Avijit Bose* | Prasenjit Das | Sumit Majumdar
OPEN ACCESS
Pricing plays an important role in Network dynamics. In Cognitive Radio there are two classes- licensed and unlicensed users. So in this paper a mathematical model is derived trying to show the effect of competition can lead to channel allocation problem at the cost of revenue. Though the model reaches Nash Equilibrium but MM1/Q model really gives an idea how channel allocation problem is giving rise to Cognitive theory concept.
Dynamic Spectrum Allocation, Cognitive Radio, Queuing theory, Profit margin.
[1] Jin et al., A highly available spectrum allocation service model in dynamic spectrum market, Future generation, 2011, Computer Systems, pp. 940-946.
[2] S. Sengupta, M. Chatterjee, S. Ganguly, An economic framework for spectrum allocation and service pricing with competitive wireless service provider, 2007, Proceedings of IEEE DySPAN, Dublin, Ireland, pp. 89-98.
[3] J. Chen, et al., A Hybrid game model based on reputation for spectrum allocation in wireless network, 2010, Computer Communication, vol. 33 pp. 1623-1631.
[4] Chetan Dugar, et al., Dynamic pricing of call rates: Bayesian approach, 2014, Information Processing Letters, pp. 237-242.
[5] Benoit et al., Shared or exclusive radio wave? A dilemma gone astray, 2010, pp. 293-304.
[6] Sen et al., Coverage and capacity optimization in LTE Networks based on non-cooperative games, 2012, Journal of China Universities of Posts and Telecommunication, pp. 14-21.
[7] J. Miao,, et al., Stackelberg game theoretic pricing algorithm for bandwidth allocation in cooperative access, 2012, Journal of China Universities of Posts & Telecommunication, pp. 34-42.
[8] Toka et al., General distributed economic framework for dynamic spectrum allocation, 2009, Computer Communication, pp. 1955-1964.
[9] Boix et al., A pricing method for elastic services that guarantees the GoS in a scenario of evolutionary demand, 2013, Computer Communication, pp.1317-1328.
[10] Wang et al., Analysis of dynamic spectrum management for secondary network, IWIEE, 2012, pp. 2470-2474.
[11] Sridhar et al., Flexible spectrum management for mobile broadband services: how does it vary across advanced emerging markets? 2013, Telecommunication Policy, pp. 178-191.
[12] Basure et al., Implication of dynamic spectrum management for regulation, Telecommunication Policy (in Press), 2015.
[13] Rouskas et al., A game theoretical formulation of integrated admission, Control and pricing in wireless networks, 2008, European Journal of Operation Research, vol. 191, pp. 1175-1188.
[14] Bose et al., Detecting the migration of mobile service customers using fuzzy clustering, 2015, Information & Management, vol. 52, pp. 227–238.
[15] Zhen et al., Efficient wireless packet scheduling in a non-cooperative environment: Game theoretic analysis and algorithms, 2010, J. Parallel Distrib. Comput, vol. 70, pp. 790-799.
[16] Paul et al., A sustainable and collaborative strategy for dynamic spectrum management in next generation wireless networks, 2013, Engineering Applications of Artificial Intelligence, Vol. 26, pp 1620–1630, 2013.
[17] Zhang et al., Hybrid model of inter-stage spectrum trading in multistage game-theoretic framework, 2011, The Journal of China Universities of Posts and Telegraph, vol. 18, no. 2, pp. 78–85.
[18] D. Wu et al., Research on pricing game strategy for load-balancing in VANET, 2013, The Journal of China Universities of Posts and Telegraph, vol. 20, no. 1, pp. 73–78.
[19] Niyato et al., QoS aware bandwidth allocation and admission control in IEEE802.16 broadband wireless access Network: A non-cooperative game theoretic approach, 2007, Computer Networks, pp. 3305-3321.
[20] Attar et al Challenges of real time secondary usage spectrum, 2008, Computer Networks, pp. 816-830.
[21] Yuedong et al. An oligopoly spectrum allocation game in cognitive radio network with capacity constraint, 2010, Computer Networks, pp. 925-943.
[22] Wang et al., Game Theory for cognitive radio network – An Overview Computer Networks, 2010, pp. 2537-2561. 146
[23] Economics for Engineers, H.L. Ahuja, PHI, 2003, pp. 273-346.
[24] Operations Research, V.K.Kapoor S Chand & Sons, 1997, pp. 9.1-9.38. 147