ABAE-RTN: A Deep Learning Framework for Robust Physical Layer Security in Radio Transformer Networks

This research presents ABAE-RTN, a deep learning framework that improves security in wireless radio networks by using adaptive beamforming (technology that focuses radio signals toward intended receivers) and autoencoders (neural networks that learn to compress and reconstruct data) to protect against eavesdropping. The system adds artificial noise to disrupt attackers while maintaining communication quality, and adjusts its signal patterns in real time to handle changing channel conditions. Testing shows it outperforms other AI approaches like LSTM (long short-term memory, a type of neural network good at processing sequences) in protecting wireless communications.