Enhancing Physical Layer Security in Millimeter Wave Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing Networks

Millimeter‐Wave (mmWave) massive Multiple Input Multiple Output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) is a key enabler of next‐generation wireless communication, providing high spectral efficiency and improved data rates. Massive MIMO utilizes a large number of antennas at the base station to enhance spatial diversity, while OFDM mitigates inter‐symbol interference and enhances spectral efficiency. The combination of mmWave and massive MIMO with OFDM enables high‐capacity communication with reduced latency, making it ideal for 5G and beyond networks. Physical Layer Security (PLS) provides a robust alternative to traditional cryptographic techniques, leveraging information‐theoretic principles to counter advanced eavesdropping threats, including quantum computing attacks. However, conventional PLS key generation methods suffer from reduced secrecy due to the slow variation of channel characteristics. To enhance security, a deep learning‐based key generation approach is proposed using a deep graph convolutional neural network to extract virtual angle of arrival and angle of departure features. The proposed method achieves a bit agreement ratio of 0.5767 and a key generation rate of 1856.59 bits/s, significantly improving key secrecy. Secure data transmission is ensured using a hybrid deep Q‐learning with recurrent neural network framework, incorporating on‐off transmission schemes, which enhances security by achieving a secrecy capacity of 5.37 bps/Hz, maintaining a throughput of 119.55 Mbps, and a latency of 8.00 ms, ensuring scalability. Additionally, evaluations of the secrecy rate of 5.37 bps/Hz, secrecy outage probability of 10 −2 , and 95% Eavesdropper interception capability, demonstrate significant improvements over state‐of‐the‐art techniques.