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Clustering-Based Symmetric Radial Basis Function Beamforming.

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Název: Clustering-Based Symmetric Radial Basis Function Beamforming.
Autoři: Chen, S., Labib, K., Hanzo, L.
Zdroj: IEEE Signal Processing Letters; Sep2007, Vol. 14 Issue 9, p589-592, 4p, 1 Diagram, 1 Chart, 4 Graphs
Témata: BEAM dynamics, RADIAL basis functions, APPROXIMATION theory, ANTENNA arrays, BAYESIAN analysis, SIGNAL-to-noise ratio, INFORMATION measurement
Abstrakt: We propose a clustering-based symmetric radial basis function (SRBF) detector for multiple-antenna assisted beamforming systems. By exploiting the inherent symmetry of the underlying optimal Bayesian detection solution, this SRBF detector is capable of realizing the optimal Bayesian performance by clustering noisy observation data using an enhanced κ-means clustering algorithm. The proposed adaptive solution provides a signal-to-noise ratio gain in excess of 8 dB against the theoretical linear minimum bit error rate benchmark, when supporting five users with the aid of three receive antennas. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:We propose a clustering-based symmetric radial basis function (SRBF) detector for multiple-antenna assisted beamforming systems. By exploiting the inherent symmetry of the underlying optimal Bayesian detection solution, this SRBF detector is capable of realizing the optimal Bayesian performance by clustering noisy observation data using an enhanced κ-means clustering algorithm. The proposed adaptive solution provides a signal-to-noise ratio gain in excess of 8 dB against the theoretical linear minimum bit error rate benchmark, when supporting five users with the aid of three receive antennas. [ABSTRACT FROM AUTHOR]
ISSN:10709908
DOI:10.1109/LSP.2007.896149