Machine learning classifiers for signal detection in ambient backscatter communication under time-selective fading

Abstract

In the world of the Internet of Things (IoT), ambient backscatter communication, or AmBaC, is a cutting-edge technology that has attracted a lot of interest. It meets the requirement for low-power communication in IoT devices, which are often located in a multitude of settings and scenarios where power efficiency is essential. In the literature, most signal detection is carried out using an energy detector or Minimum Mean Square Error (MMSE) detector under static conditions. In this work, we perform signal detection using multiple supervised machine learning (ML) classifiers, namely random forest, K-nearest neighbors (KNN), and support vector machine (SVM), and we examine the system performance in terms of bit error rate (BER) for both stationary and moving radio-frequency (RF) source. The simulation findings demonstrate that the proposed supervised ML classifiers surpass the conventional signal detector. In particular, the performance of the SVM classifier is best among all the other ML classifiers. Additionally, we examined the performance of different kernels of the SVM classifier and discovered that the Gaussian kernel offers the highest performance when it comes to SVM classifier performance.