An efficient deep neural network channel state estimator for OFDM wireless systems

Abstract Channel state estimation (CSE) is essential for orthogonal frequency division multiplexing (OFDM) wireless systems to deal with multipath channel fading. To attain a high data rate with the use of OFDM technology, an efficient CSE and accurate signal detection are required. The use of machine learning (ML) to improve channel estimates has attracted a lot of attention lately. This is because ML techniques are more adaptable than traditional model-based estimation techniques. The present study proposes a receiver for low-spectrum usage in OFDM wireless systems on Rayleigh fading channels using deep learning (DL) long short-term memory (LSTM). Before online deployment and data retrieval, the proposed DL LSTM estimator gathers channel state information from transmit/receive pairs using offline training. Based on the simulation results of a comparative study, the proposed estimator outperforms conventional channel estimation approaches like minimum mean square error and least squares in noisy and interfering wireless channels. Furthermore, the proposed estimator outperforms the DL bidirectional LSTM (BiLSTM)-based CSE model. In particular, the proposed CSE performs better than other examined estimators with a reduced number of pilots, no cycle prefixes, and no prior knowledge of channel statistics. Because the proposed estimator relies on a DL neural network approach, it holds promise for OFDM wireless communication systems.