Deep learning techniques for enhanced security and privacy in 6G terrestrial–nonterrestrial network architecture
摘要
The seamless integration of terrestrial networks (TNs) and non-terrestrial networks (NTNs) is a cornerstone in achieving the vision of ubiquitous connectivity in 6G systems. This native integration of TN and NTN components enables global coverage, ultrareliable low-latency communications, and massive device connectivity, but it also presents significant challenges. These challenges include ensuring robust security in distributed architectures, safeguarding privacy during cross-domain data handling, and managing complex interference scenarios in shared and adjacent spectrum environments. Artificial intelligence (AI) serves as a transformative enabler in overcoming these challenges, providing advanced capabilities for real-time threat detection, adaptive authentication, privacy-preserving mechanisms, and dynamic spectrum management. By leveraging advanced techniques such as bidirectional long short-term memory (BiLSTM) networks, multi-agent reinforcement learning (MARL), and federated learning (FL), the proposed solutions enable robust anomaly detection, dynamic spectrum allocation, and scalable privacy-preserving model training. These techniques collectively improve key performance metrics, including a 30% enhancement in signal-to-interference-plus-noise ratio (SINR), over 90% spectrum efficiency, and a privacy budget of