Automatic modulation classification using bimodal parallel multichannel deep learning framework for spatial multiplexing MIMO system

Abstract

In recent years, automatic modulation classification (AMC) has proved its importance for the military as well as civil applications and deep learning (DL) based AMC has attracted wide attention. But existing methods neglect to consider the advantages of both multimodality and complementarities simultaneously in a single DL framework for multiple-input multiple-output (MIMO) system. To mitigate this, bimodal multichannel configurable DL-based AMC has been presented for the MIMO system under perfect channel state information with zero forcing equalizer. The proposed DL framework consists of two parallel structures of multichannel convolutional layers in which one multichannel structure is fed with in-phase/quadrature (I/Q) as first modal information while another multichannel structure accepts amplitude/phase as second modal information. Features extracted from this parallel structure then pass through long short-term memory (LSTM) layers for further extracting temporal information effectively. Finally, classification is accomplished through fully connected layers. Simulation results manifest the robustness of the proposed framework that achieves an average accuracy of about 0.6% to 12% higher compared to the state-of-the-art DL models. Simulations also illustrate the impact of antenna diversities with spatial multiplexing on the classification.