Dual-Domain Deep Learning-Assisted NOMA-CSK Systems for Secure and Efficient Vehicular Communications

2025-10-23

AI Summary

To address low spectral efficiency, poor scalability, and high synchronization overhead in multi-user chaotic communication for vehicular networks, this paper proposes a deep learning–driven non-orthogonal chaotic shift keying (NOMA-CSK) system. The method introduces a time-frequency dual-domain feature extraction neural network for reference-free, end-to-end demodulation; integrates power-domain NOMA with chaotic shift keying; and incorporates deep learning–assisted successive interference cancellation (SIC) to effectively suppress error propagation. Crucially, the scheme eliminates conventional chaotic synchronization, thereby significantly improving spectral and energy efficiency while enhancing robustness under dynamic channel conditions and supporting scalable user access. Experimental results demonstrate that the proposed system achieves lower bit error rates than both conventional differential chaotic shift keying (DCSK) and state-of-the-art deep learning–based approaches, with reduced computational complexity. Its feasibility is further validated in real-world vehicular environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Learning & Optimization for CV
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Ensuring secure and efficient multi-user (MU) transmission is critical for vehicular communication systems. Chaos-based modulation schemes have garnered considerable interest due to their benefits in physical layer security. However, most existing MU chaotic communication systems, particularly those based on non-coherent detection, suffer from low spectral efficiency due to reference signal transmission, and limited user connectivity under orthogonal multiple access (OMA). While non-orthogonal schemes, such as sparse code multiple access (SCMA)-based DCSK, have been explored, they face high computational complexity and inflexible scalability due to their fixed codebook designs. This paper proposes a deep learning-assisted power domain non-orthogonal multiple access chaos shift keying (DL-NOMA-CSK) system for vehicular communications. A deep neural network (DNN)-based demodulator is designed to learn intrinsic chaotic signal characteristics during offline training, thereby eliminating the need for chaotic synchronization or reference signal transmission. The demodulator employs a dual-domain feature extraction architecture that jointly processes the time-domain and frequency-domain information of chaotic signals, enhancing feature learning under dynamic channels. The DNN is integrated into the successive interference cancellation (SIC) framework to mitigate error propagation issues. Theoretical analysis and extensive simulations demonstrate that the proposed system achieves superior performance in terms of spectral efficiency (SE), energy efficiency (EE), bit error rate (BER), security, and robustness, while maintaining lower computational complexity compared to traditional MU-DCSK and existing DL-aided schemes. These advantages validate its practical viability for secure vehicular communications.

Problem

• Enhancing spectral efficiency and user connectivity in vehicular communication systems
• Reducing computational complexity and improving flexibility in chaotic modulation schemes
• Achieving secure multi-user transmission without chaotic synchronization or reference signals

Innovation

• Deep learning demodulator eliminates chaotic synchronization needs
• Dual-domain architecture extracts time-frequency chaotic signal features
• DNN integrated SIC framework reduces error propagation issues