Dynamic pricing of call rates: Bayesian approach

In this paper, we present different cases and their possible solutions in the telecommunications market by incorporating dynamically changing call rates over the channel depending upon the network congestion. Since dynamic pricing of call rates is beneficial from both the perspectives of subscribers and service providers, our solution can significantly help to adapt this pricing mechanism in real market scenario. In order to deploy this scheme, we have incorporated the competing network provider's strategy into the mechanism of deciding dynamic price. Establishment of Nash equilibrium with the competing network provider has stabilized our pricing mechanism. We implement dynamic pricing scheme against the static scheme for voice call rates.Our pricing scheme uses the opponent network provider's pricing mechanism into consideration.We establish Nash equilibrium with the opponent network provider's pricing strategy.We show how Bayesian approach is best suited for the studied market scenario.Our scheme even reduces call blocking probability to minimum level.

[1]  WiFi access point pricing as a dynamic game , 2006, IEEE/ACM Trans. Netw..

[2]  M. Sorell,et al.  Signaling Requirements for Smart Dynamic Pricing in Cellular Networks , 2006, 2006 First International Conference on Communications and Networking in China.

[3]  Athanasios D. Panagopoulos,et al.  A survey on game theory applications in wireless networks , 2010, Comput. Networks.

[4]  Antoni Barba Marti,et al.  A QoS-based dynamic pricing approach for services provisioning in heterogeneous wireless access networks , 2011, Pervasive Mob. Comput..

[5]  Hossam S. Hassanein,et al.  Efficient bandwidth management in Broadband Wireless Access Systems using CAC-based dynamic pricing , 2008, 2008 33rd IEEE Conference on Local Computer Networks (LCN).

[6]  Charles R. Plott,et al.  The control of game form recognition in experiments: understanding dominant strategy failures in a simple two person “guessing” game , 2009 .

[7]  Antoni Barba Marti,et al.  A Quality of Service-Enabled Pricing Approach for Heterogeneous Wireless Access Networks , 2010, 2010 Sixth International Conference on Intelligent Environments.

[8]  Bruce Bueno de Mesquita,et al.  An Introduction to Game Theory , 2014 .

[9]  Srinivas Shakkottai,et al.  Economics of network pricing with multiple ISPs , 2006 .

[10]  Sam C. M. Lee,et al.  Interplay of ISPs: Distributed Resource Allocation and Revenue Maximization , 2006, 26th IEEE International Conference on Distributed Computing Systems (ICDCS'06).

[11]  Shamik Sengupta,et al.  Dynamic pricing for service provisioning and network selection in heterogeneous networks , 2009, Phys. Commun..

[12]  Shengkai Zhang,et al.  Introduction to game theory , 2003 .

[13]  David A. Malueg Mixed-strategy equilibria in the Nash Demand Game , 2005 .

[14]  Tilman Wolf,et al.  A Cournot–Nash–Bertrand game theory model of a service-oriented Internet with price and quality competition among network transport providers , 2014, Comput. Manag. Sci..

[15]  J. Walrand,et al.  Pricing Internet Services With Multiple Providers ∗ , 2003 .

[16]  John C. Harsanyi,et al.  Games with Incomplete Information Played by "Bayesian" Players, I-III: Part I. The Basic Model& , 2004, Manag. Sci..