English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Automatic modulation classification (AMC) is to identify the modulationformat of the received signal corrupted by the channel effects and noise. Mostexisting works focus on the impact of noise while relatively little attentionhas been paid to the impact of channel effects. However, the instability posedby multipath fading channels leads to significant performance degradation. Tomitigate the adverse effects of the multipath channel, we propose achannel-robust modulation classification framework named spectral quotientcumulant classification (SQCC) for orthogonal frequency division multiplexing(OFDM) systems. Specifically, we first transform the received signal to thespectral quotient (SQ) sequence by spectral circular shift division operations.Secondly, an outlier detector is proposed to filter the outliers in the SQsequence. At last, we extract spectral quotient cumulants (SQCs) from thefiltered SQ sequence as the inputs to train the artificial neural network (ANN)classifier and use the trained ANN to make the final decisions. Simulationresults show that our proposed SQCC method exhibits classification robustnessand superiority under various unknown Rician multipath fading channels comparedwith other existing methods. Specifically, the SQCC method achieves nearly 90%classification accuracy at the signal to noise ratio (SNR) of 4dB when testingunder multiple channels but training under AWGN channel.

Channel-robust Automatic Modulation Classification Using Spectral Quotient Cumulants