Multi-Carrier Modulations Digital Modem with the Narrow-Band Optimal Signals and High Spectral-Energy Efficiency

Sannikov, V. G.; Volchkov, V. P.

doi:10.1109/dspa48919.2020.9213281

Cited by 4 publications

(1 citation statement)

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“…However, the CNN has a high signal loss in the max-pooling layer, which reduces the accuracy of AMC [13,14]. However, the communication channel may be corrupted by additive noise, which cannot be handled by many AMC works, and the loworder features are not suitable for classifying higher-order modulation schemes [15][16][17]. Table I presents a comparison of single-and multi-carrier modulations parameters.…”

Section: Introductionmentioning

confidence: 99%

AMC2-Pyramid: Intelligent Pyramidal Feature Engineering and Multi-Distance Decision Making for Automatic Multi-Carrier Modulation Classification

et al. 2021

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Automatic modulation classification (AMC) is a method that supported different wireless communication systems for modulation type classification. Currently, orthogonal frequency division multiplexing, multiple-input, multiple-output systems are widely using this technique. Recent AMC methods are designed for a single-carrier system identifying a few modulation types. To motivate the AMC for the current communication systems, we present an intelligent pyramid model for automatic multi-carrier modulation classification (AMC2pyramid) with three significant operations, namely, signal fortification, feature engineering and modulation classification. First, signal quality is estimated to reduce the complexity in classification because some signals are affected by noise and other environmental or channel artefacts. Hence, before preprocessing the signal, the quality is assessed according to the channel state information, signal to inference plus noise ratio, received signal strength indicator and spectral efficiency. For low quality, quality augmentation is applied. Then, quality augmentation is implemented with noise elimination, equalisation, quantisation and channel frequency offset compensation. In the feature engineering step, feature extraction and clustering are presented using the gated feature response pyramid network, and a twin-functioned human mental search algorithm is used. The modulation classification is implemented using amulti-distance-based nearest centroid classifier, and improved Q-learning is used to identify signals as any of 16QAM, 32QAM, 64QAM, 128QAM, QPSK, BPSK, DPSK, ASK and FSK. The performance of the proposed AMC2-pyramid is implemented using MatlabR2017b, where accuracy, precision, recall, F-score, error rate and computational time is computed for the proposed work including previous well-known methods. The proposed work proves that this method outperforms the previous ones.

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