Channel Equalization Using Adaptive Complex Radial Basis Function Networks
Channel Equalization Using Adaptive Complex Radial Basis Function Networks
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DOI：
10.1109/49.363139
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
Inhyok Cha;S. Kassam
中科院分区：
文献类型：
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作者：
Inhyok Cha;S. Kassam
人们普遍认为数字通道均衡可以解释为非线性分类问题。能够逼近非线性映射的网络在此类应用中非常有用。径向基函数网络（RBFN）就是这样一种网络。我们考虑复值信号的 RBFN 扩展（复 RBFN 或 CRBFN）。我们还提出了一种随机梯度（SG）训练算法，可以适应网络的所有自由参数。然后，我们使用 CRBFN 作为均衡器的一部分来考虑复杂非线性通道的均衡问题。我们进行的仿真结果表明，采用 SG 算法的 CRBFN 在信道均衡方面非常有效。 >
It is generally recognized that digital channel equalization can be interpreted as a problem of nonlinear classification. Networks capable of approximating nonlinear mappings can be quite useful in such applications. The radial basis function network (RBFN) is one such network. We consider an extension of the RBFN for complex-valued signals (the complex RBFN or CRBFN). We also propose a stochastic-gradient (SG) training algorithm that adapts all free parameters of the network. We then consider the problem of equalization of complex nonlinear channels using the CRBFN as part of an equalizer. Results of simulations we have carried out show that the CRBFN with the SG algorithm can be quite effective in channel equalization. >