Effective Capacity Analysis for Cognitive Networks under QoS Satisfaction

Mohamed Elalem

Abstract


Spectrum sensing and dynamic spectrum access (DSA) techniques in cognitive radio networks (CRN) have been extensively investigated since last decade. Recently, satisfaction of quality-of-service (QoS) demands for secondary users (SU) has attracted great attention. The SU can not only discover the transmission opportunities, but also cognitively adapts the dynamic spectrum access strategies to its own QoS requirement and the environment variations. In this paper, we study how the delay QoS requirement affects the strategy on network performance. We first treat the delay-QoS in interference constrained cognitive radio network by applying the effective capacity concept, focusing on the two dominant DSA schemes: underlay and overlay. We obtain the effective capacity of the secondary network and determine the power allocation policies that maximize the throughput of the cognitive user. The underlay and overlay approaches may have their respective advantages under diverse propagation environment and system parameters. If the cognitive network can dynamically choose the DSA strategy under different environment, its performance could be further improved. We propose a selection criterion to determine whether to use underlay or overlay scheme under the given QoS constraint and the PUs’ spectrum-occupancy probability. Thus, the throughput of the CRN could be increased. Performance analysis and numerical evaluations are provided to demonstrate the effective capacity of CRN based on the underlay and the overlay schemes, taking into consideration the impact of delay QoS requirement and other related parameters.

Keywords


Underlay, Overlay, Effective capacity, QoS requirement, Selection criterion.

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References


C. Cordeiro, K. Challapali, D. Birru, and N. Shankar, “Ieee 802.22: The first worldwide wireless standard based on cognitive radio,” First IEEE Int. Symp. New Frontiers in Dynamic Spectrum Access Networks, pp. 328–337, Nov 2005.

D. Qiao, M. Gursoy, and S. Velipasalar, “Energy efficiency of fixed-rate wireless transmissions under queueing constraints and channel uncertainty,”

In Proceedings of IEEE GLOBECOM, pp. 24–29, Nov 2009.

F. Hou and J. Huang, “Dynamic channel selection in cognitive radio network with channel heterogeneity,” In Proceedings of IEEE GLOBECOM,

Nov 2009.

M. Khoshkholgh, K. Navaie, and H. Yanikomeroglu, “Access strategies for spectrum sharing fading environment: overlay, underlay, and mixed,”

IEEE JOURNAL on MOBILE Computing, vol. 9, no. 12, pp. 1780–1793, Dec 2010.

X. Kang, Y. Liang, A. Nallanathan, H. Garg, and R. Zhang, “Optimal power allocation for fading channels in cognitive radio networks: ergodic

capacity and outage capacity,” IEEE TR. on Comm., vol. 8, no. 2, pp. 940–950, Feb 2009.

X. Gong, S. Vorobyov, and C. Tellambura, “Optimal bandwidth and power allocation for sum ergodic capacity under fading channels in cognitive

radio networks,” IEEE TR. on Signal Process., vol. 59, no. 4, pp. 1814–1826, Apr 2011.

X. Kang, Y. Liang, H. Garg, and L. Zhang, “Sensing-based spectrum sharing in cognitive radio networks,” IEEE TR. on Veh. Technol., vol. 58, no.

, pp. 4649–4654, Oct 2009.

S. Stotas and A. Nallanathan, “Optimal sensing time and power allocation in multiband cognitive radio networks,” IEEE TR. on Comm., vol. 59, no.

, pp. 226–235, Jan 2011.

D. Zhang, Z. Tian, and G. Wei, “Spatial capacity of narrowband versus ultra-wideband cognitive radio systems,” IEEE TR. on Wireless Comm, vol.

, no. 11, pp. 4670–4680, Nov 2008.

R. Buehrer R. Menon and J.Reed, “On the impact of dynamic spectrum sharing techniques on legacy radio systems,” IEEE TR. on Wireless Comm,

vol. 7, no. 11, pp. 4198–4207, Nov 2008.

D. Wu and R. Negi, “Effective capacity: A wireless link model for support of quality of service,” IEEE TR. Wireless Comm., vol. 2, no. 4, pp.

–643, Jul 2003.

L. Musavian and S. Aissa, “Effective capacity of delay-constrained cognitive radio in nakagami fading channels,” IEEE TR. on Wireless Comm, vol.

, no. 3, pp. 1054–1062, Mar 2010.

S. Akin and M. Gursoy, “Effective capacity analysis of cognitive radio channels for quality of service provisioning,” IEEE TR. on Wireless Comm, vol. 9, no. 11, pp. 3354–3363, Nov 2010.

F. Ramos, V. Kontorovitch, and M. Lara, “High order fading distributions in nakagami wireless channels,” in Proc. IEEE Vehicular Technology Conf., pp. 564–568, Apr. 2007.

S. Al-Ahmadi and H. Yanikomeroglu, “On the approximation of the generalized distribution by a gamma distribution for modeling composite fading channels,” IEEE TR on Wireless Comm.,, vol. 9, no. 2, pp. 706–713, 2010.

Q. Zhao, S. Geirhofer, L. Tong, and B. Sadler, “Opportunistic spectrum access via periodic channel sensing,” IEEE TR. on Signal process., vol. 56, no. 2, pp. 785–796, Feb 2008.

T. Cover and J. Thomas, Elements of Information Theory, John Wiley & Sons, 2006.

A. Goldsmith and S. Chua, “Variable-rate variable-power MQAM for fading channels,” IEEE TR. on Inform.Theory., vol. 43, no. 6, pp. 1986–1992, Nov 1997.

J. Tang and X. Zhang, “Quality-of-service driven power and rate adaptation over wireless links,” IEEE TR. on Comm, vol. 6, no. 8, pp. 3058–3068, Aug 2007.

G. Kesidis, J. Walrand, and C. Chang, “Effective bandwidths for multiclass markov fluids and other ATM sources,” IEEE/ACM TR. Networking, vol. 1, no. 4, pp. 424–428, Aug 1993.

C. Chang, “Stability, queue length, and delay of deterministic and stochastic queueing neyworks,” IEEE TR. on Automatic Control., vol. 39, no. 5, pp. 913–931, May 1994.

J. Tang, , and X. Zhang, “Cross-layer-model based adaptive resource allocation for statistical QoS guarantees in mobile wireless networks,” IEEE TR. on Wireless Comm, vol. 7, no. 6, pp. 2318–2328, Jun 2008.

L. Liu and J. Chamberland, “On the effective capacities of multiple-antenna gaussian channels,” Proceedings of IEEE international symposium on information theory (ISIT), pp. 2583–2587, Jul 2008.

F. Olver, D. Lozier, R. Boisvert, and C. Clark, NIST Handbook of Mathematical Functions, Cambridge University Press, 2010.

R. Kwan and C. Leung, “Gamma variate ratio distribution with application to CDMA performance analysis,” IEEE-Sarnoff Symposium on Advances in Wired and Wireless Communication, pp. 188–191, Apr. 2005.

D. Bertsekas, Convex Analysis and Optimization, Athena Scientific, Belmont, MA, 2003.

W. Zhang, R. Mallik, and K. Letaief, “Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks,” IEEE TR Wireless Comm., vol. 8, no. 12, pp. 5761–5766, Dec 2009.

H. Poor, An Introduction to Signal Detection and Estimation, Springer, New York, pp 84-88, 2nd edition, 1994.

R. Yates and D. Goodman, Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, John Wiley &

Sons, pp 257-264, 2nd edition, 2005.

Y. Liang, Y. Zeng, E. Peh, and A. Hong, “Sensing-throughput tradeoff for cognitive radio networks,” IEEE TR Wireless Comm., vol. 7, no. 4, pp. 1326–1337, Apr 2008.

S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, UK, 7th edition, 2009, Electronice version.

S. Senthuran, A. Anpalagan, and O. Das, “Throughput analysis of opportunistic access strategies in hybrid underlay/overlay cognitive radio networks,”

IEEE TR. on wireless Comm, vol. 11, no. 6, pp. 2024–2035, Jun 2012.

C. Chang, Performance Guarantees in Commucation Networks, Springer, London, 2000.

J. Gentle, Matrix Algebra: Theory, Computations, and Applications in Statistics, Springer, New York, 2010.


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