Blind modulation classification in multiple input and output‐orthogonal frequency division multiplexing using time‐frequency analysis and customized convolutional neural network architecture

Abstract

AbstractMIMO with OFDM is a powerful communication technology in both army and civilian environments. Because there is no prior knowledge of carrier state information or signal overlapping in MIMO‐OFDM systems, outdated probability and feature‐based approaches can be applied. However, these methods cannot easily be used to provide the channel characteristics. Blind modulation categorization is a critical stage in deploying 5G networks. The issue of blind modulation classification for a multiple input and output (MIMO) antenna system using orthogonal frequency division multiplexing (OFDM) is investigated in this research. The independent component analysis (ICA) method has been used to separate the MIMO modulated signal, and the smoothed pseudo Wigner‐Ville distribution (SPWVD) with a Hamming window of 0.2 s has been used to analyze the time‐frequency characteristics of time‐domain modulated signals to get around the challenge of blind attenuation categorization in MIMO‐OFDM arrangements. The recovered time‐frequency characteristics are then transformed into red‐green‐blue (RGB) spectrogram pictures and a perfect‐tweaked customized CNN in which CNN (convolutional neural network) is integrated with LSTM (long short term memory) is used to categorize modulation kinds based on the illustrations of RGB spectrograms. Finally, a module for merging decisions based on majority voting was employed to combine the categorization findings of all receiving antennas. As a result, a customized CNN‐based blind modulation technique outperforms existing works to find the exact modulation type in the MIMO‐OFDM communication.