Optimal Discriminant Functions Based On Sampled Distribution Distance for Modulation Classification
This work addresses modulation classification in signal processing, offering an incremental improvement over prior distribution-based approaches.
The authors derived optimal discriminant functions for modulation classification using sampled distribution distance, achieving asymptotically minimum classification error with optimized testpoints and significant gains over existing methods.
In this letter, we derive the optimal discriminant functions for modulation classification based on the sampled distribution distance. The proposed method classifies various candidate constellations using a low complexity approach based on the distribution distance at specific testpoints along the cumulative distribution function. This method, based on the Bayesian decision criteria, asymptotically provides the minimum classification error possible given a set of testpoints. Testpoint locations are also optimized to improve classification performance. The method provides significant gains over existing approaches that also use the distribution of the signal features.