The University of Southampton
University of Southampton Institutional Repository

Blind Joint Maximum Liklihood Channel Estimation and Data Detection for Single-Input Multiple-Output Systems

Chen, S., Yang, X.C. and Hanzo, L. (2005) Blind Joint Maximum Liklihood Channel Estimation and Data Detection for Single-Input Multiple-Output Systems At IEE 3G and Beyond, United Kingdom. 07 - 09 Nov 2005. , pp. 201-205.

Record type: Conference or Workshop Item (Paper)

Abstract

A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single-input multiple-output (SIMO) systems. The joint ML optimization of the channel and data estimation is decomposed into an iterative optimization loop. An efficient global optimization algorithm termed as the repeated weighted boosting aided search is employed first to identify the unknown SIMO channel model, and then the Viterbi algorithm is used for the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used for demonstrating the efficiency of this joint ML optimization scheme designed for blind adaptive SIMO systems.

PDF sqc-xcy-lh-3G.pdf - Other
Download (2MB)

More information

Published date: 2005
Additional Information: Event Dates: 7-9 November 2005
Venue - Dates: IEE 3G and Beyond, United Kingdom, 2005-11-07 - 2005-11-09
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 261682
URI: http://eprints.soton.ac.uk/id/eprint/261682
PURE UUID: fa4ef108-7642-41b5-9a4a-bfecfc70a71c

Catalogue record

Date deposited: 15 Dec 2005
Last modified: 18 Jul 2017 09:00

Export record

Contributors

Author: S. Chen
Author: X.C. Yang
Author: L. Hanzo

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×