Deep Learning Based Detector for Multi-Mode OFDM Index Modulation

‍ ‍In this paper， a Deep Learning （DL） based Multi-Mode Orthogonal Frequency Division Multiplexing with Index Modulation （MM-OFDM-IM） detector is proposed. In this detector， two Sub-Convolutional Neural Network （SCNN） detects the index and carrier bits of MM-OFDM-IM signal in parallel， and the received symbols are preprocessed after Zero Force （ZF） equalization to generate a two-dimensional matrix， which is fed to the SCNN in parallel to learn the signal's intrinsic features. After offline training， the detector can achieve online detection of MM-OFDM-IM symbols. Simulation results show that the detector achieves approximate Maximum Likelihood （ML） detection performance with low computational complexity under Rayleigh fading channel conditions. By pruning the trained model， the number of parameters of the model can be significantly reduced while the Bit Error Rate （BER） is guaranteed， achieving an effective balance between performance and computational complexity.