Machine Learning Based Antenna Design for Physical Layer Security in Ambient Backscatter Communications

Abstract

Ambient backscatter employs existing radio frequency (RF) signals in the environment to support sustainable and independent communications, thereby providing a new set of applications that promote the Internet of Things (IoT). However, nondirectional forms of communication are prone to information leakage. In order to ensure the security of the IoT communication system, in this paper, we propose a machine learning based antenna design scheme, which achieves directional communication from the relay tag to the receiving reader by combining patch antenna with log-periodic dual-dipole antenna (LPDA). A multiobjective genetic algorithm optimizes the antenna side lobe, gain, standing wave ratio, and return loss, with a goal of limiting the number of large side lobes and reduce the side lobe level (SLL). The simulation results demonstrate that our proposed antenna design is well suited for practical applications in physical layer security communication, where signal-to-noise ratio of the wiretap channel is reduced, communication quality of the main channel is ensured, and information leakage is prevented.

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

“…Reducing the side lobe level is therefore crucial to preventing eavesdropping. To this end, an ML-based antenna design scheme was proposed in [140] that achieved directional communication between transceivers by combining patch antenna with Log Periodic Dual-dipole Antenna (LPDA). Aiming to limit the number of large side lobes and reduce the Side Lobe Level (SLL), a multi-objective genetic algorithm was proposed to optimize the antenna side lobe, gain, standing wave ratio, and return loss.…”

Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks

“…The physical layer security of backscatter tags is another important issue [45]. To address this using machine learning techniques, the authors of [46] used a multiobjective genetic algorithm. More specifically, they reduced the antenna side lobes and obtained optimal Pareto fronts.…”

Application of Machine Learning in Electromagnetics: Mini-Review

“…In another study [86], ANN was used to estimate beam alignment and distribution without prior knowledge of user location information. Hong et al [87] described a directional antenna design scheme using multi-objective GA to reduce the number of antenna side lobes and the lobe level, which eventually aided directivity and security in wireless communication. In a prior report [88], GPR was used for computing the resonant frequency of a square microstrip patch antenna.…”