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

• URL: http://arxiv.org/abs/2510.24763v1
• Date: Thu, 23 Oct 2025 13:41:00 GMT
• Title: Dual-Domain Deep Learning-Assisted NOMA-CSK Systems for Secure and Efficient Vehicular Communications
• Authors: Tingting Huang, Jundong Chen, Huanqiang Zeng, Guofa Cai, Georges Kaddoum,
• Abstract summary: This paper proposes a deep learning-assisted power domain non-orthogonal multiple access chaos shift keying (DL-NOMA-CSK) system for vehicular communications.<n>A deep neural network (DNN)-based demodulator is designed to learn intrinsic chaotic signal characteristics during offline training.<n>The proposed system achieves superior performance in terms of spectral efficiency (SE), energy efficiency (EE), bit error rate (BER), security, and robustness.
• Score: 36.359307639974524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition [49.955269674859004]
  This paper introduces PhyG-MoE (Physics-Guided Mixture-of-Experts), a framework designed to align model capacity with signal complexity.<n>Unlike static architectures, the proposed system employs a spectrum-based gating mechanism that routes signals based on their spectral feature entanglement.<n>A high-capacity TransNeXt expert is activated on-demand to disentangle complex features in saturated scenarios, while lightweight experts handle fundamental signals to minimize latency.
  arXiv  Detail & Related papers  (2026-01-19T07:57:52Z)
• Towards Ultra-Low Latency: Binarized Neural Network Architectures for In-Vehicle Network Intrusion Detection [0.0]
  This paper presents a lightweight intrusion detection technique based on Binarized Neural Networks (BNNs)<n>We develop hybrid binary encoding techniques to integrate non-binary features, such as message IDs and frequencies.<n>The proposed method, namely the BNN framework specifically optimized for in-vehicle intrusion detection combined with hybrid binary quantization techniques for non-payload attributes, demonstrates efficacy in both anomaly detection and multi-class network traffic classification.
  arXiv  Detail & Related papers  (2025-11-02T06:47:56Z)
• Resource Allocation in Hybrid Radio-Optical IoT Networks using GNN with Multi-task Learning [11.833896722352568]
  This paper addresses the problem of dual-technology scheduling in hybrid Internet of Things (IoT) networks that integrate Optical NeuralOWC and Radio Frequency (RF)<n>We propose a supervised multi-task learning architecture combining a two-stage Graph Embedding with Transformer (DGET) framework.<n>The proposed framework achieves near-optimal scheduling with over 90% classification accuracy, reduces computational complexity, and demonstrates higher robustness under partial channel observability.
  arXiv  Detail & Related papers  (2025-10-29T15:02:28Z)
• Joint Channel Estimation and Computation Offloading in Fluid Antenna-assisted MEC Networks [81.36647816787713]
  We propose an FA-assisted offloading framework to minimize the delay of channel estimation.<n>We show that the proposed system significantly reduces the accuracy under efficient communication.
  arXiv  Detail & Related papers  (2025-09-16T08:48:44Z)
• Large-Scale Model Enabled Semantic Communication Based on Robust Knowledge Distillation [45.347078403677216]
  Large-scale models (LSMs) can be an effective framework for semantic representation and understanding.<n>However, their direct deployment is often hindered by high computational complexity and resource requirements.<n>This paper proposes a novel knowledge distillation based semantic communication framework.
  arXiv  Detail & Related papers  (2025-08-04T07:47:18Z)
• Heterogeneous Secure Transmissions in IRS-Assisted NOMA Communications: CO-GNN Approach [7.2414248136307195]
  IRS-assisted NOMA communications have garnered significant research interest.<n>The passive nature of the IRS, lacking authentication and security protocols, makes these systems vulnerable to external eavesdropping.<n>This paper investigates secure transmissions in IRS-assisted NOMA systems with heterogeneous resource configuration in wireless networks to mitigate both external and internal eavesdropping.
  arXiv  Detail & Related papers  (2025-06-03T04:01:50Z)
• Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems [0.0]
  Cyberattacks on critical infrastructure, particularly water distribution systems, have increased due to rapid digitalization.<n>This study addresses key challenges in anomaly detection by leveraging time correlations in sensor data.<n>We propose a hybrid autoencoder-based approach, referred to as hybrid TDC-AE, which extends TDC by incorporating both deterministic nodes and conventional statistical nodes.
  arXiv  Detail & Related papers  (2025-04-08T09:22:44Z)
• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
  Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks.<n>Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance.<n>We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
  arXiv  Detail & Related papers  (2025-01-20T04:26:21Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles [52.79089414630366]
  We develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL) We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
  arXiv  Detail & Related papers  (2020-07-27T12:38:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.