Behavior modeling for spectrum sharing in wireless cognitive networks

  • Authors:

  • Yinglei Teng;F. Richard Yu;Yifei Wei;Li Wang;Yong Zhang

  • Affiliations:

  • Beijing University of Posts and Telecommunications, Beijing, China;Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada;Beijing University of Posts and Telecommunications, Beijing, China;Beijing University of Posts and Telecommunications, Beijing, China;Beijing University of Posts and Telecommunications, Beijing, China

  • Venue:
  • Wireless Networks
  • Year:
  • 2012

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Abstract

Cognitive networks are designed based on the concept of dynamic and intelligent network management, characterizing the feature of self-sensing, self-configuration, self-learning, self-consciousness etc. In this paper, focusing on the spectrum sharing and competition, we propose a novel OODA (Orient-Observe-Decide-Act) based behavior modeling methodology to illustrate spectrum access problem in the heterogenous cognitive network which consists of multiple primary networks (PN, i.e. licensed networks) and multiple secondary networks (SN, i.e. unlicensed networks). Two different utility functions are designed for primary users and secondary users respectively based on marketing mechanism to formulate the decide module mathematically. Also, we adopt expectation and learning process in the utility design which considers the variance of channels, transmission forecasting, afore trading histories and etc. A double auction based spectrum trading scheme is established and implemented in two scenarios assorted from the supply-and-demand relationship i.e. LPMS (Less PNs and More SNs) and MPLS (More PNs and Less SNs). After the discussion of the Bayesian Nash Equilibrium, numerical results with four bidding strategies of SNs are presented to reinforce the effectiveness of the proposed utility evaluation based decision modules under two scenarios. Besides, we prove that the proposed behavior model based spectrum access method maintains frequency efficiency comparable with traditional centralized cognitive access approaches and reduces the network deployment cost.