ETRI Journal

  • 제40권6호
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  • Pages.726-735
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  • 2018
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Discrete bacterial foraging optimization for resource allocation in macrocell-femtocell networks

  • Lalin, Heng (Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada) ;
  • Mustika, I Wayan (Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada) ;
  • Setiawan, Noor Akhmad (Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada)
  • 투고 : 2017.08.22
  • 심사 : 2018.05.14
  • 발행 : 2018.12.06
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초록

Femtocells are good examples of the ultimate networking technology, offering enhanced indoor coverage and higher data rate. However, the dense deployment of femto base stations (FBSs) and the exploitation of subcarrier reuse between macrocell base stations and FBSs result in significant co-tier and cross-tier interference, thus degrading system performance. Therefore, appropriate resource allocations are required to mitigate the interference. This paper proposes a discrete bacterial foraging optimization (DBFO) algorithm to find the optimal resource allocation in two-tier networks. The simulation results showed that DBFO outperforms the random-resource allocation and discrete particle swarm optimization (DPSO) considering the small number of steps taken by particles and bacteria.

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참고문헌

  1. F. Mhiri, K. Sethom, and R. Bouallegue, A survey on interference management techniques in femtocell self-organizing networks, J. Netw. Comput. Applicat. 36 (2013), no. 1, 58-65. https://doi.org/10.1016/j.jnca.2012.04.021
  2. H. Marshoud et al., Genetic algorithm based resource allocation and interference mitigation for OFDMA macrocell-femtocells networks, Wireless Mobile Netw. Conf. (WMNC), Dubai, United Arab Emirates, Apr. 23-25, 2013, pp. 1-7.
  3. V. Chandrasekhar and J. G. Andrews, Femtocell networks: A survey, IEEE Commun. Mag. 46 (2008), no. 9, 59-67. https://doi.org/10.1109/MCOM.2008.4623708
  4. D. Y. Yuan et al., Stackelberg game for backhaul resource allocation in the two-tier LTE femtocell networks, J. China Univ. Posts Telecommun. 21 (2014), no. 2, 32-39.
  5. M. Feng, S. Mao, and T. Jiang, Joint duplex mode selection, channel allocation, and power control for full-duplex cognitive femtocell networks, Digit. Commun. Netw. 1 (2015), no. 1, 30-44. https://doi.org/10.1016/j.dcan.2015.01.002
  6. I. W. Mustika et al., Potential game approach for self-organization scheme in open access heterogeneous networks, Int. ICST Conf. Cogn. Radio Oriented Wireless Netw. Commun. (CROWNCOM), Osaka, Japan, June 1-3, 2011, pp. 216-220.
  7. O. Mehanna, Sharing vs. splitting spectrum in OFDMA femtocell networks, IEEE Int. Conf. Acoustics, Speech Signal Proc., Vancouver, Canada, May 26-31, 2013, pp. 4824-4828.
  8. X. Kang, Y. C. Liang, and H. K. Garg, Distributed power control for spectrum-sharing femtocell networks using Stackelberg game, IEEE Int. Conf. Commun., Kyoto, Japan, June 5-9, 2011, pp. 1-5.
  9. H. Marshoud et al., Realistic framework for resource allocation in macro-femtocell networks based on genetic algorithm, Telecommun. Syst. 63 (2016), no. 1, pp. 99-110. https://doi.org/10.1007/s11235-015-9976-x
  10. S. Padmapriya and M. Tamilarasi, A case study on femtocell access modes, Eng. Sci. Technol. Int. J. 19 (2016), no. 3, 1534-1542. https://doi.org/10.1016/j.jestch.2016.05.007
  11. B. G. Choi et al., A femtocell power control scheme to mitigate interference using listening TDD frame, Int. Conf. Inform. Netw., Barcelona, Spain, Jan. 26-28, 2011, pp. 241-244.
  12. H.-S. Jo et al., Interference mitigation using uplink power control for two-tier femtocell networks, IEEE Trans. Wireless Commun. 8 (2009), no. 10, 4906-4910. https://doi.org/10.1109/TWC.2009.080457
  13. T. Zahir et al., A downlink power control scheme for interference avoidance in femtocells, Int. Wireless Commun. Mob. Comput. Conf., Istanbul, Turkey, July 4-8, 2011, pp. 1222-1226.
  14. W. Yalong et al., Resource allocation scheme based on game theory in heterogeneous networks, J. China Univ. Posts Telecommun. 23 (2016), no. 3, 57-88. https://doi.org/10.1016/S1005-8885(16)60033-X
  15. N. Fath et al., Optimal resource allocation scheme in femtocell networks based on Bat algorithm, Asia-Pacific Conf. Commun., Yogyakarta, Indonesia, Aug. 25-27, 2016, pp. 281-285.
  16. D. Liu et al., The sub-channel allocation algorithm in femtocell networks based on ant colony optimization, Militay Commun. Conf., Orlando, FL, USA, Oct. 29-Nov. 1, 2012, pp. 1-6.
  17. H. Marshoud et al., Resource allocation in macrocell-femtocell network using genetic algorithm, IEEE Int. Conf. Wireless Mobile Comput., Netw. Commun. (WiMob), Barcelona, Spain, Oct. 8-10, 2012, pp. 474-479.
  18. R. Estrada, H. Otrok, and Z. Dziong, Resource allocation model based on particle swarm optimization for OFDMA macro-femtocell networks, IEEE Int. Conf. Adv. Netw. Telecommun. Syst., Kattankulathur, India, Dec. 15-18, 2013, pp. 1-6.
  19. X. Chen, L. Li, and X. Xiang, Ant colony learning method for joint MCS and resource block allocation in LTE femtocell downlink for multimedia applications with QoS guarantees, Multimed. Tools Applicat. 76 (2017), no. 3, 4035-4054. https://doi.org/10.1007/s11042-015-2991-9
  20. V. Sharma, A review of bacterial foraging optimization and its applications, National Conf. Futur. Asp. Artif. Intell. Ind. Autom. 1 (2012), 9-12.
  21. H. E. A. Ibrahim, F. N. Hassan, and A. O. Shomer, Optimal PID control of a brushless DC motor using PSO and BF techniques, Ain Shams Eng. J. 5 (2014), no. 2, 391-398.
  22. A. Rajni and I. Chana, Bacterial foraging based hyper-heuristic for resource scheduling in grid computing, Futur. Gener. Comput. Syst. 29 (2013), no. 3, 751-762. https://doi.org/10.1016/j.future.2012.09.005
  23. B. Bhushan and M. Singh, Adaptive control of DC motor using bacterial foraging algorithm, Applicat. Soft Comput. J. 11 (2011), no. 8, 4913-4920. https://doi.org/10.1016/j.asoc.2011.06.008
  24. O. P. Verma et al., A novel bacterial foraging technique for edge detection, Pattern Recogn. Lett. 32 (2011), no. 8, 1187-1196. https://doi.org/10.1016/j.patrec.2011.03.008
  25. B. Hernandez-Ocana, E. Mezura-Montes, and P. Pozos-Parra, A review of the bacterial foraging algorithm in constrained numerical optimization, IEEE Congress Evolutionary Comput., Cancun, Mexico, June 20-23, 2013, pp. 2695-2702.
  26. 3GPP TS 36.211, Physical channels and modulation, Technical Specification, 2014, pp. 1-121.
  27. S. S. Patnaik and A. K. Panda, Optimizing current harmonics compensation in three-phase power systems with an enhanced bacterial foraging approach, Int. J. Electr. Power Energy Syst. 61 (2014), 386-398. https://doi.org/10.1016/j.ijepes.2014.03.051
  28. Y.-W. Chen, C.-L. Lin, and A. Mimori, Multimodal medical image registration using particle swarm optimization, Int. Conf. Intell. Syst. Des. Applicat., Kaohsiung, Taiwan, Nov. 26-28, 2008, pp. 127-131.
  29. S. Sharma and H. M. Pandey, Genetic algorithm, particle swarm optimization and harmony search: A quick comparison, Int. Conf. - Cloud Syst. Big Data Eng. (Confluence), Noida, India, Jan. 14-15, 2016, pp. 40-44.