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ZTE Communications ›› 2009, Vol. 7 ›› Issue (2): 6-10.

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Cognitive Engine Technology

Wang Lifeng, Wei Shengqun   

  1. Institute of Chinese Electronic Equipment System Enginee ring Corporation , Beijing 100141 , P . R . China
  • 出版日期:2009-06-25 发布日期:2020-03-03
  • 作者简介:Wang Lifeng, PhD, graduated from PLA University of Science and Technology, and now is a senior engineer of Institute of Chinese Electronic Equipment System Engineering Corporation. His research interests include mobile Ad hoc networks and cognitive radio networks. He has participated in or presided about ten projects, including major military communication research, and projects funded by the "973" program and the National Natural Science Foundation. He won multiple Science Progress Awards including one first prize of National Science Progress Award, and has published more than 20 papers.

    Wei Shengqun, PhD, graduated from Institute of Communications Engineering, PLA University of Science and Technology, and now is an engineer of Institute of Chinese Electronic Equipment System Engineering Corporation. His research interests include spread spectrum communications, adaptive equalization, Turbo equalization and cognitive radio technology. He has participated in multiple research projects, and published more than 10 papers.
  • 基金资助:
    The work was supported by the National Basic Research Program of China("973" Program) under Grant No. 2009CB320403, and the National Natural Science Foundation of China under Grant No. 60832008.

Cognitive Engine Technology

Wang Lifeng, Wei Shengqun   

  1. Institute of Chinese Electronic Equipment System Enginee ring Corporation , Beijing 100141 , P . R . China
  • Online:2009-06-25 Published:2020-03-03
  • About author:Wang Lifeng, PhD, graduated from PLA University of Science and Technology, and now is a senior engineer of Institute of Chinese Electronic Equipment System Engineering Corporation. His research interests include mobile Ad hoc networks and cognitive radio networks. He has participated in or presided about ten projects, including major military communication research, and projects funded by the "973" program and the National Natural Science Foundation. He won multiple Science Progress Awards including one first prize of National Science Progress Award, and has published more than 20 papers.

    Wei Shengqun, PhD, graduated from Institute of Communications Engineering, PLA University of Science and Technology, and now is an engineer of Institute of Chinese Electronic Equipment System Engineering Corporation. His research interests include spread spectrum communications, adaptive equalization, Turbo equalization and cognitive radio technology. He has participated in multiple research projects, and published more than 10 papers.
  • Supported by:
    The work was supported by the National Basic Research Program of China("973" Program) under Grant No. 2009CB320403, and the National Natural Science Foundation of China under Grant No. 60832008.

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摘要： Cognitive Radio (CR ) is an intelligent radio communication system, whose intelligence mostly comes from the Cognitive Engine (CE ). Based on the techniques of software-defined radio and with the support of machine reasoning and learning in artificial intelligence, cognitive engine implements the cognitive loop to realize the abilities of sensing, adaptation and learning in CR. Cognitive engine consists of the modeling system, knowledge base, reasoning engine, learning engine and interfaces. The key techniques are knowledge representation, machine reasoning and machine learning.

Abstract: Cognitive Radio (CR ) is an intelligent radio communication system, whose intelligence mostly comes from the Cognitive Engine (CE ). Based on the techniques of software-defined radio and with the support of machine reasoning and learning in artificial intelligence, cognitive engine implements the cognitive loop to realize the abilities of sensing, adaptation and learning in CR. Cognitive engine consists of the modeling system, knowledge base, reasoning engine, learning engine and interfaces. The key techniques are knowledge representation, machine reasoning and machine learning.