Programming Wireless Security Through Learning‐Aided Spatiotemporal Digital Coding Metamaterial Antenna

Abstract

The advancement of future large‐scale wireless networks necessitates the development of cost‐effective and scalable security solutions. Specifically, physical layer (PHY) security has been put forth as a cost‐effective alternative to cryptographic mechanisms that can circumvent the need for explicit key exchange between communication devices. Herein, a space–time‐modulated digitally‐coded metamaterial (MTM) leaky wave antenna (LWA) is proposed that can enable PHY security by achieving the functionalities of directional modulation (DM) using a machine learning‐aided branch‐and‐bound (B&B) optimized coding sequence. Theoretically, it is first shown that the proposed space–time MTM antenna can achieve DM through both the spatial and spectral manipulation of the orthogonal frequency division multiplexing signal. Simulation results are then provided as proof‐of‐principle, demonstrating the applicability of the approach for achieving DM in various communication settings. Furthermore, a prototype of the proposed architecture controlled by a field‐programmable gate array is realized, which achieves DM via an optimized coding sequence carried out by the learning‐aided B&B algorithm corresponding to the states of the MTM LWA's unit cells. Experimental results confirm the theory behind the space–time‐modulated MTM LWA in achieving DM, which is observed via both the spectral harmonic patterns and bit error rate measurements.

Keywords

• directional modulation
• Internet-of-Things
• metamaterial antennas
• physical layer security
• wireless communication