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.

Preview

Text manuscript_v07.pdf - Accepted version Manuscript Download (371kB) | Preview

Abstract

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 :	Name Email ORCID Li, Ang ang.li@surrey.ac.uk Ma, Yi Y.Ma@surrey.ac.uk Xue, Songyan songyan.xue@surrey.ac.uk Tafazolli, Rahim R.Tafazolli@surrey.ac.uk 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
URI:	http://epubs.surrey.ac.uk/id/eprint/850455

Actions (login required)

View Item

Downloads