Reciprocally opportunistic spectrum access

Xianfu Chen (Corresponding Author), H Zhang, Marko Höyhtyä, Mika Lasanen, J Palicot

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The problem of opportunistic spectrum access in cognitive radio networks (CRNs) is considered in this paper. Especially, we discuss designing distributed learning protocols for non-cooperative secondary users (SUs) to ef?ciently utilise the spectrum opportunities. It is well known that the SUs' sel?sh behaviors will result in a network collapse. Each SU is thus enabled to form conjectures over how the competing SUs would respond to its decision-makings and believes that they alter future spectrum access strategies in proportion to its own current strategy variation. The conjectural belief adapts in accordance with local observations. The interaction among SUs is modelled as a stochastic learning procedure, in which each autonomous SU behaves as an intelligent agent. In this way, all SUs can independently 'learn' the behaviors of their competitors, optimise the opportunistic spectrum access strategies and ?nally achieve the goal of reciprocity in CRNs. Based on the belief model, two learning mechanisms, that is, the best response learning algorithm and the gradient ascent learning algorithm, are proposed to stabilise the opportunistic CRNs. We also theoretically prove that the SUs' stochastic behaviors and beliefs converge to the conjectural equilibrium under some conditions rising from the learning procedure. Numerical results are provided to evaluate the performance of the proposed mechanisms, and show that the achieved system performance gain outperforms the multi-agent Q-learning scheme and the Nash equilibrium scheme when there are numerous SUs in the CRN
Original languageEnglish
Pages (from-to)1073-1085
JournalTransactions on Emerging Telecommunications Technologies
Volume26
Issue number8
DOIs
Publication statusPublished - 2015
MoE publication typeA1 Journal article-refereed

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Cognitive radio
Learning algorithms
Intelligent agents
Decision making
Network protocols

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title = "Reciprocally opportunistic spectrum access",
abstract = "The problem of opportunistic spectrum access in cognitive radio networks (CRNs) is considered in this paper. Especially, we discuss designing distributed learning protocols for non-cooperative secondary users (SUs) to ef?ciently utilise the spectrum opportunities. It is well known that the SUs' sel?sh behaviors will result in a network collapse. Each SU is thus enabled to form conjectures over how the competing SUs would respond to its decision-makings and believes that they alter future spectrum access strategies in proportion to its own current strategy variation. The conjectural belief adapts in accordance with local observations. The interaction among SUs is modelled as a stochastic learning procedure, in which each autonomous SU behaves as an intelligent agent. In this way, all SUs can independently 'learn' the behaviors of their competitors, optimise the opportunistic spectrum access strategies and ?nally achieve the goal of reciprocity in CRNs. Based on the belief model, two learning mechanisms, that is, the best response learning algorithm and the gradient ascent learning algorithm, are proposed to stabilise the opportunistic CRNs. We also theoretically prove that the SUs' stochastic behaviors and beliefs converge to the conjectural equilibrium under some conditions rising from the learning procedure. Numerical results are provided to evaluate the performance of the proposed mechanisms, and show that the achieved system performance gain outperforms the multi-agent Q-learning scheme and the Nash equilibrium scheme when there are numerous SUs in the CRN",
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Reciprocally opportunistic spectrum access. / Chen, Xianfu (Corresponding Author); Zhang, H; Höyhtyä, Marko; Lasanen, Mika; Palicot, J.

In: Transactions on Emerging Telecommunications Technologies, Vol. 26, No. 8, 2015, p. 1073-1085.

Research output: Contribution to journalArticleScientificpeer-review

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AB - The problem of opportunistic spectrum access in cognitive radio networks (CRNs) is considered in this paper. Especially, we discuss designing distributed learning protocols for non-cooperative secondary users (SUs) to ef?ciently utilise the spectrum opportunities. It is well known that the SUs' sel?sh behaviors will result in a network collapse. Each SU is thus enabled to form conjectures over how the competing SUs would respond to its decision-makings and believes that they alter future spectrum access strategies in proportion to its own current strategy variation. The conjectural belief adapts in accordance with local observations. The interaction among SUs is modelled as a stochastic learning procedure, in which each autonomous SU behaves as an intelligent agent. In this way, all SUs can independently 'learn' the behaviors of their competitors, optimise the opportunistic spectrum access strategies and ?nally achieve the goal of reciprocity in CRNs. Based on the belief model, two learning mechanisms, that is, the best response learning algorithm and the gradient ascent learning algorithm, are proposed to stabilise the opportunistic CRNs. We also theoretically prove that the SUs' stochastic behaviors and beliefs converge to the conjectural equilibrium under some conditions rising from the learning procedure. Numerical results are provided to evaluate the performance of the proposed mechanisms, and show that the achieved system performance gain outperforms the multi-agent Q-learning scheme and the Nash equilibrium scheme when there are numerous SUs in the CRN

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