Channel equalization using complex-valued recurrent neural network

被引：0
|
作者：
Wang, XQ [1 ]
Lin, H [1 ]
Lu, JM [1 ]
Yahagi, T [1 ]
机构：
[1] Chiba Univ, Grad Sch Sci & Technol, Yahagi & Lu Lab, Inage Ku, Chiba 2638522, Japan
来源：
2001 INTERNATIONAL CONFERENCES ON INFO-TECH AND INFO-NET PROCEEDINGS, CONFERENCE A-G: INFO-TECH & INFO-NET: A KEY TO BETTER LIFE | 2001年
关键词：
recurrent neural network; real-time recurrent learning; quadrature amplitude modulation;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Recurrent neural network (RNN) is a kind of neural network with one or more feedback loops. In this paper, a complex-valued fully connected RNN with real-time recurrent learning is presented for the equalization of complex-valued system, such as quadrature amplitude modulation (QAM), in the presence of intersymbol interference and nonlinear distortions. Simulation results show that the proposed scheme is quite effective in channel equalization when facing the nonlinear distortions.
引用
收藏
页码：C498 / C503
页数：6
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