电子学报 ›› 2019, Vol. 47 ›› Issue (2): 302-307.DOI: 10.3969/j.issn.0372-2112.2019.02.007

• 学术论文 • 上一篇    下一篇

基于前馈神经网络的非合作PCMA信号盲分离算法

郭一鸣1, 彭华1, 杨勇2   

  1. 1. 解放军信息工程大学信息系统工程学院, 河南郑州 450002;
    2. 61886部队, 北京 100084
  • 收稿日期:2018-01-11 修回日期:2018-06-12 出版日期:2019-02-25
    • 通讯作者:
    • 郭一鸣
    • 作者简介:
    • 彭华 男,1973年8月出生,江西萍乡人.教授,博士生导师.主要研究方向为软件无线电、通信信号处理等;杨勇 男,1988年2月出生,云南大理人,博士.研究方向为航天信号分析处理.
    • 基金资助:
    • 国家自然科学基金 (No.61401511,No.U1736107)

Blind Separation Algorithm for Non-cooperative PCMA Signal Based on Feedforward Neural Network

GUO Yi-ming1, PENG Hua1, YANG Yong2   

  1. 1. PLA Information Engineering University, Zhengzhou, Henan 450002, China;
    2. 61886 Troops of PLA, Beijing 100084, China
  • Received:2018-01-11 Revised:2018-06-12 Online:2019-02-25 Published:2019-02-25
    • Supported by:
    • National Natural Science Foundation of China (No.61401511, No.U1736107)

摘要: 针对非合作接收PCMA混合信号盲分离中高复杂度束缚,提出一种基于前馈神经网络的分离算法,通过搭建神经网络分离平台,规避传统的发送符号遍历思想,实现PCMA混合信号低复杂度高性能盲分离.仿真实验表明,神经网络能够极大挖掘信号内在信息,针对QPSK调制PCMA混合信号,在信噪比7dB时误比特率达到10-3数量级,并伴随着较PSP分离算法算术平方根级别的复杂度降低.

关键词: 神经网络, 非合作, 成对载波多址复用, 盲分离

Abstract: Aiming at the high complexity in blind separation of PCMA mixed signals with non-cooperative reception,the separation algorithm based on feedforward neural network is proposed.By setting up a neural network separation platform and avoiding the traditional idea of maximum a posteriori probability,the blind separation algorithm with low complexity and high performance can be realized.Simulation results show that the neural network can greatly exploit the intrinsic information of the signal,and 10-3 orders of bit error rate performance is achieved with 7 dB of signal-to-noise ratio to QPSK modulated PCMA signals,accompanied by the declining complexity of the arithmetic square root level compared with the PSP algorithm.

Key words: neural network, non-cooperative, Paired Carrier Multiple Access (PCMA), blind separation

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