Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach.

In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete certainty for channels' statistics and attains superior performance in the presence of a limited number of pilots.