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An intelligent receiver for space time block coded OFDM system

机译:用于空间时间块编码的智能接收器OFDM系统

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An intelligent receiver based on Radial Basis Function neural network (RBF) for Space-Time Block Coded MIMO system is developed here. Due to their fast maximum-likelihood(ML) decoding, STBC are commonly used in MIMO-OFDM system. However, most of the existing STBC methods rely on the availability of accurate channel state information (CSI) at the receiver. Furthermore, many STBC assume the channels are block-wise constant, but these do not always hold. So RBF based receiver is designed in the paper. With respect to the RBF neural network, PCA(Principal Component Analysis) is applied to process the train samples and growing and pruning(GAP) algorithm is adopted as the learning algorithm. Compared with ML decoder, the proposed receiver has a high precision and a good performance to track the variations of the fading channels. The result of simulation illustrates the effectiveness of the receiver introduced.
机译:这里开发了一种基于径向基函数神经网络(RBF)的智能接收器,在此开发了用于空间块编码的MIMO系统。由于其快速最大可能性(ml)解码,STBC通常用于MIMO-OFDM系统。然而,大多数现有的STBC方法依赖于接收器处的准确信道状态信息(CSI)的可用性。此外,许多STBC假设通道是块状的恒定,但这些并不总是保持。所以基于RBF的接收器是在纸上设计的。关于RBF神经网络,应用PCA(主成分分析)来处理列车样本和生长和修剪(间隙)算法被采用作为学习算法。与ML解码器相比,所提出的接收器具有高精度和良好的性能,以跟踪衰落通道的变化。仿真结果说明了接收器引入的有效性。

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