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

Keyphrases

• Vehicular Ad Hoc Networks 100%
• Dual-domain Learning 100%
• Deep Learning 66%
• Chaotic Signal 66%
• Spectral Efficiency 66%
• Deep Neural Network 66%
• Differential Chaos Shift Keying 66%
• Reference Signal 66%
• Multi-user 66%
• Demodulator 66%
• Bit Error Rate 33%
• Successive Interference Cancellation 33%
• Feature Extraction 33%
• Energy Efficiency 33%
• Feature Learning 33%
• Computational Complexity 33%
• Domain-independent 33%
• Superior Performance 33%
• Error Propagation 33%
• Physical Layer Security 33%
• Efficiency Energy 33%
• Chaos-based 33%
• Chaotic Synchronization 33%
• Non-coherent Detection 33%
• Signal Enhancement 33%
• Synchronization Signal 33%
• Signal Transmission 33%
• Chaos Shift Keying 33%
• Multiuser Transmission 33%
• Codebook Design 33%
• Sparse Code multiple Access 33%
• Online Training 33%
• Intrinsic Attributes 33%
• Chaotic Communication System 33%
• Non-orthogonal 33%
• Dynamic Channel Conditions 33%
• Modulation Scheme 33%
• Dual-domain 33%
• Fixed Codebook 33%
• Secure Vehicular Communication 33%
• User Connectivity 33%
• Neural Network-based 33%
• Power-domain NOMA 33%
• Practical Viability 33%
• Orthogonal multiple Access 33%
• Enhanced Features 33%
• Error Robustness 33%
• Frequency Domain Information 33%

Computer Science

• Deep Learning Method 100%
• Vehicular Communication 100%
• Non-Orthogonal Multiple Access 100%
• Chaotic Signal 66%
• Deep Neural Network 66%
• Spectral Efficiency 66%
• Reference Signal 66%
• Multiple Access 66%
• Frequency Domain 33%
• Channel Condition 33%
• Feature Extraction 33%
• Representation Learning 33%
• Computational Complexity 33%
• Superior Performance 33%
• Energy Efficiency 33%
• Physical Layer Security 33%
• Coherent Detection 33%
• Domain Information 33%
• Domain Feature 33%
• Intrinsic Property 33%
• Synchronism 33%
• Vehicular Communication System 33%