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Economy of Spectrum Access in Time Varying Multichannel Networks
October 2010 (vol. 9 no. 10)
pp. 1361-1376
M.H.R. Khouzani, University of Pennsylvania, Philadelphia
Saswati Sarkar, University of Pennsylvania, Philadelphia
We consider a wireless network consisting of two classes of potentially mobile users: primary users and secondary users. Primary users license frequency channels and transmit in their respective bands as required. Secondary users resort to unlicensed access of channels that are not used by their primary users. Primaries impose access fees on the secondaries which depend on access durations and may be different for different primary channels and different available communication rates in the channels. The available rates to the secondaries change with time depending on the usage status of the primaries and the random access quality of channels. Secondary users seek to minimize their total access cost subject to stabilizing their queues whenever possible. Our first contribution is to present a dynamic link scheduling policy that attains this objective. The computation time of this policy, however, increases exponentially with the size of the network. We next present an approximate scheduling scheme based on graph partitioning that is distributed and attains arbitrary trade-offs between aggregate access cost and computation times of the schedules, irrespective of the size of the network. Our performance guarantees hold for general arrival and primary usage statistics and multihop networks. Each secondary user is, however, primarily interested in minimizing the cost it incurs, rather than in minimizing the aggregate cost. Thus, it will schedule its transmissions so as to minimize the aggregate cost only if it perceives that the aggregate cost is shared among the users as per a fair cost sharing scheme. Using concepts from cooperative game theory, we develop a rational basis for sharing the aggregate cost among secondary sessions and present a cost sharing mechanism that conforms to the above basis.

[1] M. McHenry, "NSF Spectrum Occupancy Measurements Project Summary," technical report, Shared Spectrum Company, 2005.
[2] T. Weiss and F. Jondral, "Spectrum Pooling: An Innovative Strategy for the Enhancement of Spectrum Efficiency," IEEE Comm. Magazine, vol. 42, no. 3, pp. S8-14, Mar. 2004.
[3] R. Tandra, S. Mishra, and A. Sahai, "What Is a Spectrum Hole and What Does It Take to Recognize One?" Proc. IEEE, vol. 97, no. 5, pp. 824-848, May 2008.
[4] 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.
[5] Q. Zhao and B. Sadler, "A Survey of Dynamic Spectrum Access," IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 79-89, 2007.
[6] W. Xu, P. Kamat, and W. Trappe, "TRIESTE: A Trusted Radio Infrastructure for Enforcing SpecTrum Etiquettes," Proc. First IEEE Workshop Networking Technologies for Software Defined Radio Networks (SDR '06), 2006.
[7] W. Lehr and J. Crowcroft, "Managing Shared Access to a Spectrum Commons," Proc. First IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN '05), 2005.
[8] C. Kloeck, H. Jaekel, and F. Jondral, "Dynamic and Local Combined Pricing, Allocation and Billing System with Cognitive Radios," Proc. First IEEE Int'l Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN '05), 2005.
[9] M. Andrews, "Maximizing Profit in Overloaded Networks," Proc. IEEE INFOCOM, 2005.
[10] M. Neely, E. Modiano, and C. Li, "Fairness and Optimal Stochastic Control for Heterogeneous Networks," IEEE/ACM Trans. Networking, vol. 16, no. 2, pp. 396-409, Apr. 2008.
[11] A. Eryilmaz and R. Srikant, "Joint Congestion Control, Routing, and MAC for Stability and Fairness in Wireless Networks," IEEE J. Selected Areas in Comm., vol. 24, no. 8, pp. 1514-1524, Aug. 2006.
[12] P. Chaporkar and S. Sarkar, "Stable Scheduling Policies for Maximizing Throughput in Generalized Constrained Queueing Systems," IEEE Trans. Automatic Control, vol. 53, no. 8, pp. 1913-1931, Sept. 2008.
[13] M. Neely, "Energy Optimal Control for Time Varying Wireless Networks," IEEE Trans. Information Theory, vol. 52, no. 7, pp. 2915-2934, July 2006.
[14] C. Peng, H. Zheng, and B. Zhao, "Utilization and Fairness in Spectrum Assignment for Opportunistic Spectrum Access," Mobile Networks and Applications, Springer, 2006.
[15] L. Cao and H. Zheng, "Distributed Spectrum Allocation via Local Bargaining," Proc. IEEE CS Conf. Sensor and Ad Hoc Comm. and Networks, 2005.
[16] R. Urgaonkar and M. Neely, "Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks," IEEE Trans. Mobile Computing, vol. 8, no. 6, pp. 766-777, June 2008.
[17] L. Tassiulas, "Linear Complexity Algorithms for Maximum Throughput in Radionetworks and Input Queued Switches," Proc. IEEE INFOCOM, 1998.
[18] P. Chaporkar, K. Kar, and S. Sarkar, "Throughput Guarantees through Maximal Scheduling in Wireless Networks," Proc. 43rd Ann. Allerton Conf. Comm., Control and Computing, 2005.
[19] A. Eryilmaz, A. Ozdaglar, and E. Modiano, "Polynomial Complexity Algorithms for Full Utilization of Multi-Hop Wireless Networks," Proc. IEEE INFOCOM, 2007.
[20] A. Gupta, X. Lin, and R. Srikant, "Low-Complexity Distributed Scheduling Algorithms for Wireless Networks," IEEE/ACM Trans. Networking, vol. 17, no. 6, pp. 1846-1859, Dec. 2009.
[21] Y. Yi and M. Chiang, "Wireless Scheduling Algorithms with o(1) Overhead for M-Hop Interference Model," Proc. IEEE Int'l Conf. Comm. (ICC '08), 2008.
[22] X. Lin and S. Rasool, "Constant-Time Distributed Scheduling Policies for Ad Hoc Wireless Networks," IEEE Trans. Automatic Control, vol. 54, no. 2, pp 231-242, Feb. 2009.
[23] X. Lin and N. Shroff, "The Impact of Imperfect Scheduling on Cross-Layer Congestion Control in Wireless Networks," IEEE/ACM Trans. Networking, vol. 14, no. 2, pp. 302-315, Nov. 2006.
[24] S. Sarkar and S. Ray, "Arbitrary Throughput versus Complexity Tradeoffs in Wireless Networks Using Graph Partitioning," IEEE Trans. Automatic Control, vol. 53, no. 10, pp. 2307-2323, 2008.
[25] U. Kozat, I. Koutsopoulos, and L. Tassiulas, "Cross-Layer Design for Power Efficiency and QoS Provisioning in Multi-Hop Wireless Networks," IEEE Trans. Wireless Comm., vol. 5, no. 11, pp. 3306-3315, 2006.
[26] G. Sharma, R. Mazumdar, and N. Shroff, "On the Complexity of Scheduling in Wireless Networks," Proc. 12th Ann. Int'l Conf. Mobile Computing and Networking, pp. 227-238, 2006.
[27] D. Shah, P. Giaccone, and B. Prabhakar, "Efficient Randomized Algorithms for Input-Queued Switch Scheduling," IEEE Micro, vol. 22, no. 1, pp. 10-18, Jan. 2002.
[28] M. Khouzani and S. Sarkar, "Cost Efficient Secondary Access in Time Varying Cognitive Networks," technical report, Univ. of Pennsylvania, 2008.
[29] C. Courcoubetis and R. Weber, Pricing Communication Networks. Wiley Hoboken, 2003.
[30] M. Osborne and A. Rubinstein, A Course in Game Theory. MIT Press, 1994.
[31] J. MacKie-Mason and H. Varian, "Pricing Congestible Network Resources," IEEE J. Selected Areas in Comm., vol. 13, no. 7, pp. 1141-1149, Sept. 1995.
[32] L. Georgiadis, L. Tassiulas, and M. Neely, Resource Allocation and Cross Layer Control in Wireless Networks. Now Publishers, Inc., 2006.

Index Terms:
Stochastic network optimization, cognitive networks, economy of spectrum access, imperfect scheduling, graph partitioning, cost sharing, Shapley value.
Citation:
M.H.R. Khouzani, Saswati Sarkar, "Economy of Spectrum Access in Time Varying Multichannel Networks," IEEE Transactions on Mobile Computing, vol. 9, no. 10, pp. 1361-1376, Oct. 2010, doi:10.1109/TMC.2010.90
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