Machine Learning Enhanced Near-Field Secret Key Generation for Extremely Large-Scale MIMO

The next generation of communication systems are expected to operate at high frequency bands such as millimetre wave (mmWave) and terahertz (THz) bands, and use extremely large-scale multiple-input-multiple-output (XL-MIMO). This brings a paradigm shift from far-field to near-field communications. In this paper, we investigate physical-layer key generation in near-field XL-MIMO communications and focus on the most challenging line-of-sight (LoS) propagation scenario. To be specific, we introduce artificial randomness to enhance secret key generation and enable theoretical analysis of secret key rate (SKR). We provide the zero-forcing (ZF) precoding solution that can null the received signal at the eavesdropper. We show that the ZF precoding leads to a low SKR in challenging scenarios of low transmit powers and small eavesdropping distances. To improve the SKR in these challenging scenarios, we propose a novel low-complexity machine learning-based beam focusing (MLBF) scheme. Simulation results show that the proposed MLBF scheme achieves a higher SKR than the benchmark methods.