Towards deep learning-aided wireless channel estimation and channel state information feedback for 6G
Open Access
- 1 February 2023
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Journal of Communications and Networks
- Vol. 25 (1), 61-75
- https://doi.org/10.23919/jcn.2022.000037
Abstract
Deep learning (DL), a branch of artificial intelligence (AI) techniques, has shown great promise in various disciplines such as image classification and segmentation, speech recognition, language translation, among others. This remarkable success of DL has stimulated increasing interest in applying this paradigm to wireless channel estimation in recent years. Since DL principles are inductive in nature and distinct from the conventional rule-based algorithms, when one tries to use DL technique to the channel estimation, one might easily get stuck and confused by so many knobs to control and small details to be aware of. The primary purpose of this paper is to discuss key issues and possible solutions in DL-based wireless channel estimation and channel state information (CSI) feedback including the DL model selection, training data acquisition, and neural network design for 6G. Specifically, we present several case studies together with the numerical experiments to demonstrate the effectiveness of the DL-based wireless channel estimation framework.Keywords
This publication has 28 references indexed in Scilit:
- Deep Learning for Massive MIMO CSI FeedbackIEEE Wireless Communications Letters, 2018
- Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave SystemsIEEE Journal on Selected Areas in Communications, 2017
- Expectation-Maximization-Based Channel Estimation for Multiuser MIMO SystemsIEEE Transactions on Communications, 2017
- Compressed Sensing for Wireless Communications: Useful Tips and TricksIEEE Communications Surveys & Tutorials, 2017
- SoftNull: Many-Antenna Full-Duplex Wireless via Digital BeamformingIEEE Transactions on Wireless Communications, 2016
- Deep Residual Learning for Image RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Mastering the game of Go with deep neural networks and tree searchNature, 2016
- Antenna Grouping Based Feedback Compression for FDD-Based Massive MIMO SystemsIEEE Transactions on Communications, 2015
- Deep learningNature, 2015
- Millimeter-Wave Cellular Wireless Networks: Potentials and ChallengesProceedings of the IEEE, 2014