Clustering-Based Symmetric Radial Basis Function Beamforming

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.