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

ABSTRACT: 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 a priori certainty for channels' statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, a

SUBMITTER: Essai Ali MH 

PROVIDER: S-EPMC8409333 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature