Hybrid Maximum Likelihood Modulation Classification Using Multiple Radios

TL;DR

This paper introduces a robust multi-radio modulation classification framework using hybrid maximum likelihood estimation and the EM algorithm, improving accuracy over single-radio methods especially under varying SNR conditions.

Contribution

It presents a novel multi-radio data fusion approach with EM-based ML estimation for amplitude-phase modulation classification, enhancing robustness and reducing computational complexity.

Findings

Outperforms single-radio classifiers in accuracy

Provides robustness against SNR variations

Demonstrates effectiveness through numerical simulations

Abstract

The performance of a modulation classifier is highly sensitive to channel signal-to-noise ratio (SNR). In this paper, we focus on amplitude-phase modulations and propose a modulation classification framework based on centralized data fusion using multiple radios and the hybrid maximum likelihood (ML) approach. In order to alleviate the computational complexity associated with ML estimation, we adopt the Expectation Maximization (EM) algorithm. Due to SNR diversity, the proposed multi-radio framework provides robustness to channel SNR. Numerical results show the superiority of the proposed approach with respect to single radio approaches as well as to modulation classifiers using moments based estimators.