5G Heterogeneous Networks Future Assessment on Network Channel Allocation and Particle Swarm Optimization (PSO)

Authors

  • Dr. Srihari Chintha  Assoc Professor, CSE Department, AIET, Hyderabad, Telangana, India

Keywords:

Channel Allocation, Network Selection, 5G Heterogeneous Networks, Optimization

Abstract

The demand for spectrum resources has augmented dramatically with the appearance of recent wireless applications. Spectrum sharing, thought of as an essential mechanism for 5G networks, is visualized to deal with spectrum deficiency issue and accomplish high data rate access and secure the quality of service (QoS). From the licensed network's perspective, the interference caused by all secondary users (SUs) ought to be decreased. From secondary networks purpose of reading, there's a requirement to assign networks to Sus in such how that overall interference is reduced, enabling the accommodation of a growing variety of Sus. This paper presents a network choice and channel allocation mechanism so as to extend revenue by accommodating a lot of Sus and line of work to their preferences, whereas at an equivalent time, respecting the first network operator's policies. An optimization drawback is developed so as to reduce accumulated interference incurred by licensed users and also the quantity that Sus have to get hold of exploitation the first network. The aim is to produce Sus with a particular QoS at a lower cost, subject to the interference constraints of every available network with idle channels. Particle swarm optimization and a changed version of the genetic algorithmic rule square measure accustomed solve the optimization problem. Finally, this paper is supported by intensive simulation results that illustrate the effectiveness of the proposed ways in finding a near-optimal resolution.

References

  1. 'Cisco visual networking index: Global mobile data traf c forecast update 2014 2019,'White Paper c11-520862, May 2015. [Online]. Available: http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862. html
  2. S. Chen and J. Zhao, 'The requirements, challenges, and technologies for 5G of terrestrial mobile telecommunication,'IEEE Commun. Mag., vol. 52, no. 5, pp. 36 43, May 2014.
  3. M. Iwamura, 'NGMN view on 5G architecture,'in Proc. IEEE 81st Veh. Technol. Conf. (VTC Spring), Glasgow, Scotland, May 2015, pp. 1 5.
  4. H. Droste et al., 'The METIS 5G architecture: A summary of METIS work on 5G architectures,'in Proc. IEEE 81st Veh. Technol. Conf. (VTC Spring), Glasgow, Scotland, May 2015, pp. 1 5.
  5. P. K. Agyapong, M. Iwamura, D. Staehle, W. Kiess, and A. Benjebbour, 'Design considerations for a 5G network architecture,'IEEE Commun. Mag., vol. 52, no. 11, pp. 65 75, Nov. 2014.
  6. A. M. Akhtar, X. Wang, and L. Hanzo, 'Synergistic spectrum sharing in 5G HetNets: A harmonized SDN-enabled approach,'IEEE Commun. Mag., vol. 54, no. 1, pp. 40 47, Jan. 2016.
  7. N. Bhushan et al., 'Network densi cation: The dominant theme for wire-less evolution into 5G,'IEEE Commun. Mag., vol. 52, no. 2, pp. 82 89, Feb. 2014.
  8. W. Ejaz and M. Ibnkahla, 'Machine-to-machine communications in cognitive cellular systems,'in Proc. IEEE Int. Conf. Ubiquitous Wireless Broadband (ICUWB), Montreal, QC, Canada, Oct. 2015, pp.1 5.
  9. C. Rattaro, L. Aspirot, and P. Belzarena, 'Analysis and characterization of dynamic spectrum sharing in cognitive radio networks,'in Proc. IEEE Int. Wireless Commun. Mobile Comput. Conf. (IWCMC), Dubrovnik, Croatia, Aug. 2015, pp. 166 171.
  10. W. Ejaz, N. ul Hasan, S. Lee, and H. S. Kim, 'I3S: Intelligent spec-trum sensing scheme for cognitive radio networks,'EURASIP J. Wireless Commun. Netw., vol. 2013, no. 1, pp. 1 10, Dec. 2013.
  11. W. Ejaz, N. Ul Hasan, and H. S. Kim, 'Distributed cooperative spectrum sensing in cognitive radio for ad hoc networks,'Comput. Commun., vol. 36, no. 12, pp. 1341 1349, Jul. 2013.
  12. M. Ju and K. Kang, 'Cognitive radio networks with secondary net-work selection,'IEEE Trans. Veh. Technol., vol. 65, no. 2, pp. 966 972, Feb. 2016.
  13. S. Andreev et al., 'Intelligent access network selection in converged multi-radio heterogeneous networks,'IEEE Wireless Commun., vol. 21, no. 6, pp.86 96, Dec. 2014.
  14. I. Ahmad, S. Liu, Z. Feng, Q. Zhang, and P. Zhang, 'Price based spectrum sharing and power allocation in cognitive femtocell network,'in Proc. IEEE 78th Veh. Technol. Conf. (VTC Fall), Las Vegas, NV, USA, Sep. 2013,pp.1 5.
  15. M.-D. Weng, B.-H. Lee, and J.-M. Chen, 'Two novel price-based algo-rithms for spectrum sharing in cognitive radio networks,'EURASIP J. Wireless Commun. Netw., vol. 2013, p. 265, Nov. 2013.
  16. M. Fathi, 'Price-based spectrum sharing and rate allocation in the down-link of multihop multicarrier wireless networks,'IET Netw., vol. 3, no. 4,pp.252 258, Nov. 2014.
  17. N. Ul Hasan, W. Ejaz, H. S. Kim, and J. H. Kim, 'Particle swarm optimiza-tion based methodology for solving network selection problem in cognitive radio networks,'in Proc. IEEE Frontiers Inf. Technol. (FIT), Islamabad, Pakistan, Dec. 2011, pp. 230 234.
  18. S. Hu, X. Wang, and M. Z. Shakir, 'A MIH and SDN-based framework for network selection in 5G HetNet: Backhaul requirement perspectives,'in Proc. IEEE Int. Conf. Commun. Workshop (ICCW), London, U.K., Jun. 2015, pp. 37 43.
  19. A. Orsino, G. Araniti, A. Molinaro, and A. Iera, 'Effective RAT selection approach for 5G dense wireless networks,'in Proc. IEEE 81st Veh. Technol.Conf.(VTC Spring), Glasgow,U.K.,May2015, pp.1 5.
  20. Y. Wang, J. Yu, X. Lin, and Q. Zhang, 'A uniform framework for net-work selection in cognitive radio networks,'in Proc. IEEE Int. Conf. Commun. (ICC), London, U.K., Jun. 2015, pp. 3708 3713.
  21. Y. Yang, S. Aissa, and K. N. Salama, 'Spectrum band selection in delay-QoS constrained cognitive radio networks,'IEEE Trans. Veh. Technol., vol. 64, no. 7, pp. 2925 2937, Jul. 2015.
  22. L.-C. Tseng, F.-T. Chien, D. Zhang, R. Y. Chang, W.-H. Chung, and C. Huang, 'Network selection in cognitive heterogeneous networks using stochastic learning,'IEEE Commun. Lett., vol. 17, no. 12, pp. 2304 2307, Dec. 2013.
  23. T. LeAnh, M. Van Nguyen, C. T. Do, C. S. Hong, S. Lee, and J. P. Hong, 'Optimal network selection coordination in heterogeneous cognitive radio networks,'in Proc. IEEE Int. Conf. Inf. Netw. (ICOIN), Bangkok, Thailand, Jan. 2013, pp. 163 168.
  24. R. A. Rashid et al., 'Ef cient in-band spectrum sensing using swarm intelligence for cognitive radio network,'Can. J. Elect. Comput. Eng., vol. 38, no. 2, pp. 106 115, May 2015.
  25. T. M. Shami, A. A. El-Saleh, and A. M. Kareem, 'On the detection perfor-mance of cooperative spectrum sensing using particle swarm optimization algorithms,'in Proc. IEEE 2nd Int. Symp. Telecommun. Technol. (ISTT), Langkawi, Malaysia, Nov. 2014, pp. 110 114.
  26. S. B. Behera and D. D. Seth, 'Resource allocation for cognitive radio network using particle swarm optimization,'in Proc. IEEE 2nd Int. Conf. Electron. Commun. Syst. (ICECS), Coimbatore, India, Feb. 2015,pp.665 667.
  27. Y. El Morabit, F. Mrabti, and E. H. Abarkan, 'Spectrum allocation using genetic algorithm in cognitive radio networks,'in Proc. IEEE 3rd Int. Workshop RFID Adapt. Wireless Sensor Netw. (RAWSN), Agadir, Morocco, May 2015, pp. 90 93.
  28. A. Sun, T. Liang, Y. Zhang, and W. Lu, 'An adaptive genetic based cogni-tive radio parameter adjustment algorithm,'in Proc. IEEE 7th Int. Symp. Comput. Intell. Design (ISCID), vol. 2. Hangzhou, China, Dec. 2014, pp.433 436.
  29. R. B. Lopez, S. M. Sanchez, E. M. G. Fernandez, R. D. Souza, and H. Alves, 'Genetic algorithm aided transmit power control in cognitive radio networks,'in Proc. IEEE 9th Int. Conf. Cognit. Radio Oriented Wireless Netw. Commun. (CROWNCOM), Oulu, Finland, Jun. 2014, pp.61 66.
  30. Y. Qu, M. Wang, and J. Hu, 'A new energy-ef cient scheduling algorithm based on particle swarm optimization for cognitive radio networks,'in Proc. IEEE Int. Conf. Signal Process., Commun. Comput. (ICSPCC), Guilin, China, Aug. 2014, pp. 467 472.
  31. H. Kim, J. Choi, and K. G. Shin, 'Hierarchical market competition in a duopoly super Wi-Fi spectrum market,'IEEE J. Sel. Areas Commun., vol. 31, no. 11, pp. 2580 2591, Nov. 2013.
  32. W. Ejaz, G. A. Shah, N. Ul Hasan, and H. S. Kim, 'Energy and throughput ef cient cooperative spectrum sensing in cognitive radio sensor networks,'Trans. Emerg. Telecommun. Technol., vol. 26, no. 7, pp. 1019 1030, Jul. 2015.
  33. A. Kumar and K. G. Shin, 'DSASync: Managing end-to-end connections in dynamic spectrum access wireless LANs,'IEEE/ACM Trans. Netw., vol. 20, no. 4, pp. 1068 1081, Aug. 2012.
  34. R. Poli, J. Kennedy, and T. Blackwell, 'Particle swarm optimization,'Swarm Intell., vol. 1, no. 1, pp. 33 57, Jun. 2007.
  35. M. Çunka³ and M. Y. Özsaglam, 'A comparative study on particle swarm optimization and genetic algorithms for traveling salesman problems,'Cybern. Syst., Int. J., vol. 40, no. 6, pp. 490 507, Aug. 2009.
  36. P. Pongcharoen, W. Promtet, P. Yenradee, and C. Hicks, 'Stochastic optimisation timetabling tool for university course scheduling,'Int. J. Production Econ., vol. 112, no. 2, pp. 903 918, Apr. 2008.

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Published

2017-09-30

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Section

Research Articles

How to Cite

[1]
Dr. Srihari Chintha, " 5G Heterogeneous Networks Future Assessment on Network Channel Allocation and Particle Swarm Optimization (PSO), IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.1069-1077, September-October-2017.