AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

Abstract

The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

Random Graph-Based M-QAM Classification for MIMO Systems

“…The AMC literature is largely divided into two techniques: likelihood-based classification (LBC) and feature-based classification (FBC). The existing work on the likelihood-based AMC can be found in [18][19][20][21][22][23] and feature-based AMC in [24][25][26][27][28][29][30].…”

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

“…In [175] an AMR using a feature clustering based two-lane capsule network (AMC2N) is proposed. The authors were able to improve the classification accuracy by designing a new two-layer capsule network (TL-CapsNet), and the classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet.…”

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

“…After feature extraction, we perform a feature clustering process to minimise the complexity during classification. To form feature clusters, we propose a novel TF-HMS algorithm, which provides a better-clustered result than the Neutrosophic C-Means clustering algorithm [1]. HMS is a Meta-heuristic population-based optimisation algorithm that works by bid space search.…”

“…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]. The communication system needs modulation because the low-frequency signals cannot be transmitted over long distances as such signals need to modulate high-frequency signals.…”

“…(1) ,(2) ,(3) , … ( ) , in which the th sample belongs to the component of GMM model, is computed as follows: represents the variance and the corresponding soft membership, computed as follows:…”