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

Ensuring secure and efficient multi-user (MU) transmission is critical for vehicular communication systems. Chaos-based modulation schemes have garnered considerable interest owing to their inherent advantages 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 overhead and limited user connectivity under orthogonal multiple access (OMA). Although non-orthogonal schemes such as sparse code multiple access (SCMA)-based differential chaos shift keying (DCSK) have been explored, they incur high computational complexity and exhibit inflexible scalability owing to 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 the intrinsic characteristics of chaotic signals 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 time-domain and frequency-domain information of the received chaotic signals, enhancing feature learning under dynamic channel conditions. The DNN is integrated into a successive interference cancellation (SIC) framework to mitigate error propagation. 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 compared to conventional MU-DCSK and existing deep learning-aided schemes. These advantages confirm the practical viability of the proposed system for secure vehicular communications.