University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Unsupervised Deep Learning for MU-SIMO Joint Transmitter and Noncoherent Receiver Design

Xue, Songyan, Ma, Yi and Tafazolli, Rahim (2018) Unsupervised Deep Learning for MU-SIMO Joint Transmitter and Noncoherent Receiver Design IEEE Wireless Communications Letters.

[img]
Preview
Text
Unsupervised Deep Learning for MU-SIMO Joint Transmitter and Noncoherent Receiver Design.pdf - Accepted version Manuscript

Download (336kB) | Preview

Abstract

This work aims to handle the joint transmitter and noncoherent receiver optimization for multiuser single-input multiple-output (MU-SIMO) communications through unsupervised deep learning. It is shown that MU-SIMO can be modeled as a deep neural network with three essential layers, which include a partially-connected linear layer for joint multiuser waveform design at the transmitter side, and two nonlinear layers for the noncoherent signal detection. The proposed approach demonstrates remarkable MU-SIMO noncoherent communication performance in Rayleigh fading channels.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Xue, Songyansongyan.xue@surrey.ac.uk
Ma, YiY.Ma@surrey.ac.uk
Tafazolli, RahimR.Tafazolli@surrey.ac.uk
Date : 15 August 2018
DOI : 10.1109/LWC.2018.2865563
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
Uncontrolled Keywords : Unsupervised deep learning; Joint transmitter and receiver design; Noncoherent detection; Multiuser single-input and multiple-output (MU-SIMO)
Related URLs :
Depositing User : Clive Harris
Date Deposited : 13 Aug 2018 22:31
Last Modified : 16 Nov 2018 10:39
URI: http://epubs.surrey.ac.uk/id/eprint/848911

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800