Deep learning-based pilot-assisted channel state estimator for OFDM systems

This study proposes an online deep learning-based channel state estimator for OFDM wireless communication systems by employing the deep learning long short-term memory (LSTM) neural networks. The proposed algorithm is a pilot-assisted estimator type. The proposed estimator is initially offline trained using simulated data sets, and then it follows the channel statistics in an online deployment, where finally the transmitted data can be recovered. A comparative investigation is performed using three different optimisation algorithms for deep learning to evaluate the performance of the proposed estimator at each. The proposed estimator provides a superior performance in comparison to least square (LS) and minimum mean square error (MMSE) estimators when limited pilots are used, thanks to the outstanding learning and generalisation capabilities of deep learning LSTM neural networks. Also, it does not require any prior knowledge of channel statistics. So, the proposed estimator is promising for channel state estimation in OFDM communication systems.

Automated summary

This study introduces an online deep learning-based channel state estimator for OFDM wireless communication systems using LSTM neural networks. The proposed estimator outperforms traditional LS and MMSE estimators when limited pilots are used, and does not require prior knowledge of channel statistics, showing promising performance in channel state estimation.