Hybrid Maximum Likelihood Modulation Classification Using Multiple Radios
This work addresses SNR sensitivity in modulation classification for wireless communications, but it is incremental as it builds on existing ML and EM techniques.
The paper tackled the sensitivity of modulation classifiers to channel SNR by proposing a multi-radio framework with hybrid maximum likelihood and EM algorithm, achieving robustness and superiority over single-radio and moments-based methods in numerical results.
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.