Optimal channel-sensing policy based on Fuzzy Q-Learning process over cognitive radio systems

Fereidoun H. Panahi, Tomoaki Ohtsuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

In a cognitive radio (CR) network, the channel sensing scheme to detect the appearance of a primary user (PU) directly affects the performances of both CR and PU. However, in practical systems, the CR is prone to sensing errors due to inefficient sensing scheme. This may lead to interfering with primary user and low system performance. In this paper, we present a learning based scheme for channel sensing in CR network. Specifically, we formulate the channel sensing problem as a partially observable Markov decision process (POMDP), where the most likely channel state is derived by a learning process called Fuzzy Q-Learning (FQL). The optimal policy is derived by solving the problem. The simulation results show the effectiveness and efficiency of our proposed scheme.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Communications, ICC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2677-2682
Number of pages6
ISBN (Print)9781467331227
DOIs
Publication statusPublished - 2013 Jan 1
Event2013 IEEE International Conference on Communications, ICC 2013 - Budapest, Hungary
Duration: 2013 Jun 92013 Jun 13

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607

Other

Other2013 IEEE International Conference on Communications, ICC 2013
Country/TerritoryHungary
CityBudapest
Period13/6/913/6/13

Keywords

  • Cognitive Radio (CR)
  • Fuzzy Q-Learning (FQL)
  • Reinforcement learning (RL)
  • channel sensing
  • partially observable Markov decision process (POMDP)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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