Data-Driven Modulation and Antenna Classification of Wireless Digital Communication Signals

TL;DR

This paper introduces data-driven machine learning methods to classify the number of antennas and modulation schemes of wireless signals without prior knowledge, achieving high accuracy and enabling deeper transmitter analysis.

Contribution

It presents novel ML-based techniques for joint and independent classification of antenna count and modulation type in wireless signals, without requiring prior system information.

Findings

High classification accuracy achieved

Effective joint and independent classification schemes demonstrated

Approach applicable across various system parameters

Abstract

In this paper we are interested to learn from a wireless digitally modulated signal the number of antennas that the transmitter (Tx) of this signal uses, as well as its specific modulation scheme (from phase-shift keying (PSK) or quadrature amplitude modulation (QAM)). Formally, these are modulation and antenna classification problems. We examine the problems with data-driven machine learning (ML)-based techniques. The two sub-problems of modulation and number of transmitter antenna classification are initially examined independently for a variety for system parameters, namely the SNR, number of receiver (Rx) antennas, and classification algorithms. Then we consider the joint problem where we follow two approaches. One, where the sub-problems are solved independently and in parallel, and one where the antenna classifier waits on the result of the modulation classifier. The two proposed schemes do not require any knowledge/details of the used modulation schemes and the way the Tx antennas are used (spatial multiplexing, space-time codes,etc.) as it is fully data-driven and not decision-theoretic based. The results of our approach are characterized by high classification accuracy and they pave the way for more ML-based data-driven techniques that reveal more characteristics of the Tx.