Channel Estimation and Equalization for SC-FDMA Using Machine Learning
Fakharizadeh P, Karakas O, Bovolis CA, Breiling M, Gerstacker W (2024)
Publication Type: Conference contribution
Publication year: 2024
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 123-130
Conference Proceedings Title: WSA 2024 - Proceedings of the 27th International Workshop on Smart Antennas
Event location: Dresden, DEU
ISBN: 9798350361995
DOI: 10.1109/WSA61681.2024.10512105
Abstract
We design neural network (NN)-based schemes for channel estimation and equalization tasks in Single-Carrier Frequency Division Multiple Access (SC-FDMA) transmission over a dispersive block-fading channel. It is demonstrated that the proposed schemes outperform their traditional counterparts for the 5G Clustered Delay Line (CDL) channel model. A significant gain is achieved compared to linear minimum mean-squared error (MMSE) equalization and Bahl-Cocke-Jelinek-Raviv (BCJR) equalizer using a pre-filter in the case of perfect channel state information (CSI) available at the receiver. The proposed NN-based channel estimator can be combined with conventional and NN-based equalizers, as well as the proposed NN-based channel equalizer can be combined with conventional channel estimators. When the proposed NN-based channel estimator and equalizer are combined, it is possible to optimize them separately or jointly. Additionally, we derive a Cramer-Rao Bound (CRB) for unbiased channel estimation error in our proposed pilot insertion regime.
Authors with CRIS profile
Involved external institutions
Fraunhofer-Institut für Integrierte Schaltungen (IIS)
Germany (DE)
National Technical University of Athens / Εθνικό Μετσόβιο Πολυτεχνείο
Greece (GR)
Germany (DE)
National Technical University of Athens / Εθνικό Μετσόβιο Πολυτεχνείο
Greece (GR)
How to cite
APA:
Fakharizadeh, P., Karakas, O., Bovolis, C.A., Breiling, M., & Gerstacker, W. (2024). Channel Estimation and Equalization for SC-FDMA Using Machine Learning. In Thomas Uhle (Eds.), WSA 2024 - Proceedings of the 27th International Workshop on Smart Antennas (pp. 123-130). Dresden, DEU: Institute of Electrical and Electronics Engineers Inc..
MLA:
Fakharizadeh, Pouya, et al. "Channel Estimation and Equalization for SC-FDMA Using Machine Learning." Proceedings of the 27th International Workshop on Smart Antennas, WSA 2024, Dresden, DEU Ed. Thomas Uhle, Institute of Electrical and Electronics Engineers Inc., 2024. 123-130.
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