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A Carrier-Frequency-Offset Resilient OFDMA Receiver Designed Through Machine Deep Learning

Li, Ang, Ma, Yi, Xue, Songyan, Yi, Na and Tafazolli, Rahim (2018) A Carrier-Frequency-Offset Resilient OFDMA Receiver Designed Through Machine Deep Learning In: 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC): IEEE PIMRC 2018, 09-12 Sep 2018, Bologna, Italy.

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Abstract

The aim of this paper is to handle the multifrequency synchronization problem inherent in orthogonal frequency-division multiple access (OFDMA) uplink communications, where the carrier frequency offset (CFO) for each user may be different, and they can be hardly compensated at the receiver side. Our major contribution lies in the development of a novel OFDM receiver that is resilient to unknown random CFO thanks to the use of a CFO-compensator bank. Specifically, the whole CFO range is evenly divided into a set of sub-ranges, with each being supported by a dedicated CFO compensator. Given the optimization for CFO compensator a NP-hard problem, a machine deep-learning approach is proposed to yield a good sub-optimal solution. It is shown that the proposed receiver is able to offer inter-carrier interference free performance for OFDMA systems operating at a wide range of SNRs.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Li, Angang.li@surrey.ac.uk
Ma, YiY.Ma@surrey.ac.uk
Xue, Songyansongyan.xue@surrey.ac.uk
Yi, NaN.Yi@surrey.ac.uk
Tafazolli, RahimR.Tafazolli@surrey.ac.uk
Date : 12 September 2018
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Related URLs :
Depositing User : Clive Harris
Date Deposited : 02 Jul 2018 13:19
Last Modified : 13 Sep 2018 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/848624

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