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Semi-blind fast equalisation of QAM channels using concurrent gradient-Newton CMA and soft decision-directed scheme

Semi-blind fast equalisation of QAM channels using concurrent gradient-Newton CMA and soft decision-directed scheme
Semi-blind fast equalisation of QAM channels using concurrent gradient-Newton CMA and soft decision-directed scheme
This contribution considers semi-blind adaptive equalization for communication systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the equalizer, are first utilized to provide a rough initial least-squares estimate of the equalizer’s weight vector. A novel gradient-Newton concurrent constant modulus algorithm and soft decision-directed scheme are then applied to adapt the equalizer. The proposed semi-blind adaptive algorithm is capable of converging fast and accurately to the optimal minimum mean-square error equalization solution. Simulation results obtained demonstrate that the convergence speed of this semi-blind adaptive algorithm is close to that of the training-based recursive least-square algorithm.
467-476
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Chen, Sheng (2010) Semi-blind fast equalisation of QAM channels using concurrent gradient-Newton CMA and soft decision-directed scheme Int. J. Adaptive Control and Signal Processing, 24, (6), pp. 467-476.

Record type: Article

Abstract

This contribution considers semi-blind adaptive equalization for communication systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the equalizer, are first utilized to provide a rough initial least-squares estimate of the equalizer’s weight vector. A novel gradient-Newton concurrent constant modulus algorithm and soft decision-directed scheme are then applied to adapt the equalizer. The proposed semi-blind adaptive algorithm is capable of converging fast and accurately to the optimal minimum mean-square error equalization solution. Simulation results obtained demonstrate that the convergence speed of this semi-blind adaptive algorithm is close to that of the training-based recursive least-square algorithm.

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Published date: June 2010
Organisations: Southampton Wireless Group

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Local EPrints ID: 271059
URI: https://eprints.soton.ac.uk/id/eprint/271059
PURE UUID: 0eb545ce-deee-4516-9ea0-f4c19adcff04

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Date deposited: 12 May 2010 09:38
Last modified: 18 Jul 2017 06:47

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Contributors

Author: Sheng Chen

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