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Unsupervised Deep Learning for Blind Multiuser Frequency Synchronization in OFDMA Uplink

Li, Ang, Ma, Yi, Xue, Songyan, Tafazolli, Rahim and Dodgson, Terence E (2019) Unsupervised Deep Learning for Blind Multiuser Frequency Synchronization in OFDMA Uplink In: The 55th IEEE International Conference on Communications (ICC 2019), 20-24 May 2019, Shanghai, China.

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In this paper, a novel unsupervised deep learning approach is proposed to tackle the multiuser frequency synchronization problem inherent in orthogonal frequency-division multiple-access (OFDMA) uplink communications. The key idea lies in the use of the feed-forward deep neural network (FF-DNN) for multiuser interference (MUI) cancellation taking advantage of their strong classification capability. Basically, the proposed FF-DNN consists of two essential functional layers. One is called carrier-frequency-offsets (CFOs) classification layer that is responsible for identifying the users’ CFO range, and another is called MUI-cancellation layer responsible for joint multiuser detection (MUD) and frequency synchronization. By such means, the proposed FF-DNN approach showcases remarkable MUIcancellation performances without the need of multiuser CFO estimation. In addition, we also exhibit an interesting phenomenon occurred at the CFO-classification stage, where the CFO-classification performance get improved exponentially with the increase of the number of users. This is called multiuser diversity gain in the CFO-classification stage, which is carefully studied in this paper.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Dodgson, Terence E
Date : 3 February 2019
Funders : Airbus project on Artificial Intelligence for Communications, UK HEFEC-funded 5G Innovation Centre
Copyright Disclaimer : © Copyright 2019 IEEE Communications Society - All rights reserved. Use of this Web site signifies your agreement to the IEEE Terms and Conditions.
Depositing User : Diane Maxfield
Date Deposited : 18 Feb 2019 13:01
Last Modified : 22 May 2019 13:21

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