LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Reverse spectrum auction algorithm for cellular network offloading

Photo from wikipedia

Abstract The explosion of mobile traffic and highly dynamic property often make it increasingly stressful for a cellular service provider to provide sufficient cellular spectrum resources to support the dynamic… Click to show full abstract

Abstract The explosion of mobile traffic and highly dynamic property often make it increasingly stressful for a cellular service provider to provide sufficient cellular spectrum resources to support the dynamic change of traffic demand in a day. In this paper, considering the dynamic characteristics of the cellular network traffic demands, we propose an optimal, truthful reverse auction incentive framework, which can minimize the leasing costs sustained by the mobile network operator at the premise of meeting the traffic demand of each time period. Such an issue is formulated as an Integer Programming (IP) optimization problem and we use an adaptive Lagrangian relaxation algorithm to solve the optimal reverse auction allocation problem. Besides, we propose a payment rule satisfying the truthfulness property (incentive compatibility) and the individual rationality property. Numerical results demonstrate that our proposed adaptive algorithm well captures the economical and networking essence of the reverse auction allocation problem, thus representing a promising approach to solve the optimal reverse auction allocation problem.

Keywords: traffic; cellular network; reverse; auction; reverse auction

Journal Title: Ad Hoc Networks
Year Published: 2017

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.