Journal
IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY
Volume 1, Issue -, Pages 320-332Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/OJCOMS.2020.2982513
Keywords
5G; artificial Intelligence; channel prediction; channel state information; deep learning; GRU; LSTM; machine learning; multi-antenna system; recurrent neural network
Funding
- German Federal Ministry of Education and Research (BMBF) through TACNET4.0 Project
- German Federal Ministry of Education and Research (BMBF) through KICK Project
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Channel state information (CSI), which enables wireless systems to adapt their transmission parameters to instantaneous channel conditions and consequently achieve great performance boost, plays an increasingly vital role in mobile communications. However, getting accurate CSI is challenging due mainly to rapid channel variation caused by multi-path fading. The inaccuracy of CSI imposes a severe impact on the performance of a wide range of adaptive wireless systems, highlighting the significance of channel prediction that can combat outdated CSI effectively. The aim of this article is to shed light on the state of the art in this field and then go beyond by proposing a novel predictor that leverages the strong time-series prediction capability of deep recurrent neural networks incorporating long short-term memory or gated recurrent unit. In addition to an analytical comparison of computational complexity, performance evaluation in terms of prediction accuracy is carried out upon multi-antenna fading channels. Numerical results reveal that deep learning brings a notable performance gain compared with the conventional predictors built on shallow recurrent neural networks.
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