Queueing Delay Performance of Schedulers in Centralized Sensing Cognitive Radio Networks
Wireless communication has become cost-effective and it can be established very rapidly in order to bring broadband access to under-served rural as well as urban area users. The underutilized spectrum has enormous, untapped, public capacity to provide high-speed and pervasive broadband connectivity. The wireless Radio Frequency (RF) spectrum is highly congested due to the continuous increase in the demand for high data rate services. Cognitive Radio (CR) allows secondary/unlicensed users to use a part/slice of a licensed spectrum, referred to as a channel, whenever the primary/licensed user is not transmitting in that channel during a period. In order to find the primary–free channels in the RF spectrum, CR performs spectrum sensing before transmitting its signal. When there are multiple secondary users trying to sense, find and access the available free channels it becomes very challenging. Scheduling resolves the problem by the use of queues. The behaviour of queues in terms of Queuing delay performance of Schedulers in Multiuser Cognitive Radio Networks when there is a Centralized Coordinator performs sensing and scheduling, has been studied. Three different scheduling policies are considered, namely Maximum Gain, Maximum Weight and Proportionally Fair scheduling. When the network is heavily loaded, each policy presents varied Queuing Delay Performance. The cases when average Signal-to-Noise Ratio of users is considered to be unequal have been analysed with varying normalized sensing duration. Queuing delay for various arrival rates of users is plotted and the performances have been compared and discussed. From the results, it can be concluded that the Maximum Weight Scheduler performs better than others do.
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Dimitri P. Bertsekas and Robert G. Gallagher, Data Networks (2nd Edition, Prentice Hall, Reprint 1992, ISBN 0132009161).
John N. Daigle, Queueing Theory for Telecommunications (Addison-Wesley Series in Telecommunications, 1991).
Leonard Kleinrock, Queueing Systems Volume J: Theory (A Wiley-Inter science Publication John Wiley & Sons Copyright © 1975, by John Wiley & Sons, Inc.).
Ross S.M., Introduction to probability models (95th edition San Diego: Academic Press, 1993).
Thomas Bonald and James Roberts, Scheduling Network Traffic, ACM SIGMETRICS Performance Evaluation Review Archive Volume 34 Issue 4, March 2007, Pages 29 - 35.
V.Hamsadhwani, Dr. Arun Pachai Kannu and Dr. K. Lakshmi, Stability of Queues in Multi-User Cognitive Radios, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 13 (2017) pp. 3810-3816 © Research India Publications http://www.ripublication.com.
V.Hamsadhwani, Dr. K. Lakshmi and Dr. Arun Pachai Kannu, Centralized Sensing and Scheduling in Multi-User Cognitive Radio Networks, International Journal of Applied Engineering Research ISSN 0973-4562 Volume13, Number 8(2018) pp. 6463–6471 Research India Publications. http://www.ripublication.com.
Misra R. and Kannu A.P., Optimal decentralized sensing-orders in multi-user cognitive radio networks, Global Communications Conference (GLOBECOM), 2012 IEEE (pp. 1447-1452), IEEE.
Misra R. and Kannu A.P., Optimal sensing-order in cognitive radio networks with cooperative centralized sensing, In 2012 IEEE International Conference on Communications (ICC), (pp. 1566-1570), IEEE.
Hai Jiang, Lifeng Lai, Rongfei Fan and H. Vincent Poor, Optimal Selection of Channel Sensing Order in Cognitive Radio, IEEE Transactions on Wireless Communications, Vol. 8, No. 1. pp 297-307, January 2009.
Fan R. and Jiang H., Channel sensing-order setting in cognitive radio networks: a two-user case, IEEE Trans. Vehicular Technology, Vol. 58, No. 9, pp. 4997–5008, Nov. 2009.
Cheng H.T. and Zhuang W., Simple channel sensing order in cognitive radio networks, IEEE Journal on Selected Areas in Communications, vol. 29, no. 4, pp. 1–13, 2011.
Lin, X. Shroff N.B. and Srikant R., A tutorial on cross-layer optimization in wireless networks, IEEE Journal on Selected Areas in Communications, vol. 24, pp. 1452–1463, Aug 2006.
Jain R., The Art of Computer Systems Performance Analysis (John Wiley and Sons, 1991).
Garg, R., Saluja, N., Implementation of Particle Swarm Optimization Technique for Spectrum Sensing in Cognitive Radio Network, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (5), pp. 412-420.
Astaiza, E., Salcedo, O., Correa, L., Efficient Wideband Spectrum Sensing: A Methodological Approach, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (4), pp. 306-315.
Hashmi, S., Sattar, S., Soundararajan, K., Multi Objective Coordination Approach for Resource Utilization in Heterogeneous Cognitive Radio Network, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (1), pp. 72-79.
Saifan, R., Qaisi, T., Sweidan, A., Alnabelsi, S., Darabkh, K., A Novel Reduced Sensing Time Routing Protocol in Cognitive Radio Networks, (2019) International Journal on Communications Antenna and Propagation (IRECAP), 9 (5), pp. 371-381.
Alnabelsi, S., Finding an Immuned Path Against Single Primary User Activity in Cognitive Radio Networks, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (7), pp. 562-571.
Esenogho, E., Srivastava, V., Channel Assembling Strategy in Cognitive Radio Networks: a Queuing Based Approach, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (1), pp. 31-47.
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