Chen S, Wolfgang A, Harris C J, Hanzo L
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
Neural Netw. 2008 Mar-Apr;21(2-3):358-67. doi: 10.1016/j.neunet.2007.12.014. Epub 2007 Dec 17.
A powerful symmetrical radial basis function (RBF) aided detector is proposed for nonlinear detection in so-called rank-deficient multiple-antenna assisted beamforming systems. By exploiting the inherent symmetry of the optimal Bayesian detection solution, the proposed RBF detector becomes capable of approaching the optimal Bayesian detection performance using channel-impaired training data. A novel nonlinear least bit error algorithm is derived for adaptive training of the symmetrical RBF detector based on a stochastic approximation to the Parzen window estimation of the detector output's probability density function. The proposed adaptive solution is capable of providing a signal-to-noise ratio gain in excess of 8 dB against the theoretical linear minimum bit error rate benchmark, when supporting four users with the aid of two receive antennas or seven users employing four receive antenna elements.
针对所谓的秩亏多天线辅助波束形成系统中的非线性检测，提出了一种强大的对称径向基函数（RBF）辅助检测器。通过利用最优贝叶斯检测解决方案的固有对称性，所提出的RBF检测器能够使用信道受损的训练数据接近最优贝叶斯检测性能。基于对检测器输出概率密度函数的Parzen窗口估计的随机近似，推导了一种用于对称RBF检测器自适应训练的新型非线性最小误码算法。当借助两个接收天线支持四个用户或使用四个接收天线元件支持七个用户时，所提出的自适应解决方案相对于理论线性最小误码率基准能够提供超过8 dB的信噪比增益。
