Signal-to-noise ratio estimation algorithm for adaptive coding and modulation in advanced DVB-RCS systems

Ijaz, A, Awoseyila, AB and Evans, BG (2012) Signal-to-noise ratio estimation algorithm for adaptive coding and modulation in advanced DVB-RCS systems IET Communications, 6 (11). pp. 1587-1593.

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Abstract

This paper presents a signal-to-noise ratio (SNR) estimation algorithm for advanced digital video broadcastingreturn channel via satellite (DVB-RCS) systems using adaptive coding and modulation (ACM). Due to the absence of a repetitive pilot symbol structure, SNR estimation has to be performed using the fixed symbol preamble data. Moreover, sporadic nature of data traffic on the return link causes variation in interference level from slot to slot and, therefore, the estimation has to be done within one traffic slot duration. Hence, it becomes necessary to use a combination of data-aided and decision-directed (DD) algorithms so as to make use of traffic data. A non-data-aided estimator that was previously proposed by the authors for binary and quadrature phase shift keying schemes is extended to 8-PSK in a decision directed manner. The inherent bias of DD approach at low values of SNR is reduced by using a hybrid approach, that is, using the proposed estimator at moderate/high values of SNR and the moments-based estimator (M2M4) at low values of SNR. Overall improved performance of the proposed hybrid estimator, in terms of accuracy and complexity, makes it an attractive choice for implementing ACM in advanced DVB-RCS systems.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research
Authors :	Name Email ORCID Ijaz, A Awoseyila, AB Evans, BG
Date :	24 July 2012
DOI :	10.1049/iet-com.2010.0938
Additional Information :	This paper is a postprint of a paper submitted to and accepted for publication in IET Communications and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library
Depositing User :	Symplectic Elements
Date Deposited :	30 Nov 2012 11:38
Last Modified :	31 Oct 2017 14:50
URI:	http://epubs.surrey.ac.uk/id/eprint/735356

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