The University of Southampton
University of Southampton Institutional Repository

Sliding window adaptive filter with diagonal loading for estimation of sparse UWA channels

Sliding window adaptive filter with diagonal loading for estimation of sparse UWA channels
Sliding window adaptive filter with diagonal loading for estimation of sparse UWA channels
In this paper, we propose a recursive least square (RLS) adaptive filter for sparse identification of underwater acoustic (UWA) channels. The adaptive filter is based on sliding window, diagonal loading, and dichotomous coordinate descent iterations. The adaptive algorithm possesses a complexity that is only linear in the filter length. The adaptive filter is used for channel estimation in an UWA communication system with guard-free orthogonal frequency division multiplexing (OFDM) signals and superimposed pilot symbols. We investigate and compare performance of various RLS adaptive filters and show that the proposed sliding window sparse RLS adaptive filter with diagonal loading demonstrates the best performance. We also show that adaptive filters with the sliding window outperform adaptive filters with the exponential window. The comparison has been done using signals recorded in a sea trial at a distance of 80 km transmitted by a fast moving transducer, resulting in fast-varying channel. In these conditions, a low-error-rate transmission is achieved at a data rate of 0.5 bit/s/Hz.
underwater acoustic communication, Adaptive filtering, Channel Estimation
1-5
Li, Jianghui
9c589194-00fa-4d42-abaf-53a32789cc5e
Zakharov, Yuriy
2abf7642-edba-4f15-8b98-4caca66510f6
Li, Jianghui
9c589194-00fa-4d42-abaf-53a32789cc5e
Zakharov, Yuriy
2abf7642-edba-4f15-8b98-4caca66510f6

Li, Jianghui and Zakharov, Yuriy (2016) Sliding window adaptive filter with diagonal loading for estimation of sparse UWA channels. Oceans 2016 Shanghai, , Shanghai, China. 10 - 13 Apr 2016. pp. 1-5 . (doi:10.1109/OCEANSAP.2016.7485346).

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we propose a recursive least square (RLS) adaptive filter for sparse identification of underwater acoustic (UWA) channels. The adaptive filter is based on sliding window, diagonal loading, and dichotomous coordinate descent iterations. The adaptive algorithm possesses a complexity that is only linear in the filter length. The adaptive filter is used for channel estimation in an UWA communication system with guard-free orthogonal frequency division multiplexing (OFDM) signals and superimposed pilot symbols. We investigate and compare performance of various RLS adaptive filters and show that the proposed sliding window sparse RLS adaptive filter with diagonal loading demonstrates the best performance. We also show that adaptive filters with the sliding window outperform adaptive filters with the exponential window. The comparison has been done using signals recorded in a sea trial at a distance of 80 km transmitted by a fast moving transducer, resulting in fast-varying channel. In these conditions, a low-error-rate transmission is achieved at a data rate of 0.5 bit/s/Hz.

This record has no associated files available for download.

More information

Accepted/In Press date: 1 February 2016
Published date: 10 April 2016
Venue - Dates: Oceans 2016 Shanghai, , Shanghai, China, 2016-04-10 - 2016-04-13
Keywords: underwater acoustic communication, Adaptive filtering, Channel Estimation

Identifiers

Local EPrints ID: 425239
URI: http://eprints.soton.ac.uk/id/eprint/425239
PURE UUID: 37076bc5-4224-4108-a9be-bd480c15092e
ORCID for Jianghui Li: ORCID iD orcid.org/0000-0002-2956-5940

Catalogue record

Date deposited: 11 Oct 2018 16:30
Last modified: 15 Mar 2024 22:01

Export record

Altmetrics

Contributors

Author: Jianghui Li ORCID iD
Author: Yuriy Zakharov

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.

×