# Kuiper test based modulation level classification under unknown frequency selective channels

## TL;DR

This paper introduces a novel modulation level classification method that combines a reduced-complexity Kuiper test with CMA-based channel mitigation, significantly improving accuracy in frequency selective channels.

## Contribution

It proposes a CMA-rcK technique that enhances modulation classification performance under unknown frequency selective channels, outperforming existing cumulant and blind equalizer methods.

## Key findings

- CMA-rcK outperforms state-of-the-art techniques in simulations.
- The method effectively mitigates channel distortion effects.
- Analytical results confirm improved classification accuracy.

## Abstract

In this paper, we address the problem of Identifying the modulation level of the received signal under an unknown frequency selective channel. The modulation level classification is performed using reduced-complexity Kuiper (rcK) test which utilizes the distribution of signal features such as magnitude of the received samples or phase difference in consecutive received samples. However, in frequency selective channels, these features are severely distorted resulting in a poor classification performance. We propose to use constant modulus algorithm (CMA) to mitigate the impact of the frequency selective channel on the signal feature. Simulation and analytical results show that the proposed CMA-rcK technique outperforms state-of-the-art cumulant-based technique as well as blind equalizer-based technique that uses Alphabet Matched Algorithm.