MIMO-OFDM Modulation Classification Using 4D2DConvNet for 5G Communications
Bing Ren, Kah Chan Teh, Hongyang An, Erry Gunawan
Abstract
Orthogonal frequency-division multiplexing (OFDM) has gained increasing attention for the automatic modulation classification (AMC) tasks in multiple-input multiple-output OFDM (MIMO-OFDM) systems. This letter proposes a MIMO-OFDM modulation classification network called 4D2DConvNet for MIMO-OFDM systems, which integrates multi-branch shallow two-dimensional convolution (2DConv) with four-dimensional convolution (4DConv) to extract channel-specific OFDM symbol features and cross-channel correlation. Simulation results demonstrate that the 4D2DConvNet achieves robust performance, which attains a classification accuracy exceeding 95% at a signal-to-noise ratio (SNR) of 8 dB over fifth-generation (5G) frequency-selective fading channels across various delay profiles and Doppler shift configurations.