A joint denoising and deep learning detector for OFDM-IM.

Only a subset of subcarriers are activated in orthogonal frequency division multiplexing-index modulation (OFDM-IM), which achieves higher energy efficiency and resists frequency offset. In the OFDM-IM, the energy of the received signal is computed and then combined with pre-processed signal to create the input of detection network. Inspired by image denoising technology, this study enhances the detection performance by denoising the pre-processed data and improving the energy distribution in the OFDM-IM system. First, considering that the noise reduction process of the pre-processed signal can effectively mitigate the distortion by noise which affects the detection accuracy, this study proposes a two-phase neural network termed as Deep-Denoising-IM through the combination of a noise reduction network and a deep learning detection method. Then, to better determine the position of the active carriers, a joint decision method of the denoised data and the received signal is designed as the IQ signal of the denoised data may change the quadrant of original signal distribution. In addition, the pre-processed data sample has insufficient diversity. Considering that data enhancement can increase the noise of the signal samples, this study proposes a method to strengthen the silent carriers in the model training phase, which improves the generalization ability of the model and further enhances the denoising performance. Simulation results show that Deep-Denoising-IM outperforms the existing detectors in terms of mean square error (MSE) and bit error rate (BER) under the Rayleigh fading channel.