Deep learning‐based channel estimation for OFDM‐IM systems over Rayleigh fading channels

Summary Deep learning (DL)‐based channel estimation for orthogonal frequency division multiplexing with index modulation (OFDM‐IM) under Rayleigh fading channel conditions is presented in this paper. A deep neural network (DNN) is utilized to estimate the channel response in simulations. The proposed DNN is trained using the channel coefficient derived through the least squares (LS) method. Then channel estimation is conducted using the trained DNN. Within the DNN, the long short‐term memory (LSTM) layer is included as the hidden layer. Different scenarios are handled in simulations and the proposed DNN is compared with traditional channel estimation methods. The simulations demonstrate that the DL‐based channel estimation significantly surpasses the LS and minimum mean‐square error (MMSE) techniques. This paper presents a deep learning (DL)‐based channel estimation method for orthogonal frequency division multiplexing with index modulation (OFDM‐IM) under Rayleigh fading channel conditions. The proposed method employs a Long Short‐Term Memory (LSTM)‐based network for channel estimation. The results demonstrate that the DL‐based channel estimation significantly outperforms the least squares (LS) and minimum mean‐square error (MMSE) techniques.