MAC Protocol Identification Using Convolutional Neural Networks

Abstract

Making network nodes aware of the spectrum parameters can help to improve the spectrum utilization and network efficiency. To achieve such goals, machine learning (ML) and deep learning (DL) have been utilized to identify spectrum parameters, such as modulation formats, power levels, medium access control (MAC) protocols, etc. This paper explores MAC protocol identification using ML and DL in additive white Gaussian noise (AWGN) and Rayleigh fading environments. We transform the received signals into spectrogram and utilize convolutional neural networks (CNN) to recognize the MAC protocols. Experimentation results demonstrate the effectiveness in MAC protocol identification using ML and DL algorithms.