Abstract
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.
Highlights
Automatic modulation classification (AMC) is an emerging field that has great attention from various signal processing applications
Synthesis of Related Work: From the prior research work analysis, we have identified that the following research issues still need to be resolved in the AMC: (1) AMC under a noisy environment often leads to lower accuracy, (2) the lack of effective feature extraction progress leads to higher time consumption and complexity, (3) existing solutions are unable to handle the noises other than additive white Gaussian noise (AWGN), (3) channel frequency offset (CFO) and I/Q imbalance, which affects the classification severely, are less focused, (4) overall, the accurate identification of the modulation scheme is still challenging because of noise, ineffective features and the poor classification algorithm
Our work focuses on four problems, such as CFO/IQ imbalance, inefficient feature extraction, high complexity and low classification accuracy, which are solved by our proposed work
Summary
Automatic modulation classification (AMC) is an emerging field that has great attention from various signal processing applications. The modulation process is essential for the communication system, which is used to transmit the highfrequency carrier wave to a low-frequency signal. In this way, the transmitted signal contains all the information of the original message signal [1]. Multi-carrier modulation is used to transmit the data by dividing it into multiple components, which send to the individual carrier signals [2]. This signal has a narrow bandwidth, whereas the multiple signals have a wide bandwidth.
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