A differential game theoretic model for real-time spectrum pricing in cognitive radio networks

Dong Hao, Atsushi Iwasaki, Makoto Yokoo

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

1 Citation (Scopus)

Abstract

In cognitive radio networks, one key feature of spectrum trading is its short term or, even, real time, since the spectrum availability, quality, and price keep changing over time. Therefore, a spectrum pricing policy should be dynamically optimal. In this work, we address the real-time optimal pricing problem for primary users. Based on differential game model, we analyze the optimal pricing strategy for QoS-aware dynamic networks in which the secondary users' number and primary users' QoS level keep changing over time. Nash equilibrium is derived and an optimal pricing and QoS setting policy is formulated. Since the Nash equilibrium of our differential game based model deals with optimal pricing in each time instance, the real-time optimal pricing characteristic can be realized.

Original languageEnglish
Title of host publicationProceedings of the 37th Annual IEEE Conference on Local Computer Networks, LCN 2012
Pages236-239
Number of pages4
DOIs
Publication statusPublished - Dec 1 2012
Event37th Annual IEEE Conference on Local Computer Networks, LCN 2012 - Clearwater, FL, United States
Duration: Oct 22 2012Oct 25 2012

Publication series

NameProceedings - Conference on Local Computer Networks, LCN

Other

Other37th Annual IEEE Conference on Local Computer Networks, LCN 2012
CountryUnited States
CityClearwater, FL
Period10/22/1210/25/12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture

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  • Cite this

    Hao, D., Iwasaki, A., & Yokoo, M. (2012). A differential game theoretic model for real-time spectrum pricing in cognitive radio networks. In Proceedings of the 37th Annual IEEE Conference on Local Computer Networks, LCN 2012 (pp. 236-239). [6423617] (Proceedings - Conference on Local Computer Networks, LCN). https://doi.org/10.1109/LCN.2012.6423617