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Fast converging semi-blind space-time equalisation for dispersive QAM MIMO systems

Fast converging semi-blind space-time equalisation for dispersive QAM MIMO systems
Fast converging semi-blind space-time equalisation for dispersive QAM MIMO systems
A novel semi-blind space-time equaliser (STE) is proposed for dispersive multiple-input multiple-output systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the STE, are first utilised to provide a rough initial least squares estimate of the STE's weight vector. A concurrent gradient-Newton constant modulus algorithm and soft decision-directed scheme is then applied to adapt the STE. The proposed semi-blind adaptive STE is capable of converging fast to the minimum mean square error STE solution. Simulation results confirms that the convergence speed of this semi-blind adaptive algorithm is very close to that of the training-based recursive least squares algorithm.
3969-3974
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Chen, Sheng and Hanzo, Lajos (2009) Fast converging semi-blind space-time equalisation for dispersive QAM MIMO systems. IEEE Transactions on Wireless Communications, 8 (8), 3969-3974.

Record type: Article

Abstract

A novel semi-blind space-time equaliser (STE) is proposed for dispersive multiple-input multiple-output systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the STE, are first utilised to provide a rough initial least squares estimate of the STE's weight vector. A concurrent gradient-Newton constant modulus algorithm and soft decision-directed scheme is then applied to adapt the STE. The proposed semi-blind adaptive STE is capable of converging fast to the minimum mean square error STE solution. Simulation results confirms that the convergence speed of this semi-blind adaptive algorithm is very close to that of the training-based recursive least squares algorithm.

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More information

Published date: August 2009
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 267789
URI: https://eprints.soton.ac.uk/id/eprint/267789
PURE UUID: 29fdb401-e6e7-4648-b8e5-76a18c726f3f
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 19 Aug 2009 08:24
Last modified: 06 Jun 2018 13:15

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Contributors

Author: Sheng Chen
Author: Lajos Hanzo ORCID iD

University divisions

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