Clustering in Cognitive Radio Networks: A Review
DOI:
https://doi.org/10.26438/ijcse/v5i8.206210Keywords:
Cognitive radio, Cooperative spectrum sensing (CSS), Clustering, Cluster head, Fusion centre, Probability of detection, Probability of false alarmAbstract
To overcome the drawback of underutilisation of the spectrum in wireless communication field, the Cognitive Radio (CR) technology came into existence, which permits the unlicensed or the Secondary Users (SU) to opportunistically use the available licensed spectrum when the licensed or the Primary User (PU) is not in use. The unlicensed user should not disrupt the working of the licensed user. To reduce the problem of shadowing and fading in CR, cooperative sensing was introduced in which many Cognitive Radio Users (CR Users) collectively report their decisions or data to the Fusion Center (FC) and it makes the final decision regarding the absence or presence of PU. In cooperative sensing, larger overhead is observed. Hence, clustering is one of the methods which reduces overhead. Clustering is a topology management system, in which the nodes are organized into logical groups known as clusters. It not only boosts the performance of the network but also achieves network scalability and stability, supports cooperative tasks, reduces the bandwidth requirement. This paper reviews the numerous clustering schemes, analyzes their characteristics and studies their performances.
References
D. Cabric, S. Mishra, R. Brodersen, “Implementation issues in spectrum sensing for cognitive radios”, 38th Asilomar Conf. on Signals, Systems and Computers, USA, 2004.
S Maleki, G Leus, “Censored truncated sequential spectrum sensing for cognitive radio networks”, IEEE Journal on Selected Areas in Communications, Vol 31, Issue 3, pp. 364-378, 2013.
N Reisi, M Ahmadian, V Jamali, S Salari, “Cluster-based cooperative spectrum sensing over correlated log-normal channels with noise uncertainty in cognitive radio networks”, IET Communication, Vol 6, Issue 16, pp. 2725-2733, 2012.
S. Althunibat, M. D. Renzo, F. Granelli, “Optimizing the K-out-of-N rule for cooperative sensing in cognitive radio networks”, IEEE Global Communication Conference, USA, 2013.
E.C.Y. Peh, Y.-C. Liang, Y.L. Guan, “Optimization of cooperative sensing in cognitive radio networks: a sensing-throughput trade-off view”, IEEE Transaction on Vehicular Technology, Vol. 58, Issue 9, pp. 5294-5299, 2009.
W. Zhang, R. Mallik, K. Letaief, “Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks”, IEEE Transactions on Wireless Communications, Vol. 8, Issue 12, pp. 2009.
Ian F. Akyildiz, B. F. Lo, R. Balakrishnan, “Cooperative spectrum sensing in cognitive radio networks: A survey”, Vol. 4, Issue 1, pp. 40-62, Elsevier, 2010.
K. A. Yau, N. Ramli, W. Hashim, H. Mohamad, “Clustering algorithms for cognitive radio networks: A survey”, Journal of network and applications, Elsevier, Vol 45, pp. 79-95, 2014.
Digham, F.F. Alouini, M.-S., Simon, M.K, “On the energy detection of unknown signals over fading channels”, IEEE Trans. Commun., Vol. 55, Issue 1, pp. 21-24, 2007.
N. Nguyen-Thanh, I. Koo, “A cluster-based selective cooperative spectrum sensing scheme in cognitive radio”, EURASIP Journal on wireless Communications and Networking, 2013.
Q Duong, VNQ Bao, H Tran, GC Alexandropoulos, HJ Zepernick, “Effect of primary networks on the performance of spectrum sharing AF relaying”, IET Lett, Vol.48, Issue 1, pp. 25–27, 2012.
N Nguyen-Thanh, I Koo, “Log-likelihood ratio optimal quantizer for cooperative spectrum sensing in cognitive radio”, IEEE Commun. Lett., Vol.15, Issue 3, pp. 317–319, 2011.
B Vo Nguyen Quoc, TQ Duong, D Benevides da Costa, GC Alexandropoulos, A Nallanathan, “Cognitive amplify-and-forward relaying with best relay selection in non-identical Rayleigh fading”, IEEE COML, Vol. 17, Issue 3, pp. 475–478, 2013.
Z Chair, PK Varshney, “Optimal data fusion in multiple sensor detection systems”, IEEE Trans. Aerospace and Electronic Syst. AES, Vol. 22, Issue 1, pp. 98–101, 1986.
Y. Jiao, P. Yin, I. Joe, “Clustering scheme for cooperative spectrum sensing in cognitive radio networks”, IET Journals, Vol. 10, Issue 13, pp. 1590-1595, 2016.
Chen, G. Wang, W. Peng, T, “Agility improvements by censor-based cooperative spectrum sensing in cognitive radio networks”, Proc. Int. Conf. on Communication and Networking in China, August 2008.
Li. H, Cheng, X., Li, K., et al., “Robust collaborative spectrum sensing schemes for cognitive radio networks”, IEEE Trans. Parallel Distrib. Syst., Vol 25, Issue 8, pp. 2190-2200, 2014.
Frey, B., Dueck, D., “Clustering by passing messages between data points”, Science, Vol 315, Issue 5814, pp. 972-976, 2007.
W. Kim, H. Jeon, SooyeolIm, H. Lee, “Optimization of Multi-Cluster Multi-Group based cooperative sensing in cognitive radio networks”, IEEE Military communications conference, USA, 2010.
O. Younis, and S. Fahmy, “Distributed Clustering in Ad hoc Sensor Networks: A Hybrid Energy-Efficient Approach,”, Proc. of IEEE Annual Joint Conference of the Computer and Communications Societies (INFOCOM), China, 2004.
F. A. Awin, E. Abdel-Raheem, M. Ahmadi, “Optimization of multi-level hierarchical cluster-based spectrum sensing structure in cognitive radio networks”, Vol 36, pp. 15-25, Elsevier, 2014.
A.C. Malady, C.R.C.M. da Silva,”Clustering methods for distributed spectrum sensing in cognitive radio systems”, Mil. Commun. Conf. MILCOM, USA, 2008.
Y. Wang, G. Nie, G. Li, C. Shi,“Sensing-throughput tradeoff in cluster-based co-operative cognitive radio networks with a TDMA reporting frame structure”, Wirel. Pers. Commun., Vol 71, Issue 3, pp. 1795-1818, 2012.
D. Oh, Y. Lee, “Energy detection based spectrum sensing for sensing error minimization in cognitive radio networks”, J. Commun. Netw. Inf. Secur., Vol. 1, Issue 1, pp. 1-5, 2009.
D.Calvetti, G.H. Golub, W.B. Gragg, L. Reichel,”Computation of Gauss–Kronrod quadrature rules”, Math. Comput., 2000.
G. Ganesan, “Cooperative spectrum sensing in cognitive radio networks”, First IEEE Int. Symp. New Front. Dyn. Spectr. Access Networks, USA, 2005.
Balaji V, T. Nagendra, C. Hota, G Raghurama, “Cooperative spectrum sensing in cognitive radio: An Archetypal Clustering Approach”, IEEE WISPNET conference, 2016.
A. Cutler, L. Breiman, “Archetypal analysis,” Technometrics, 1994.
Balaji V, X. Fernando, “Approach for cluster-based spectrum sensing over band-limited reporting channels”, IET Communications, Vol 6, Issue 11, pp. 1466-1474, 2012.
Bao, L., Carcia-Luna-Aceves, J.J, “Topology management in ad hoc networks”. Proc. Int. Symp. on Mobile Ad Hoc Networking and Computing, 2003.
Amis, A., Prakash, R., Vuong, T., Huynh, D.T, “Max-Min D-cluster formation in wireless ad hoc networks”, Proc. IEEE Int. Conf. on Computer Communications, 2000.
Krishna, P., Vaidya, N., Chatterjee, M., Pradhan, D., “A cluster-based approach for routing in dynamic networks”, Comput. Commun. Rev., Vol 27, Issue 2, pp. 49-64, 1997.
Aki f Cem Heren, H. Birkan Yilmaz, Tuna Tugcu, “Energy efficient MAC protocol for cluster formation in mobile cooperative spectrum sensing”, IEEE Wireless Communications and Networking Conference, USA, 2015.
Ahmed S. B. Kozal, M. Merabti, F. Bouhafs, “Energy-Efficient Multi-hop Clustering Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks”, IEEE 11th Consumer Communications and Networking Conference, USA, 2014.
W. B. Heinzelman, et al., "An application-specific protocol architecture for wireless micro sensor networks", IEEE Transactions on Wireless Communications, Vol 1, Issue 4, pp. 660-670, 2002.
T. Rasheed, A. Rashdi, A. N. Akhtar, “A Cluster Based Cooperative Technique for Spectrum Sensing using Rely Factor”, 12th International Bhurban Conference on Applied Sciences and Technology, Pakistan, 2015.
Santi P. Maity, Subhankar Chatterjee, Tamaghna Acharya, “On optimal fuzzy c-means clustering for energy efficient cooperative spectrum sensing in cognitive radio networks”, Elsevier journal on DSP, Vol 49, Issue 1, pp. 104-115, 2015.
J.C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms”, Plenum Press, New York, 1981.
S. Chatterjee, A. Banerjee, T. Acharya, S.P. Maity, “Fuzzy C-means clustering in energy detection for cooperative spectrum sensing in cognitive radio system”, 8th International Workshop on Multiple Access Communications, Switzerland, 2014.
S. Huang, H. Chen, Y. Zhang, F. Zhao, “Energy-efficient cooperative spectrum sensing with amplify-and-forward relaying”, IEEE Commun., Vol 16, Issue 4, pp. 450-454, 2012.
Anal Paul, Santi P. Maity, “Kernel fuzzy c-means clustering on energy detection based cooperative spectrum sensing”, Elsevier journal on digital communication and networks, Vol 2, Issue 4, pp. 196-205, 2016.
Sharma P, Abrol V, “Optimized cluster head selection & rotation for cooperative spectrum sensing in cognitive radio networks”, Proceedings of 10th inter- national conference on wireless and optical communications networks (WOCN’13), Bhopal, India, 2013.
Kim M-R, “Distributed coordination protocol for common control channel selection in multichannel ad-hoc cognitive radio networks”, Proceedings of the IEEE conference on wireless and mobile computing, networking and communications (WIMOB’09), Marrakech, Morocco, 2009.
Guo C, Peng T, Xu S, Wang H, Wang W, “Cooperative spectrum sensing with cluster- based architecture in cognitive radio networks”, Proceedings of the IEEE 69th vehicular technology conference (VTC Spring’09), Barcelona, Spain; 2009.
Huang X-L, Wang G, Hu F, Kumar S., “Stability–capacity–adaptive routing for high- mobility multi-hop cognitive radio networks”, IEEE Transaction on Vehicular Technology, Vol 60, Issue 6, pp. 2714-2729, 2011.
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