Symmetric Kernel Detector for Multiple-Antenna Aided Beamforming Systems

We propose a powerful symmetric kernel classifier for nonlinear detection in challenging rank-deficient multiple-antenna aided communication systems. By exploiting the inherent odd symmetry of the optimal Bayesian detector, the proposed symmetric kernel classifier is capable of approaching the optimal classification performance using noisy training data. The classifier construction process is robust to the choice of the kernel width and is computationally efficient. The proposed solution is capable of providing a signal-to-noise ratio gain in excess of 8 dB against the powerfull linear minimum bit error rate benchmarker, when supporting five users with the aid of three receive antennas.