The use of artificial neural networks for classification of signal sources in cognitive radio systems

被引：10
|
作者：
Adjemov, S. S. [1 ]
Klenov, N. V. [1 ]
Tereshonok, M. V. [1 ]
Chirov, D. S. [1 ]
机构：
[1] Moscow Tech Univ Commun & Informat, Ul Aviamotornaya 8a, Moscow 111024, Russia
来源：
PROGRAMMING AND COMPUTER SOFTWARE | 2016年 / 42卷 / 03期
关键词：
Artificial Neural Network; Cognitive Radio; Radio Signal; Noise Immunity; Cognitive Radio System;
D O I：
10.1134/S0361768816030026
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
In the paper, methods of classification of signal sources in cognitive radio systems that are based on artificial neural networks are discussed. A novel method for improving noise immunity of RBF networks is suggested. It is based on introducing an additional self-organizing layer of neurons, which ensures automatic selection of variances of basis functions and a significant reduction of the network dimension. It is shown that the use of auto-associative networks in the problem of the classification of sources of signals makes it possible to minimize the feature space without significant deterioration of its separation properties.
引用
收藏
页码：121 / 128
页数：8
相关论文
共 50 条
  • [1] The use of artificial neural networks for classification of signal sources in cognitive radio systems
    S. S. Adjemov
    N. V. Klenov
    M. V. Tereshonok
    D. S. Chirov
    Programming and Computer Software, 2016, 42 : 121 - 128
  • [2] A neural-network method for the synthesis of informative features for the classification of signal sources in cognitive radio systems
    S. S. Adjemov
    N. V. Klenov
    M. V. Tereshonok
    D. S. Chirov
    Moscow University Physics Bulletin, 2016, 71 : 174 - 179
  • [3] A neural-network method for the synthesis of informative features for the classification of signal sources in cognitive radio systems
    Adjemov, S. S.
    Klenov, N. V.
    Tereshonok, M. V.
    Chirov, D. S.
    MOSCOW UNIVERSITY PHYSICS BULLETIN, 2016, 71 (02) : 174 - 179
  • [4] Cognitive radio signal classification based on subspace decomposition and RBF neural networks
    Centeno, Ludimila La Rosa
    Comparsi De Castro, Fernando Cesar
    Felippetto De Castro, Maria Cristina
    Muller, Candice
    Ribeiro, Sandro Machado
    WIRELESS NETWORKS, 2018, 24 (03) : 821 - 831
  • [5] Cognitive radio signal classification based on subspace decomposition and RBF neural networks
    Ludimila La Rosa Centeno
    Fernando César Comparsi De Castro
    Maria Cristina Felippetto De Castro
    Candice Müller
    Sandro Machado Ribeiro
    Wireless Networks, 2018, 24 : 821 - 831
  • [6] USE OF ARTIFICIAL IMMUNE SYSTEMS AND ARTIFICIAL NEURAL NETWORKS IN PROSTATE CANCER CLASSIFICATION
    Ozsen, Seral
    Cingilli Vural, Hasibe
    2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2014, : 87 - 90
  • [7] Artificial Neural Networks for Cognitive Radio: A Preliminary Survey
    Pattanayak, S.
    Venkateswaran, P.
    Nandi, R.
    2012 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING (WICOM), 2012,
  • [8] Microseismic Signal Classification Based on Artificial Neural Networks
    Xin, Chong-wei
    Jiang, Fu-xing
    Jin, Guo-dong
    SHOCK AND VIBRATION, 2021, 2021
  • [9] Handoff security using artificial neural networks in cognitive radio networks
    2020, Institute of Electrical and Electronics Engineers Inc. (03):
  • [10] Frequency Allocation with Artificial Neural Networks in Cognitive Radio System
    Tan, Xuezhi
    Huang, Haoqin
    Ma, Lin
    2013 IEEE TENCON SPRING CONFERENCE, 2013, : 366 - 370
12345