Cross-layer modeling and optimization of multi-channel cognitive radio networks under imperfect channel sensing

Jae Deok Kim; Ganguk Hwang

doi:10.3934/jimo.2015.11.807

Article Contents

2015, Volume 11, Issue 3: 807-828. Doi: 10.3934/jimo.2015.11.807

This issue Previous Article On a discrete-time GI$^X$/Geo/1/N-G queue with randomized working vacations and at most $J$ vacations Next Article Performance analysis of buffers with train arrivals and correlated output interruptions

Cross-layer modeling and optimization of multi-channel cognitive radio networks under imperfect channel sensing

Jae Deok Kim^1, and
Ganguk Hwang^2,

1.
Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon
2.
Department of Mathematical Sciences and Telecommunication Engineering Program, Korea Advanced Institute of Science and Technology, Daejeon

Received: August 31, 2013

Revised: April 30, 2014

Published: June 2015

Abstract Related Papers Cited by

Abstract

In this paper, we consider a multi-channel cognitive radio network with multiple secondary users (SUs) and analyze the performance of users in the network. We assume primary users (PUs) adopt the automatic repeat request (ARQ) protocol at the medium access control layer. We have two main goals. Our first goal is to develop a cross-layer performance model of the cognitive radio network by considering the retransmission characteristics of the ARQ protocol and the interference between PUs and SUs due to imperfect channel sensing. Using the cross-layer performance model we analyze the throughput performance of SUs and the delay performance of PUs.
Our second goal is to propose an optimal channel sensing method that maximizes the throughput performance of SUs while a given delay requirement of PUs is guaranteed. To this end, using our cross-layer performance model, we formulate an optimization problem and solve it to get an optimal channel sensing method that satisfies the design objectives. Numerical and simulation results are provided to validate our analysis and to investigate the performance of the optimal channel sensing method.

Keywords:

Mathematics Subject Classification: Primary: 68M10, 68M20.

Citation:

Related Papers

Cited by

References

[1]	S. Akin and M. C. Gursoy, Effective Capacity Analysis of Cognitive Radio Channels for Quality of Service Provisioning, IEEE Trans. on Wireless Comm., 9 (2010), 3354-3364.doi: 10.1109/TWC.2010.092410.090751.
[2]	I. F. Akyildiz, B. F. Lo and R. Balakrishnan, Cooperative spectrum sensing in cognitive radio networks: A survey, Physical Communication, 4 (2011), 40-62.doi: 10.1016/j.phycom.2010.12.003.
[3]	C-S. Chang, Performance guarantees in communication networks, Springer, 2000.
[4]	C. Cormio and K. R. Chowdhury, A Survey on MAC Protocols for Cognitive Radio Networks, Ad Hoc Networks, 7 (2009), 1315-1329.doi: 10.1016/j.adhoc.2009.01.002.
[5]	F. F. Digham, M-S. Alouini and M. K. Simon, On the energy detection of unknown signals over fading channels, IEEE Tran. on Comm., 55 (2007), 21-24.doi: 10.1109/TCOMM.2006.887483.
[6]	A. A. El-Sherif and K. J. Ray Liu, Joint design of spectrum sensing and channel access in cognitive radio networks, IEEE Trans. Wireless Comm., 10 (2011), 1743-1753.doi: 10.1109/TWC.2011.032411.100131.
[7]	Federal Communications Commission, Spectrum Policy Task Force, Rep. ET Docket No. 02-135, 2002.
[8]	Federal Communications Commission, Notice of Proposed Rule Making and Order, Rep. ET Docket No. 02-222, 2003.
[9]	G. U. Hwang and S. Roy, Design and analysis of optimal random access policies in cognitive radio networks, IEEE Transactions on Comm., 60 (2012), 121-131.doi: 10.1109/TCOMM.2011.112311.100702.
[10]	S. C. Jha, M. M. Rashod and V. K. Bhargava, Medium access control in distributed cognitive radio networks, IEEE Wireless Comm. Mag., 18 (2011), 41-51.doi: 10.1109/MWC.2011.5999763.
[11]	S. M. Kay, Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, Prentice-Hall, 1998.
[12]	G. Latouche and V. Ramaswami, Introduction to Matrix Analytic Methods in Stochastic Models, SIAM, 1999.doi: 10.1137/1.9780898719734.
[13]	W-Y. Lee and I. F. Akyildiz, Optimal Spectrum Sensing Framework for Cognitive Radio Networks, IEEE Trans. on Wireless Comm., 7 (2008), 3845-3857.doi: 10.1109/T-WC.2008.070391.
[14]	X. Li, Q. Zhao, X. Guan and L. Tong, Optimal cognitive access of markovian channels under tight collision constraints, IEEE J. Selected Areas in Comm., 29 (2010), 1-5.doi: 10.1109/ICC.2010.5502055.
[15]	Y-C. Liang, Y. Zeng, E. C. Y. Peh and A. T. Hoang, Sensing-throughput tradeoff for cognitive radio networks, IEEE Transactions on Wireless Comm., 7 (2008), 5330-5335.doi: 10.1109/TWC.2008.060869.
[16]	S-Y. Lien, C-C. Tseng and K-C. Chen, Carrier sensing based multiple access protocols for cognitive radio networks, Proc. IEEE ICC, (2008), 3208-3214.doi: 10.1109/ICC.2008.604.
[17]	L. Ma, X. Han and C-C. Shen, Dynamic open spectrum sharing for wireless ad hoc networks, Proc. IEEE DySPAN, (2005), 203-213.doi: 10.1109/DYSPAN.2005.1542636.
[18]	J. Mitola and G. Q. Maguire, Cognitive radio: Making software radios more personal, IEEE Pers. Commun., 6 (1999), 13-18.doi: 10.1109/98.788210.
[19]	E. C. Y. Peh, Y-C. Liang, Y. L. Guan and Y. Zeng, Optimization of cooperative sensing in cognitive radio networks: A sensing-throughput tradeoff view, IEEE Trans. on Vech. Tech., 58 (2009), 5294-5299.doi: 10.1109/TVT.2009.2028030.
[20]	S. M. Ross, Stochastic Processes, John Willey & Sons, 1996.
[21]	A. Singh, M. R. Bhatnagar and R. K. Mallik, Threshold optimization of finite sample based cognitive radio network, NCC 2012, (2012), 1-5.doi: 10.1109/NCC.2012.6176816.
[22]	H. Su and X. Zhang, Cross-layer based opportunistic MAC protocols for QoS provisionings over cognitive radio wireless networks, Journal on Selected Areas in Comm., 26 (2008), 118-129.doi: 10.1109/JSAC.2008.080111.
[23]	S. Wang, J. Zhang and L. Tong, Delay analysis for cognitive radio networks with random access: A fluid flow view, Proc. 2010 IEEE INFOCOM, (2010), 1-9.doi: 10.1109/INFCOM.2010.5461943.
[24]	A. Wyglinski, M. Nekovee and Y. T. Hou, Cognitive Radio Communications and Networks: Principles and Practice, Elsevier, 2009.
[25]	M. Xu, and H. Li and X. Gan, Energy efficient sequential sensing for wideband multi-channel cognitive network, Proc. IEEE ICC, (2011), 1-5.doi: 10.1109/icc.2011.5962519.
[26]	T. Yücek and H. Arslan, A survey of spectrum sensing algorithms for cognitive radio applications, IEEE Comm. Surveys & Tutorials, 11 (2009), 116-130.doi: 10.1109/SURV.2009.090109.
[27]	Y. H. Zeng, Y.-C. Liang, A. T. Hoang and R. Zhang, A review on spectrum sensing for cognitive radio: Challenges and solutions, EURASIP J. Advances Signal Process, 2010 (2010), 381465.doi: 10.1155/2010/381465.
[28]	Q. Zhao, L. Tong, A. Swami and Y. Chen, Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework, IEEE Journal on Selected Areas in Comm., 25 (2007), 589-600.doi: 10.1109/JSAC.2007.070409.
[29]	Standard for Wireless Regional Area Networks (WRAN) - Specific requirements - Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and procedures for operation in the TV Bands, The Institute of Electrical and Electronics Engineering, Inc. Std. IEEE 802.22.