Year: 2022
Type: preprint
Abstract: As wireless network technology becomes more and more popular, mutual interference between various signals has become more and more severe and common. Therefore, there is often a situation in which the transmission of its own signal is interfered with by occupying the channel. Especially in a confrontational environment, Jamming has caused great harm to the security of information transmission. So I propose ML-based secure ultra-low power communication, which is an approach to use machine learning to predict future wireless traffic by capturing patterns of past wireless traffic to ensure ultra-low-power transmission of signals via backscatters. In order to be more suitable for the adversarial environment, we use backscatter to achieve ultra-low power signal transmission, and use frequency-hopping technology to achieve successful confrontation with Jamming information. In the end, we achieved a prediction success rate of 96.19%.
Source: arXiv (Cornell University)
Authors: Song, Guanqun, Zhu, Ting
Institution: The Ohio State University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4313305567