# Hybrid Maximum Likelihood Modulation Classification Using Multiple Radios

arXiv: https://arxiv.org/abs/1303.0775 · HTML: https://mlsanity.com/paper/1303.0775/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2013.

## Built on by (most cited first, 1 of 1)

- [Modulation Classification Through Deep Learning Using Resolution Transformed Spectrograms](https://mlsanity.com/paper/2306.04655/) (2023)

## Abstract (arXiv)

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.