基于实虚型连续多值复数Hopfield神经网络的QAM盲检测

张昀; 于舒娟; 张志涌; 郭宇峰

doi:10.3969/j.issn.0372-2112.2013.02.008

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基于实虚型连续多值复数Hopfield神经网络的QAM盲检测

学术论文 | 更新时间：2025-07-16

- 基于实虚型连续多值复数Hopfield神经网络的QAM盲检测
- Blind Detection of QAM Signals with a Complex Hopfield Neural Network with Real-Imaginary-Type Soft-Multistate-Activation-Function
- 电子学报 2013年41卷第2期页码：255-259
- 作者机构：
  
  1. 南京邮电大学电子科学与工程学院,江苏,南京,210003
  2. 南京邮电大学自动化学院,江苏,南京,210003
  3. 南京邮电大学电子科学与工程学院江苏南京,210003
  4. 南京邮电大学自动化学院江苏南京,210003
- 作者简介：
- 基金信息：
  
  国家自然科学基金 (No.60772060,No.NY212022)
- DOI：10.3969/j.issn.0372-2112.2013.02.008
  中图分类号： TN911.23
- 纸质出版：2013
- 稿件说明：
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张昀, 于舒娟, 张志涌, 等. 基于实虚型连续多值复数Hopfield神经网络的QAM盲检测[J]. 电子学报, 2013,41(2):255-259.

ZHANG Yun, YU Shu-juan, ZHANG Zhi-yong, et al. Blind Detection of QAM Signals with a Complex Hopfield Neural Network with Real-Imaginary-Type Soft-Multistate-Activation-Function[J]. Acta Electronica Sinica, 2013, 41(2): 255-259.
张昀, 于舒娟, 张志涌, 等. 基于实虚型连续多值复数Hopfield神经网络的QAM盲检测[J]. 电子学报, 2013,41(2):255-259. DOI： 10.3969/j.issn.0372-2112.2013.02.008.

ZHANG Yun, YU Shu-juan, ZHANG Zhi-yong, et al. Blind Detection of QAM Signals with a Complex Hopfield Neural Network with Real-Imaginary-Type Soft-Multistate-Activation-Function[J]. Acta Electronica Sinica, 2013, 41(2): 255-259. DOI： 10.3969/j.issn.0372-2112.2013.02.008.

摘要

针对统计量算法盲检测QAM信号的缺陷

该文提出了一个实虚型连续多值复数Hopfield神经网络算法

该网络的实部、虚部各含一个连续多值实激活函数.该文构造了适用于该网络的能量函数

并分别在异步和同步更新模式下证明了该神经网的稳定性.当该神经网的权矩阵借助接收数据补投影算子构成时

该实虚型连续多值复数Hopfield神经网络可有效地实现QAM信号盲检测.仿真试验表明:该算法采用较短接收数据即可到达全局真解点

并且适用于含公零点信道.

Abstract

Considering the disadvantage of the algorithms based on statistics

a novel algorithm based on Complex Hopfield Neural Network with Real-Imaginary-type Soft-Multistate-activation-function (CHNN_RISM) is proposed to detect QAM signals blindly.A multi-valued continuous activation function is constructed in both of the real part and imaginary part of CHNN_RISM.A new energy function for CHON_RISM is constructed in this paper and the stabilities with asynchronous and synchronous operating mode are also analyzed separately.While the weighted matrix of CHNN_RISM is constructed by the complementary projection operator of received signals

the problem of quadratic optimization with integer constraints can successfully solved with the CHNN_RISM

and the QAM signals are blindly detected.Simulation results show that the algorithm reaches the real equilibrium points with shorter received signals and appropriate for channel with common zeros.

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