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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Since the signal with strong power should be demodulated first for successiveinterference cancellation (SIC) demodulation in non-orthogonal multiple access(NOMA) systems, the base station (BS) should inform the near user terminal(UT), which has allocated higher power, of modulation mode of the far userterminal. To avoid unnecessary signaling overhead in this process, a blinddetection algorithm of NOMA signal modulation mode is designed in this paper.Taking the joint constellation density diagrams of NOMA signal as the detectionfeatures, deep residual network is built for classification, so as to detectthe modulation mode of NOMA signal. In view of the fact that the jointconstellation diagrams are easily polluted by high intensity noise and losetheir real distribution pattern, the wavelet denoising method is adopted toimprove the quality of constellations. The simulation results represent thatthe proposed algorithm can achieve satisfactory detection accuracy in NOMAsystems. In addition, the factors affecting the recognition performance arealso verified and analyzed.

Deep Learning-based Modulation Detection for NOMA Systems