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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The success rate of a neural network (NN) classifier (rectified linear unit, 10 layers, softmax output layer activation)-based demodulator was proposed and evaluated for phase-shift keying (PSK) and quadrature amplitude modulation (QAM) modulated signals corrupted by additive white Gaussian, chisquared, uniform, and Rayleigh noise channels with signal-to-noise ratios (SNR) ranging from -20 dB to +20 dB. Low SNR are common in spectrum sensing, cognitive radio networks, underwater acoustics, target detection, remote sensing, seismic monitoring, and helicopter blade detection. This classifier-modulator performance was compared with that of the matched filter detector (MFD) for varying channel noise, constellation type (PSK or QAM), constellation size (M=2, 4, 8, 16), sample size (N), and training to test the data ratio. The classifier demodulator had a performance equal to or better than MFD in 98% of the scenarios. A training-to-test ratio of 70:30 or 80:20 is appropriate. The classifier performances of M-PSK and M-QAM are comparable. The superior performance of the NN classifier is more pronounced for M values greater than 2. N=5000 or higher is sufficient for most scenarios, and N=20000 is necessary for M=16. A higher success rate was obtained for additive chisquare and Rayleigh noise channels. The proposed demodulator performed significantly better than the matched filter for SNR values &#x2264; 0 dB. 16-QAM over an additive uniform noise channel had a better success rate for an SNR of 0 dB or less, whereas 16-QAM over an additive Rayleigh noise channel had a better success rate for an SNR of 5 dB or higher.

Performance of a Neural Network Classifier Based Demodulator in Communication System