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Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning

Title:

Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning

Alsarhan, Ayoub and Agarwal, Anjali (2012) Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning. Journal of Electrical and Computer Engineering, 2012 . pp. 1-12. ISSN 2090-0147

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Official URL: http://dx.doi.org/10.1155/2012/562615

Abstract

In a cognitive wireless mesh network, licensed users (primary users, PUs) may rent surplus spectrum to unlicensed users (secondary users, SUs) for getting some revenue. For such spectrum sharing paradigm, maximizing the revenue is the key objective of the PUs while that of the SUs is to meet their requirements. These complex contradicting objectives are embedded in our reinforcement learning (RL) model that is developed and implemented as shown in this paper. The objective function is defined as the net revenue gained by PUs from renting some of their spectrum. RL is used to extract the optimal control policy that maximizes the PUs’ profit continuously over time. The extracted policy is used by PUs to manage renting the spectrum to SUs and it helps PUs to adapt to the changing network conditions. Performance evaluation of the proposed spectrum trading approach shows that it is able to find the optimal size and price of spectrum for each primary user under different conditions. Moreover, the approach constitutes a framework for studying, synthesizing and optimizing other schemes. Another contribution is proposing a new distributed algorithm to manage spectrum sharing among PUs. In our scheme, PUs exchange channels dynamically based on the availability of neighbor’s idle channels. In our cooperative scheme, the objective of spectrum sharing is to maximize the total revenue and utilize spectrum efficiently. Compared to the poverty-line heuristic that does not consider the availability of unused spectrum, our scheme has the advantage of utilizing spectrum efficiently.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Article
Refereed:Yes
Authors:Alsarhan, Ayoub and Agarwal, Anjali
Journal or Publication:Journal of Electrical and Computer Engineering
Date:2012
Digital Object Identifier (DOI):10.1155/2012/562615
ID Code:975143
Deposited By: Danielle Dennie
Deposited On:18 Jan 2013 14:15
Last Modified:18 Jan 2018 17:39

References:

I. F. Akyildiz, W. Y. Lee, M. C. Vuran, and S. Mohanty, “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey,” Computer Networks, vol. 50, no. 13, pp. 2127–2159, 2006.

Hossain and V. K. Bhargava, Cognitive Wireless Communication Networks, Springer, New York, NY, USA, 2007. I. F. Akyildiz, X. Wang, and W. Wang, “Wireless mesh networks: a survey,” Computer Networks, vol. 47, no. 4, pp. 445–487, 2005.

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, The MIT Press, Cambridge, Mass, USA, 1998. R. K. Lam, J. C. S. Lui, and C. Dah-Ming, “On the access pricing issues of wireless mesh networks,” in Proceedings of the IEEE International Conference on Distributed Computing Systems (ICDCS '06), pp. 61–61, 2006.

Z. Ji and K. J. R. Liu, “Belief-assisted pricing for dynamic spectrum allocation in wireless networks with selfish users,” in Proceedings of the 3rd Annual IEEE Communications Society on Sensor and Ad hoc Communications and Networks (SECON '06), pp. 119–127, September 2006.

O. Simeone, I. Stanojev, S. Savazzi, Y. Bar-Ness, U. Spagnolini, and R. Pickholtz, “Spectrum leasing to cooperating secondary ad hoc networks,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 1, pp. 203–213, 2008.

D. Niyato and E. Hossain, “Competitive spectrum sharing in cognitive radio networks: a dynamic game approach,” IEEE Transactions on Wireless Communications, vol. 7, no. 7, pp. 2651–2660, 2008.

D. Niyato and E. Hossain, “Market-equilibrium, competitive, and cooperative pricing for spectrum sharing in cognitive radio networks: analysis and comparison,” IEEE Transactions on Wireless Communications, vol. 7, no. 11, pp. 4273–4283, 2008.

M. M. Bykowsky, M. Olson, and W. W. Sharkey, “Efficiency gains from using a market approach to spectrum management,” Information Economics and Policy, vol. 22, no. 1, pp. 73–90, 2010.

H. Sartono, Y. H. Chew, W. H. Chin, and C. Yuen, “Joint demand and supply auction pricing strategy in dynamic spectrum sharing,” in Proceedings of the IEEE 20th Personal, Indoor and Mobile Radio Communications Symposium (PIMRC '09), pp. 833–837, September 2009.

S. Sengupta and M. Chatterjee, “Sequential and concurrent auction mechanisms for dynamic spectrum access,” in Proceedings of the 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom '07), pp. 448–455, August 2007.

O. Ileri, D. Samardzija, T. Sizer, and N. B. Mandayam, “Demand responsive pricing and competitive spectrum allocation via a spectrum server,” in Proceedings of the 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN '05), pp. 194–202, November 2005.

G. Işiklar and A. B. Bener, “Brokering and pricing architecture over cognitive radio wireless networks,” in Proceedings of the 5th IEEE Consumer Communications and Networking Conference (CCNC '08), pp. 1004–1008, January 2008.

M. M. Buddhikot, P. Kolody, S. Miller, K. Ryan, and J. Evans, “DIMSUMNet: new directions inwireless networking using coordinated dynamic spectrum access,” in Proceedings of the IEEE WoWMoM, pp. 78–85, 2005.

L. Cao and H. Zheng, “Distributed rule-regulated spectrum sharing,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 1, pp. 130–145, 2008.

A. Alsarhan and A. Agarwal, “Cluster-based spectrum management using cognitive radios in wireless mesh network,” in Proceedings of the 18th International Conference on Computer Communications and Networks (ICCCN '09), San Francisco, Calif, USA, August 2009.

P. Beckmann, Elementary Queuing Theory and Telephone Traffic, Series on Telephone Traffic, Lee's ABC of the Telephone, Geneva, Switzerland, 1977.

G. Gallego and G. van Ryzin, “Optimal dynamic pricing of inventories with stochastic demand over finite horizons,” Management Science, vol. 40, no. 8, pp. 999–1020, 1994.
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