Secure Initial Access and Beam Alignment Using Deep Learning in 5G and Beyond Systems

Abstract

5G and beyond networks will require fast, energy efficient, and secure initial access. In this study, a deep learning-based secure initial beam selection method is proposed that ranks the beam pairs between a transmitter and a legitimate user aiming to maximize the signal strength the user receives, while keeping the signal strength that the eavesdropper sees below a threshold. Instead of an exhaustive search, the initial beam selection is performed over a limited number of the top beam pairs, leading to reduced communication overhead and energy consumption. The proposed scheme is evaluated using data obtained from a real-life mobile network topology as well as a synthetic data set based on the same geographical site but with statistical system-level environment variables. Utilizing a multi-layer perceptron model, the neural network takes receiver locations as input and produces a ranked list of beam pairs between transmitter and receiver based on the specified coverage criteria. Numerical results show that the signalling overhead can be reduced by 75% with 99.66% accuracy in terms of the best beam pair, and 99.89% of the achievable signal strength. In terms of security, the proposed method has been shown to improve secure coverage probability by 68.12% compared to the best-coverage beam selection scenario.