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Optimal Spectrum Sensor Assignment in Multi-channel Multi-user Cognitive Radio Networks

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Accurate detection of spectrum holes is the most important and critical task in any cognitive radio (CR) communication system. When a single spectrum sensor is assigned to detect a specific primary channel, then the detection may be unreliable because of noise, random multipath fading and shadowing. Also, even when the primary channel is invisible at the CR transmitter, it may be visible at the CR receiver (the hidden primary channel problem). With a single sensor per channel, a high and consistently uniform level of sensitivity is required for reliable detection. These problems are solved by deploying multiple heterogeneous sensors at distributed locations. The proposed spectrum hole detection method uses cooperative sensing, where the challenge is to properly assign sensors to different primary channels in order to achieve the best reliability, a minimum error rate and high efficiency. Existing methods use particle swarm optimization, the ant colony system, the binary firefly algorithm, genetic algorithms and non-linear mixed integer programming. These methods are complex and require substantial pre-processing. The aim of this paper is to provide a simpler solution by using simpler binary integer programming for optimal assignment. Optimal assignment minimizes the probability of interference which is a non-linear function of decision variables. We present an approach used to linearize the objective function. Since multiple spectrum sensors are used, the optimal constrained assignment minimizes the maximum of interferences. While performing the optimization, the proposed method also takes care of the topological layout concerned with channel accessibility. The proposed algorithm is easily scalable and flexible enough to adapt to different practical scenarios.
Rocznik
Tom
Strony
88--96
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
  • Jain University, Bangalore, India
  • School of Engineering and Technology, Jain University, Bangalore, India
Bibliografia
  • [1] I. F. Akyildiz, B. F. Lo, and R. Balakrishnan, “Cooperative spectrum sensing in cognitive radio networks: a survey”, Phys. Commun., vol. 4, no. 1, pp 40–62, 2011 (doi: 101016/j.phycom.2010.12.003).
  • [2] I. F. Akyildiz, W. Y. Lee, M. Vuran, and S. Mohanty, “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey”, Comput. Netw., vol. 50, no. 13, pp. 2127–2159, 2006 (doi: 10.1016/j.comnet.2006.05.001).
  • [3] B. Wang and K. J. Ray Liu, “Advances in cognitive radio networks: a survey”, IEEE J. of Selec. Topics in Sig. Process., vol. 5, no. 1, pp. 5–23, 2011 (doi: 10.1109/JSTSP.2010.2093210).
  • [4] Y.-C. Liang et al., “Guest editorial – cognitive radio: theory and application”, IEEE J. on Selec. Areas in Commun., vol. 26, no. 1, pp. 1–4, 2008 (doi: 10.1109/JSAC.2008.080101).
  • [5] R. Tandra, S. M. Mishra, and A. Sahai, “What is a spectrum hole and what does it take to recognize one?”, Proc. of the IEEE, vol. 97, no. 5, pp. 824–848, 2009 (doi: 10.1109/JPROC.2009.2015710).
  • [6] Y. Chen, “Improved energy detector for random signals in Gaussian noise”, IEEE Trans. on Wirel. Commun., vol. 9, no. 2, pp. 558–563, 2010 (doi: 10.1109/TWC.2010.02.090622).
  • [7] H. M. Abdelsalam and A. Al-shaar, “An enhanced binary particle swarm optimization algorithm for channel assignment in cognitive radio networks”, in Proc. 5th Int. Conf. on Modell., Identif. and Control ICMIC 2013, Cairo, Egypt, 2013, pp. 221–226.
  • [8] Q. Liu, W. Lu, and W. Xu, “Spectrum allocation optimization for Cognitive Radio networks using Binary Firefly Algorithm”, in Proc. of the 2014 Int. Conf. on Innov. Design and Manufact. ICIDM 2014, Montreal, QC, Canada, 2014, pp. 257–262, 2014 (doi: 10.1109/IDAM.2014.6912704).
  • [9] F. Koroupi, S. Talebi, and H. Salehinejad, “Cognitive radio networks spectrum allocation: an ACS perspective”, Scientia Iranica, vol. 19, no. 3, pp. 767–773, 2012 (doi: 10.1016/j.scient.2011.04.029).
  • [10] J. Elhachmi and Z. Guennoun, “Cognitive radio spectrum allocation using genetic algorithm”, EURASIP J. on Wirel. Commun. and Netw., pp. 133–143, 2016 (doi: 10.1186/s13638-016-0620-6).
  • [11] S. Chatterjee, S. Dutta, P. P. Bhattacharya, and J. S. Roy, “Optimization of spectrum sensing parameters in cognitive radio using adaptive genetic algorithm”, J. of Telecommun. and Inform. Technol., no. 1, pp. 21–27, 2017.
  • [12] R. M. Eletreby, H. M. Elsayed, and M. M. Khairy, “Optimal spectrum assignment for cognitive radio sensor networks under coverage constraint”, IET Communications, vol. 8, no. 18, pp. 3318–3325, 2014 (doi: 10.1049/iet-com.2014.0423).
  • [13] A. S. Alfa, B. T. Maharaj, S. Lall, and S. Pal, “Mixed-integer programming based techniques for resource allocation in underlay cognitive radio networks: a survey”, J. of Commun. and Netw., vol. 18, no. 5, pp. 744–761, 2016 (doi: 10.1109/JCN.2016.000104).
  • [14] T. S. Rappaport, Wireless Communications: Principles and Practice, 2nd ed. Upper Saddle River, NJ: Prentice Hall PTR, 2002 (ISBN 978-0130422323).
  • [15] F. K. Jondral, “Software-defined radio-basic and evolution to cognitive radio”, EURASIP J. on Wirel. Commun. and Netw., vol. 3, pp. 275–283, 2005 (doi: 10.1155/WCN.2005.275).
  • [16] R. Urgaonkar and M. J. Neely, “Opportunistic scheduling with reliability guarantees in cognitive radio networks”, IEEE Trans. Mob. Comput., vol. 8, no. 6, pp. 766–777, 2009 (doi: 10.1109/TMC.2009.38).
  • [17] Y. Liang, L. Lai, and J. Halloran, “Distributed cognitive radio network management via algorithms in probabilistic graphical models”, IEEE J. on Selec. Areas in Commun., vol. 29, no. 2, pp. 338–348, 2011 (doi: 10.1109/JSAC.2011.110207).
  • [18] L. Y. Yang, M. H. Nie, Z. W. Wu, and Y. Y. Nie, “Modeling and solution for assignment problem”, Int. J. of Mathem. Models and Methods in Appl. Sci., vol. 2, no. 2, pp. 205–212, 2008.
  • [19] R. E. Burkard and E. Çela, “Linear assignment problems and extensions”, in Handbook of Combinatorial Optimization – Supplement Volume A, and D. Z. Du and P. M. Pardalos, Eds. Boston, MA, USA: Springer, 1999, pp. 75–149 (doi: 10.1007/978-1-4757-3023-4).
  • [20] R. S. Garfinkel, “An improved algorithm for the bottleneck assignment problem”, Operations Res., vol. 19, no. 7, pp. 1747– 1751, 1971 (doi: 10.1287/opre.19.7.1747).
  • [21] J. E. Hopcroft and R. M. Karp, “An n5/2 algorithm for maximum matching in bipartite graphs”, Soc. for Indust. and Appl. Mathem. J. of Comput., vol. 2, no. 4, pp. 225–231, 1973 (doi: 10.1137/0202019).
  • [22] MOSEK Optimization Toolbox for MATLAB [Online]. Available: http://docs.mosek.com/8.0/toolbox/intlinprog.html
  • [23] W. Wang and X. Liu, “List-coloring based channel allocation for open spectrum wireless networks”, in IEEE 62nd Conf. on Veh. Technol. VTC-2005-Fall, Dallas, TX, USA, 2005 (doi: 10.1109/VETECF.2005.1558001).
  • [24] N. Shylashree, “Non-crossing rectilinear shortest minimum bend paths in the presence of rectilinear obstacles”, J. of Telecommun. and Inform. Technol., no. 3, 2018 (doi: 10.26636/jtit.2018.120417).
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d5a1c9a5-bd59-44b2-8143-d055446771b8
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