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A Prediction-Based Solution for Improving the Performance of CSMA/CA Networks Under the Hidden Collision Effect

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In Dynamic Spectrum Access (DSA) policy, a secondary user (SU) is allowed to access the primary user (PU) channel when it is idle. This access has to be disruption free for the PU. Literature on DSA focuses on PU and SU signal interferences as the main source of disruption. However the idle state may result from a non-transmitting activity such as the case where the primary user is in the Backoff Window in CSMA/CA networks. This particular idle state can be wrongly seen as an opportunity for SU to access the channel. However, accessing the channel in this context will cause Hidden Collision (HC) and will decrease the primary user performance. In this paper, we extend our previous work on HC and propose to distinguish the PU behaviour, modeled as a three states process, from the channel evolution modeled as an ON/OFF process. To combine these two interdependent processes we use the Hidden Markov Model (HMM) and we propose a solution to predict the PU state and therefore reduce the hidden collision effect. As a proof of concept, our approach accuracy is evaluated using a set of inputs obtained from simulation. We demonstrate the ability of the model to predict accurately enough the PU state and reduce hidden collision.
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CSMA/CA; Hidden Collision; Forbidden White Spaces; Cognitive Radio; Dynamic Spectrum Access; Hidden Markov Model; Baum-Welsh

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FCC, Spectrum Policy Task Force Report, No.02-155, Nov, 2002.

DARPA XG WG, The XG Vision RFC V1.0, 2003.

802.11 IEEE Standard for Information technology - Local and metropolitan area networks - 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.

M. Sebgui, J. Almhana, Z. Liu, S. Bah, B. El Graini “Modeling Hidden Collision in Dynamic Spectrum Access to the CSMA/CA Networks” Proc. IEEE International Conference on Communications (ICC), Jun. 2014, pp 4927 – 4932.

E. Ziouva, T. Antonakopoulos, "CSMA/CA Performance Under High Traffic Conditions : Throuput and Delay Analysis". Computer Communications, December 1998,s Vol 25, Issue 3, pp 313–321.

Velumani, R., Duraiswamy, K., Leadership endurance prudential mutual sharing multicast routing, (2013) International Review on Computers and Software (IRECOS), 8 (9), pp. 2229-2238.

M. Garetto, T. Salonidis, E. W. Knightly, « Modeling Per-flow Throughput and Capturing Starvation in CSMA Multi-hop Wireless Networks » IEEE/ACM Transaction on Networking, August 2008, Vol 16, Issue 4, pp 864-877.

J. Mitola and G. Q. Maguire, “Cognitive Radio: Making Software Radios More Personal,” IEEE Pers. Commun, Aug. 1999., Vol. 6, no. 4, pp. 13–18.

I. F. Akyildiz, W. Y. Lee, M. C. Vuran, S. Mohanty “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey” Computer Networks, Vol 50, 2006, pp 2127-2159.

Q. Zhao, B. M. Sadler “A Survey of Dynamic Spectrum Access” IEEE Signal Processing Magazine” Vol 24, Issue 3, May 2007, pp 79 – 89.

D. Cabric, S. M. Mishra, and R. W. Brodersen, “Implementation Issues in Spectrum Sensing for Cognitive Radios” Proc. Asilomar Conf. Signals, Systems, and Computers, Nov. 2004, pp. 772–76.

S. Haykin, D. J. Thomson, J. H. Reed, “Spectrum Sensing for Cognitive Radio” Proceedings of the IEEE, Vol. 97, Issue. 5, May 2009, pp. 849 – 877.

A. Ghasmi, E. S. Sousa “Spectrum Sensing in Cognitive Radio Networks: Requirements, Challenges and design Trade-offs” IEEE Communication Magazine, April 2008, Vol , Issue , pp 32-39.

A. Ghasemi, E. S. Sousa "Spectrum Sensing in Cognitive Radio Networks: The Cooperation-Processing Trade-Off", Wiley Wireless Commun. and Mobile Comp, May 2007, Vol. 7, no. 9, pp.1049 -1060.

A. Ghasemi, E.S. Sousa, “Fundamental limits of spectrum-sharing in fading environments”, IEEE Transactions on Wireless Communications, Vol. 6, Issue. 2, Feb 2007 pp 649 – 658.

Q. Zhao, S. Geirhofer, L. Tong, B. M. Sadler, “ Opportunistic Spectrum Access via Periodic Channel Sensing” IEEE Transaction on Signal Processing, Vol 56, n 2, pp 785-791.

Q. Zhao, L. Tong, A. Swami, Y. Chen “ Decentralized Cognitive MAC for Opportunistic Spectrum Acces in Ad Hoc Networks : A POMDP Framework” IEEE Journal on Selected Area in Communication, Vol 25, n 3, April 2007, pp 589-600.

F. Khozeimeh, S. Haykin, “Brain-Inspired Dynamic Spectrum Management for Cognitive Radio Ad Hoc Networks”, IEEE Transactions on Wireless Communications, Vol. 11, Issue. 10, Oct 2012, pp 3509 – 3517.

T. Clancy and B. Walker, “Predictive dynamic spectrum access,” Proc. SDR forum technical conference, 2006.

V. K. Tumuluru, P. Wang,*D. Niyato, “Channel status prediction for cognitive radio networks” Wireless Communications and Mobile Computing, July 2012, Vol 12, Issue 10, pp 862–874.

S. Pattanayak, P. Venkateswaran, R. Nandi, “Artificial Neural Networks for Cognitive Radio: A Preliminary Survey” Proc. of 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), Sept. 2012, pp 1 – 4.

Z. Wen et al., “Autoregressive Spectrum Hole Prediction Model for Cognitive Radio Systems,” IEEE ICC Wksps, May 2008, pp. 154–57.

I. A. Akbar and W. H. Tranter, “Dynamic Spectrum Allocation in Cognitive Radio Using Hidden Markov Models: Poisson Distributed Case,” Proc. IEEE Southeastcon, 2007, pp. 196–201.

C. Ghosh, C. Cordeiro, D.P. Agrawal, M.B. Rao, “Markov chain existence and Hidden Markov models in spectrum sensing”, Proc. IEEE International Conference on Pervasive Computing and Communications (PerCom), Mar. 2009, pp 1-6.

Z. Chen, R.C. Qiu, , “Prediction of channel state for cognitive radio using higher-order hidden Markov model” Proc. IEEE Southeastcon, Mar. 2010, pp 18-21.

H. Ahmadi, I. Macaluso, L.A. DaSilva, “Predictive opportunistic spectrum access using learning based hidden Markov models” IEEE 22nd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC),Sept. 2011, pp 401-405.

H. Ahmadi, I. Macaluso, L.A. DaSilva, “The effect of the spectrum opportunities diversity on opportunistic access” Proc. IEEE International Conference on Communications (ICC), Jun. 2013, pp 2829-2834.

Prakasam, S., Wahi, A., Duraisamy, R., A comparative analysis on markov model (MM) association rule mining (ARM), Association rule mining-statistical features (ARM-SF), Boosting and bagging model (BBM) to impervious web page prediction, (2014) International Review on Computers and Software (IRECOS), 9 (7), pp. 1163-1168.

A.J. Viterbi, “Error bounds for convolutional codes and an asymptotically optimum decoding algorithm”, IEEE Transactions on Information Theory, Vol.13, Issue. 2, April 1967, pp. 260-269.

C.M. Bishop, “Pattern Recognition and Machine Learning", 2006 Springer Science Business Media, LLC; 2006.

Orumwense, E., Oyerinde, O., Mneney, S., Impact of primary user emulation attacks on cognitive radio networks, (2014) International Journal on Communications Antenna and Propagation (IRECAP), 4 (1), pp. 19-26.


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