INDEX TERMS Differential chaos shift keying (DCSK), permutation index (PI), noise reduction (NR), performance analysis.

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“…This level of BER performance can be achieved only if the cross-correlation problem is solved and the basis functions can be recovered successfully. However, since complete synchronization is still complicated to attain in realistic environments, this theoretical performance is not practically attainable [6]. If it is not possible to recover the basis functions, the DCSK scheme [7] may provide better performance.…”

Section: Introductionmentioning

confidence: 99%

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

Mobini,

Herceg,

Kaddoum

2023

IEEE Open J. Commun. Soc.

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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 E b /N 0 = 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.

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Deep Chaos Synchronization

Cited by 9 publications

References 78 publications

Projective Synchronization of Delayed Uncertain Coupled Memristive Neural Networks and Their Application

Projective Synchronization of Delayed Uncertain Coupled Memristive Neural Networks and Their Application

Design of Permutation Index DCSK With Noise Reduction for Short-Range IoT Communications

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

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