October  2012, 8(4): 821-840. doi: 10.3934/jimo.2012.8.821

Optimal design for dynamic spectrum access in cognitive radio networks under Rayleigh fading

1. 

Department of Mathematical Sciences and Telecommunication Engineering Program, Korea Advanced Institute of Science and Technology, Daejeon, South Korea, South Korea

Received  September 2011 Revised  July 2012 Published  September 2012

We consider a time slotted cognitive radio network under Rayleigh fading where multiple secondary users (SUs) contend for spectrum usage over available primary users' channels. We analyze the performance of a channel access policy where each SU stochastically determines whether to access a wireless channel or not based on a given access probability. In the analysis, we focus on the queueing performance of an arbitrary SU with the channel access policy. To improve the queueing performance of SUs, the access probability in our channel access policy is adapted to the knowledge on the wireless channel information, e.g., the number of available channels and the nonfading probability of channels. It is then important to obtain the optimal access probabilities from the queueing performance perspective.
    In this paper we consider three scenarios. In the first scenario, all SUs have full information on wireless channel status and fading channel conditions. In the second scenario, all SUs have the information on wireless channel status but do not know their fading channel conditions, and in the last scenario all SUs do not have any information on wireless channel status and conditions. For each scenario we analyze the queueing performance of an arbitrary SU and show how to obtain the optimal access probabilities with the help of the effective bandwidth theory. From our analysis we provide an insight on how to design an optimal channel access policy in each scenario. We also show how the optimal channel access policies in three scenarios are related with each other. Numerical results are provided to validate our analysis. In addition, we investigate the performance behaviors of the optimal channel access policies.
Citation: Hyeon Je Cho, Ganguk Hwang. Optimal design for dynamic spectrum access in cognitive radio networks under Rayleigh fading. Journal of Industrial & Management Optimization, 2012, 8 (4) : 821-840. doi: 10.3934/jimo.2012.8.821
References:
[1]

S. Akin and M. C. Gursoy, Effective capacity analysis of cognitive radio channels for quality of service provisioning,, IEEE Transactions on Wireless Communications, 9 (2010), 3354.  doi: 10.1109/TWC.2010.092410.090751.  Google Scholar

[2]

C.-S. Chang, "Performance Guarantees in Communication Networks,", Springer-Verlag, (2000).  doi: 10.1007/978-1-4471-0459-9.  Google Scholar

[3]

C.-S. Chang and J. A. Thomas, Effective bandwidths in high-speeddigital networks,, IEEE J. Selected Areas in Communications, 3 (1995), 1091.  doi: 10.1109/49.400664.  Google Scholar

[4]

C. Cormio and K. R. Chowdhury, A survey on MAC protocols for cogntive radio networks,, Ad Hoc Networks, 7 (2009), 1315.  doi: 10.1016/j.adhoc.2009.01.002.  Google Scholar

[5]

L. Ding, T. Melodia, S. N. Batalama, J. D. Matyjas and J. Medley, Cross-layer routing and dynamic spectrum allocation in cognitive radio Ad Hoc networks,, IEEE Transactions on Vehicular Technology, 59 (2010), 1969.  doi: 10.1109/TVT.2010.2045403.  Google Scholar

[6]

, "Spectrum Policy Task Force,", Federal Comunications Commission, (2002), 02.   Google Scholar

[7]

A. T. Hoang, Y.-C. Liang and M. Habibulm, Power control and channel allocation in cognitive radio networks with primary users' cooperation,, IEEE Transactions on Mobile Computing, 9 (2010), 348.  doi: 10.1109/TMC.2009.136.  Google Scholar

[8]

G. U. Hwang and S. Roy, "Design and Analysis of Optimal Random Access Policies in Cognitive Radio Networks,", IEEE Transactions on communications, (2012), 121.   Google Scholar

[9]

F. Ishizaki and G. U. Hwang, Cross-layer design and analysis for wireless networks using the effective bandwidth function,, IEEE Transactions on Wireless Communications, 6 (2007), 3214.  doi: 10.1109/TWC.2007.06030108.  Google Scholar

[10]

B. L. Mark and G. Ramamurthy, Real-time estimation and dynamic renegotiation of UPC parameters for arbitrary traffic sources in ATM networks,, IEEE/ACM Trans. on Networking, 6 (1998), 811.  doi: 10.1109/90.748091.  Google Scholar

[11]

M. McHenry, "Spectrum White Space Measurements,", New America Foundation BroadBand Forum, (2003).   Google Scholar

[12]

H. Minc, "Nonnegative Matrices,", John Wiley & Sons, (1988).   Google Scholar

[13]

L. Musavian and S. Aissa, Effective capacity of delay-constrained cognitive radio in nakagami fading channels,, IEEE Transactions on Wireless Communications, 9 (2010), 1054.  doi: 10.1109/TWC.2010.03.081253.  Google Scholar

[14]

M. M. Rashid, Md. J. Hossain, E. Hossain and V. K. Bhargava, Opportunistic spectrum scheduling for multiuser cognitiver radio: A queueing analysis,, IEEE Transaction on Wireless Communications, 8 (2009), 5259.  doi: 10.1109/TWC.2009.081536.  Google Scholar

[15]

J. Shen, T. Jiang, S. Liu and Z. Zhang, Maximum channel through put via cooperative spectrum sensing in cognitive radio networks,, IEEE Transactions on wireless communications, 8 (2009), 5166.  doi: 10.1109/TWC.2009.081110.  Google Scholar

[16]

O. Simeone, Y. Bar-Ness and U. Spagnolini, Cooperation and cognitive radio,, in Proc. Int. Conf. Commun., (2007), 6511.   Google Scholar

[17]

S. Stotas and A. Nallanathan, "On the Throughput Maximization ofSpectrum Sharing Cognitive Radio Networks,", in Proc. IEEE GLOBECOM'10., ().   Google Scholar

[18]

S. Wang, J. Zhang and L. Tong, "Delay Analysis for CognitiveRadio Networks with Random Access: A Fluid Queue View,", IEEE INFOCOM 2010, (2010).   Google Scholar

[19]

D. Wu and R. Negi, Effective capacity: a wireless link model for support of quality of service,, IEEE Trans. on Wireless Communications, 2 (2003), 630.   Google Scholar

[20]

X. Zhang and Q. Du, Cross-layer modeling for QoS driven multimedia multicast/broadcast over fading channels in mobile wireless networks,, IEEE Transactions on Wireless Communications, 45 (2007), 62.   Google Scholar

show all references

References:
[1]

S. Akin and M. C. Gursoy, Effective capacity analysis of cognitive radio channels for quality of service provisioning,, IEEE Transactions on Wireless Communications, 9 (2010), 3354.  doi: 10.1109/TWC.2010.092410.090751.  Google Scholar

[2]

C.-S. Chang, "Performance Guarantees in Communication Networks,", Springer-Verlag, (2000).  doi: 10.1007/978-1-4471-0459-9.  Google Scholar

[3]

C.-S. Chang and J. A. Thomas, Effective bandwidths in high-speeddigital networks,, IEEE J. Selected Areas in Communications, 3 (1995), 1091.  doi: 10.1109/49.400664.  Google Scholar

[4]

C. Cormio and K. R. Chowdhury, A survey on MAC protocols for cogntive radio networks,, Ad Hoc Networks, 7 (2009), 1315.  doi: 10.1016/j.adhoc.2009.01.002.  Google Scholar

[5]

L. Ding, T. Melodia, S. N. Batalama, J. D. Matyjas and J. Medley, Cross-layer routing and dynamic spectrum allocation in cognitive radio Ad Hoc networks,, IEEE Transactions on Vehicular Technology, 59 (2010), 1969.  doi: 10.1109/TVT.2010.2045403.  Google Scholar

[6]

, "Spectrum Policy Task Force,", Federal Comunications Commission, (2002), 02.   Google Scholar

[7]

A. T. Hoang, Y.-C. Liang and M. Habibulm, Power control and channel allocation in cognitive radio networks with primary users' cooperation,, IEEE Transactions on Mobile Computing, 9 (2010), 348.  doi: 10.1109/TMC.2009.136.  Google Scholar

[8]

G. U. Hwang and S. Roy, "Design and Analysis of Optimal Random Access Policies in Cognitive Radio Networks,", IEEE Transactions on communications, (2012), 121.   Google Scholar

[9]

F. Ishizaki and G. U. Hwang, Cross-layer design and analysis for wireless networks using the effective bandwidth function,, IEEE Transactions on Wireless Communications, 6 (2007), 3214.  doi: 10.1109/TWC.2007.06030108.  Google Scholar

[10]

B. L. Mark and G. Ramamurthy, Real-time estimation and dynamic renegotiation of UPC parameters for arbitrary traffic sources in ATM networks,, IEEE/ACM Trans. on Networking, 6 (1998), 811.  doi: 10.1109/90.748091.  Google Scholar

[11]

M. McHenry, "Spectrum White Space Measurements,", New America Foundation BroadBand Forum, (2003).   Google Scholar

[12]

H. Minc, "Nonnegative Matrices,", John Wiley & Sons, (1988).   Google Scholar

[13]

L. Musavian and S. Aissa, Effective capacity of delay-constrained cognitive radio in nakagami fading channels,, IEEE Transactions on Wireless Communications, 9 (2010), 1054.  doi: 10.1109/TWC.2010.03.081253.  Google Scholar

[14]

M. M. Rashid, Md. J. Hossain, E. Hossain and V. K. Bhargava, Opportunistic spectrum scheduling for multiuser cognitiver radio: A queueing analysis,, IEEE Transaction on Wireless Communications, 8 (2009), 5259.  doi: 10.1109/TWC.2009.081536.  Google Scholar

[15]

J. Shen, T. Jiang, S. Liu and Z. Zhang, Maximum channel through put via cooperative spectrum sensing in cognitive radio networks,, IEEE Transactions on wireless communications, 8 (2009), 5166.  doi: 10.1109/TWC.2009.081110.  Google Scholar

[16]

O. Simeone, Y. Bar-Ness and U. Spagnolini, Cooperation and cognitive radio,, in Proc. Int. Conf. Commun., (2007), 6511.   Google Scholar

[17]

S. Stotas and A. Nallanathan, "On the Throughput Maximization ofSpectrum Sharing Cognitive Radio Networks,", in Proc. IEEE GLOBECOM'10., ().   Google Scholar

[18]

S. Wang, J. Zhang and L. Tong, "Delay Analysis for CognitiveRadio Networks with Random Access: A Fluid Queue View,", IEEE INFOCOM 2010, (2010).   Google Scholar

[19]

D. Wu and R. Negi, Effective capacity: a wireless link model for support of quality of service,, IEEE Trans. on Wireless Communications, 2 (2003), 630.   Google Scholar

[20]

X. Zhang and Q. Du, Cross-layer modeling for QoS driven multimedia multicast/broadcast over fading channels in mobile wireless networks,, IEEE Transactions on Wireless Communications, 45 (2007), 62.   Google Scholar

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