Channel-robust Automatic Modulation Classification Using Spectral Quotient Cumulants

TL;DR

This paper introduces a spectral quotient cumulant classification (SQCC) framework for automatic modulation classification in OFDM systems, demonstrating robustness against multipath fading channels and outperforming existing methods.

Contribution

The paper proposes a novel channel-robust modulation classification method using spectral quotient cumulants and an outlier detector, improving accuracy under multipath fading.

Findings

Achieves nearly 90% classification accuracy at 4dB SNR under multipath channels.

Demonstrates robustness and superiority over existing methods in fading environments.

Effective in scenarios where training is done on AWGN channels but testing on multipath channels.

Abstract

Automatic modulation classification (AMC) is to identify the modulation format of the received signal corrupted by the channel effects and noise. Most existing works focus on the impact of noise while relatively little attention has been paid to the impact of channel effects. However, the instability posed by multipath fading channels leads to significant performance degradation. To mitigate the adverse effects of the multipath channel, we propose a channel-robust modulation classification framework named spectral quotient cumulant classification (SQCC) for orthogonal frequency division multiplexing (OFDM) systems. Specifically, we first transform the received signal to the spectral quotient (SQ) sequence by spectral circular shift division operations. Secondly, an outlier detector is proposed to filter the outliers in the SQ sequence. At last, we extract spectral quotient cumulants (SQCs) from the filtered SQ sequence as the inputs to train the artificial neural network (ANN) classifier and use the trained ANN to make the final decisions. Simulation results show that our proposed SQCC method exhibits classification robustness and superiority under various unknown Rician multipath fading channels compared with other existing methods. Specifically, the SQCC method achieves nearly 90% classification accuracy at the signal to noise ratio (SNR) of 4dB when testing under multiple channels but training under AWGN channel.