Title :
Clustering-Based Symmetric Radial Basis Function Beamforming
Author :
Chen, S. ; Labib, K. ; Hanzo, L.
Author_Institution :
Univ. of Southampton, Southampton
Abstract :
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 K-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.
Keywords :
antenna arrays; array signal processing; multibeam antennas; pattern clustering; radial basis function networks; signal detection; K-means clustering algorithm; SRBF detector; clustering-based symmetric radial basis function beamforming; multiple-antenna assisted beamforming systems; noisy observation data clustering; optimal Bayesian detection solution; receive antennas; Adaptive algorithm; Array signal processing; Bayesian methods; Bit error rate; Clustering algorithms; Detectors; Object detection; Receiving antennas; Signal to noise ratio; Vectors; Beamforming; clustering; multiple-antenna system; radial basis function network; symmetry;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2007.896149