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

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 (AMC2-pyramid) which alleviates the existing works challenges such as high degradation of accuracy for higher order modulation schemes, inefficient feature extraction and lack of effectiveness in low SNR environments. The proposed work contains 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 pre-processing 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 (GaFP), and a twin-functioned human mental search algorithm is used. The modulation classification is implemented using a multi-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 (6.8% – 23.15%) high when compared to sample size and (14% – 46%) high when compared to SNR at −10 dB, precision (4.96% – 29.5%) high when compared to sample size and (16.5% – 48.5%) high when compared to SNR at −10 dB, recall (2– 29.76%) high when compared to sample size and (14% – 45%) high when compared to SNR at −10dB, F-score (2– 30%) high when compared to sample size and (15.5%– 46.5%) high when compared to SNR at −10 dB, error rate (0.7% – 11.5%) low when compared to sample size and (4.5%– 17%) low when compared to SNR at −10 dB, computational time (170ms – 400ms) low when compared to sample size is computed for the proposed work including previous well-known methods. The proposed work proves that this method outperforms the previous ones.