Advertisement

Wireless Networks

, Volume 20, Issue 6, pp 1369–1386 | Cite as

A metaheuristic-based downlink power allocation for LTE/LTE-A cellular deployments

A multiobjective strategy suitable for Self-Optimizing Networks
  • David González G.Email author
  • Mario García-Lozano
  • Silvia Ruiz
  • Dong Seop Lee
Article

Abstract

The explosive growth of cellular networks makes their deployment and maintenance more and more complex, time consuming, and expensive. Self-Organizing Networks have been recognized as a promising way to alleviate this problem by minimizing human intervention in such processes. This paper introduces a novel multiobjective framework, based on evolutionary optimization, aiming at improving network performance and users Quality of Service. By tuning the transmitted power at each cell, average intercell interference levels are minimized. The design of the proposed scheme is feasible for distributed implementations in Long Term Evolution (LTE) and LTE-Advanced networks and its operation is compatible with current specifications. The framework is able to provide effective network-specific optimization and obtained results show that gains in terms of network capacity and cell edge performance are 5 and 10 %, respectively. Energy savings always accompanied such enhancements with reductions up to 35 %.

keywords

OFDMA LTE LTE-A Self-Optimizing Networks SON Full frequency reuse Energy consumption Multiobjective optimization 

Notes

Acknowledgments

This work has been funded through the project TEC2011-27723-C02-01 (Spanish Industry Ministry) and the European Regional Development Fund (ERDF).

References

  1. 1.
    Accenture. (2012). Mobile Web Watch 2012. Available online at http://www.accenture.com.
  2. 2.
    Dahlman, E., Parkvall, S., & Sköld, J. (2011). 4G LTE/LTE-advanced for mobile broadband (1st ed.). Elsevier: Academic Press. ISBN: 978-0-12-385489-6.Google Scholar
  3. 3.
    Brand, A., & Aghvami, H. (2002). Multiple access protocols for mobile communications: GPRS, UMTS and beyond (1st ed.). London: John Wiley & Sons, Ltd.CrossRefGoogle Scholar
  4. 4.
    Hu, H., Zhang, J., Zheng, X., Yang, Y., & Wu, P. (2010). Self-configuration and self-optimization for LTE networks. IEEE Communications Magazine, 48(2), 94–100CrossRefGoogle Scholar
  5. 5.
    Peng, M., Liang, D., Wei, Y., Li, J., & Chen, H.-H. (2013). Self-configuration and self-optimization in LTE-A dvanced heterogeneous networks. IEEE Communications Magazine, 51(5), 36–45CrossRefGoogle Scholar
  6. 6.
    Weise, T. (2009). Global optimization algorithms—Theory and application (2nd ed.). Self-Published, Jun. 26, 2009, online available at http://www.it-weise.de/.
  7. 7.
    Group Radio Access Network. (2010). TS 36.423, TS 32.102, 3GPP GRAN, Jun 2010, v9.3.0.Google Scholar
  8. 8.
    Marwangi, M. M. S., Fisal, N., Yusof, S. K. S., Rashid, R., Ghafar, A., Saparudin, F., & Katiran, N. (2011). Challenges and practical implementation of self-organizing networks in lte/lte-advanced systems. In 2011 International conference on information technology and multimedia (ICIM).Google Scholar
  9. 9.
    Group Radio Access Network. (2010). TR 36.902: Self-configuring and self-optimizing network (SON) use cases and solutions, 3GPP, Jun 2010, v9.2.0.Google Scholar
  10. 10.
    Feng, S., & Seidel, E. (2008). Self-Organizing Networks (SON) in 3GPP Long Term Evolution. Nomor research, Technical Report.Google Scholar
  11. 11.
    Garcia-Lozano, M., Ruiz, S., & Olmos, J. (2004). Umts optimum cell load balancing for inhomogeneous traffic patterns. In 2004 IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall (Vol. 2, pp. 909–913).Google Scholar
  12. 12.
    d’Orey, P., Garcia-Lozano, M., & Ferreira, M. (2010). Automatic link balancing using fuzzy logic control of handover parameter. In 2010 IEEE 21st international symposium on personal indoor and mobile radio communications (PIMRC), pp. 2168–2173.Google Scholar
  13. 13.
    Li, Y., Feng, Z., Xu, D., Zhang, Q., & Tian, H. (2011). Automated optimal configuring of femtocell base stations’ parameters in enterprise femtocell network. In 2011 IEEE global telecommunications conference (GLOBECOM 2011), pp. 1–5.Google Scholar
  14. 14.
    Pasandideh, M., & St-Hilaire, M. (2010). Automatic planning of UMTS release 4.0 networks using realistic traffic. In 2010 IEEE international symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM).Google Scholar
  15. 15.
    Garcia-Lozano, M., Ruiz-Boque, S., Perez-Romero, J., & Sallent, O. (2008). Performance improvement of hsdpa/umts networks through dynamic code tuning. In IEEE 19th international symposium on personal, indoor and mobile radio communications, 2008. PIMRC 2008.Google Scholar
  16. 16.
    Benedicic, L., Stular, M., & Korosec, P. (2012). Balancing downlink and uplink soft-handover areas in umts networks. In 2012 IEEE congress on evolutionary computation (CEC), pp. 1–8.Google Scholar
  17. 17.
    Garcia-Lozano, M., Ruiz, S., & Olmos, J. (2003). CPICH power optimisation by means of simulated annealing in an utra-fdd environment. Electronics Letters, 39(23), 1676–1677CrossRefGoogle Scholar
  18. 18.
    Soldani, D., Alford, G., Parodi, F., & Kylvaja, M.(2007). An autonomic framework for self-optimizing next generation mobile networks. In IEEE international symposium on a World of Wireless, Mobile and Multimedia Networks, 2007. WoWMoM 2007, pp. 1–6.Google Scholar
  19. 19.
    He, H., Wen, X., Zheng, W., Sun, Y., & Wang, B. (2010). Game theory based load balancing in self-optimizing wireless networks. In 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE) (Vol. 4, pp. 415–418).Google Scholar
  20. 20.
    Temesvary, A. (2009). Self-configuration of antenna tilt and power for plug and play deployed cellular networks. In IEEE wireless communications and networking conference, 2009. WCNC 2009, pp. 1–6.Google Scholar
  21. 21.
    Wu, R., Wen, Z., Fan, C., Liu, J., & Ma, Z. (2010). Self-optimization of antenna configuration in lte-advance networks for energy saving. In 2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), pp. 529–534.Google Scholar
  22. 22.
    Lee, K., Lee, H., & Cho, D.-H. (2011). Collaborative resource allocation for self-healing in self-organizing networks. In 2011 IEEE International Conference on Communications (ICC), pp. 1–5.Google Scholar
  23. 23.
    Combes, R., Altman, Z., Haddad, M., & Altman, E. (Jun 2011). Self-optimizing strategies for interference coordination in OFDMA Networks. In 2011 IEEE International Conference on Communications Workshops (ICC).Google Scholar
  24. 24.
    Samdanis, K., & Brunner, M. (2011). Self-organized network management functions for relay Enhanced LTE-Advanced systems. In 2011 IEEE 22nd international symposium on Personal Indoor and Mobile Radio Communications (PIMRC).Google Scholar
  25. 25.
    Zhang, M., Li, W., Jia, S., Zhang, L., & Liu, Y. (2011). A lightly-loaded cell initiated load balancing in lte self-optimizing networks. In 2011 6th international ICST conference on Communications and Networking in China (CHINACOM).Google Scholar
  26. 26.
    Komine, T., Yamamoto, T., & Konishi, S. (2012). A proposal of cell selection algorithm for lte handover optimization. In 2012 IEEE Symposium on Computers and Communications (ISCC), pp. 000037–000042.Google Scholar
  27. 27.
    Li, X., Jin, H., Jiang, J., Hou, S., Peng, M., & Wang, G. (2012). A gradient projection based self-optimizing algorithm for inter-cell interference coordination in downlink ofdma networks. In 2012 7th international ICST conference on Communications and Networking in China (CHINACOM).Google Scholar
  28. 28.
    Bo, W., Yu, S., Lv, Z., & Wang, J. (2012). A novel self-optimizing load balancing method based on ant colony in lte network. In 2012 8th international conference on Wireless Communications, Networking and Mobile Computing (WiCOM), pp. 1–4.Google Scholar
  29. 29.
    Yang, S., Zhang, W., & Zhao, X. (2012). Virtual cell-breathing based load balancing in downlink lte-a self-optimizing networks. In 2012 international conference on Wireless Communications Signal Processing (WCSP).Google Scholar
  30. 30.
    Hou, I.-H., & Chen, C. S. (2013). An energy-aware protocol for self-organizing heterogeneous lte systems. IEEE Journal on Selected Areas in Communications, 31(5), 937–946CrossRefGoogle Scholar
  31. 31.
    Huang, Y., & Rao, B. (2013). An analytical framework for heterogeneous partial feedback design in heterogeneous multicell ofdma networks. IEEE Transactions on Signal Processing, 61(3) ,753–769CrossRefMathSciNetGoogle Scholar
  32. 32.
    Soret, B., Wang, H., Pedersen, K., & Rosa, C. (2013). Multicell cooperation for lte-advanced heterogeneous network scenarios. IEEE Wireless Communications, 20(1), 27–34CrossRefGoogle Scholar
  33. 33.
    Shen, Z., Andrews, J., & Evans, B. (2005). Adaptive resource allocation in multiuser ofdm systems with proportional rate constraints. IEEE Transactions on Wireless Communications, 4(6), 2726–2737CrossRefGoogle Scholar
  34. 34.
    Joung, J., & Sun, S. (2012). Power efficient resource allocation for downlink ofdma relay cellular networks. IEEE Transactions on Signal Processing, 60(5), 2447–2459CrossRefMathSciNetGoogle Scholar
  35. 35.
    Marques, A., Lopez-Ramos, L., Giannakis, G., Ramos, J., & Caamaño A. (2012). Optimal cross-layer resource allocation in cellular networks using channel- and queue-state information. IEEE Transactions on Vehicular Technology, 61(6), 2789–2807CrossRefGoogle Scholar
  36. 36.
    Cheung, K., Yang, S., & Hanzo, L. (2013). Achieving maximum energy-efficiency in multi-relay ofdma cellular networks: A fractional programming approach. IEEE Transactions on Communications, 61(7), 2746–2757CrossRefGoogle Scholar
  37. 37.
    González G., D., García-Lozano, M., Ruiz, S., Olmos, J., & Lee, D. S. (Sep 2012). Optimization of realistic full frequency reuse OFDMA-based cellular networks. In IEEE 23th international symposium on Personal, Indoor and Mobile Radio Communications, 2012. PIMRC 2012.Google Scholar
  38. 38.
    Group Radio Access Network. (2008). TS 36.201: LTE physical layer—General description, 3GPP, Dec 2008, v8.2.0.Google Scholar
  39. 39.
    Group Radio Access Network. (2010). TS 36.213: Physical layer procedures, 3GPP GRAN, Jun 2010, v9.2.0.Google Scholar
  40. 40.
    Sawaragi Y., Hirotaka I., & Tanino T. (1985). Theory of multiobjective optimization (1st ed.). London: Academic Press, Inc.zbMATHGoogle Scholar
  41. 41.
    Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3), 268–368CrossRefGoogle Scholar
  42. 42.
    González, D., Garcia-Lozano, M., Ruiz, S., & Olmos, J. On the need for dynamic downlink intercell interference coordination for realistic LTE deployments. Wireless Communications and Mobile Computing. John Wiley & Sons, Ltd. (accepted). doi: 10.1002/wcm.2191.
  43. 43.
    Gale, D. (2007). Linear programming and the simplex method. Notices of the AMS, 54(3), 364–369zbMATHMathSciNetGoogle Scholar
  44. 44.
    Gill, P. E., & Wong, E. (2010). Sequential quadratic programming methods. Department of Mathematics, University of California, San Diego, La Jolla, CA, Technical Report NA-10-03, Aug 2010.Google Scholar
  45. 45.
    Coello, C. A., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems (2nd ed.). Springer: Genetic and Evolutionary Computation Series.Google Scholar
  46. 46.
    Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197CrossRefGoogle Scholar
  47. 47.
    Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2), 173–195CrossRefGoogle Scholar
  48. 48.
    Verdone, R., Buehler, H., Cardona, N., Munna, A., Patelli, R., Ruiz, S., Grazioso, P., Zanella, A., Eisenblätter, A., & Geerdes, H. (2004). MORANS white paper—Update. COST 273, Athens (Greece), Technical Report available as TD(04)062, Jan. 26–28, 2004.Google Scholar
  49. 49.
    Group Radio Access Network. (2000). TR 25.942: RF system scenarios. 3GPP, Feb 2000, v2.1.3.Google Scholar
  50. 50.
    Fraile, R., Lázaro, O., & Cardona, N. (2003). Two dimensional shadowing model. COST 273, Prague (Czec Republic), Technical Report available as TD(03)171, Sep. 24–26, 2003.Google Scholar
  51. 51.
    Sorensen, T. B., Mogensen, P. E., & Frederiksen, F. (2005). Extension of the ITU Channel Models for Wideband (OFDM) Systems. In 2005 IEEE 62nd Vehicular Technology Conference, 2005. VTC-2005-Fall. Sep 2005.Google Scholar
  52. 52.
    Correia, L. M. et al. (2001). Identification of relevant parameters for traffic modelling and interference estimation. Information Society Technologies (IST), Technical Report available as IST-2000-28088-MOMENTUM-D21-PUB, Nov 2001.Google Scholar
  53. 53.
    Group Radio Access Network. (2008). TS 25.201: Physical layer—General description. 3GPP, May 2008, v8.1.0.Google Scholar
  54. 54.
    Lakshminarasimman, N., Baskar, S., Alphones, A., & Willjuice, I. M. (2011). Evolutionary multiobjective optimization of cellular base station locations using modified NSGA-II. Wireless Networks, 17(3), 597–609, Apr 2011, Kluwer Academic Publishers.Google Scholar
  55. 55.
    Spall, J. C. (2003) Introduction to stochastic search and optimization (1st ed.). New York: Wiley-InterscienceCrossRefzbMATHGoogle Scholar
  56. 56.
    Yang, Q., & Ding, S. (2007). Novel algorithm to calculate hypervolume indicator of Pareto approximation set. Advanced Intelligent Computing Theories and Applications, 2, 235–244Google Scholar
  57. 57.
    Zitzler, E., & Thiele, L. (1998). Multiobjective optimization using evolutionary algorithms—A comparative case study. In Parallel problem solving from nature V, Springer, pp. 292–301Google Scholar
  58. 58.
    Fleischer, M. (2003). The measure of Pareto optima applications to multi-objective metaheuristics. In Evolutionary multi-criterion optimization (EMO) 2003, Lecture Notes in Computer Science (LNCS) (Vol. 2632, pp. 519–533). Berlin, Heidelberg: Springer-Verlag.Google Scholar
  59. 59.
    Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. Swiss Federal Institute of Technology (ETH) Zurich, Technical Report, May, 2001, TIK-Report 103.Google Scholar
  60. 60.
    Group Radio Access Network. (2011). TS 36.331: Radio Resource Control (RRC) Protocol Specification, 3GPP, Jun 2011, v8.14.0.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • David González G.
    • 1
    Email author
  • Mario García-Lozano
    • 1
  • Silvia Ruiz
    • 1
  • Dong Seop Lee
    • 2
    • 3
  1. 1.Department of Signal Theory and CommunicationsUniversitat Politécnica de CatalunyaBarcelonaSpain
  2. 2.International Center for Numerical Methods in EngineeringBarcelonaSpain
  3. 3.Deloite Analytics/Consulting LLCSeoulKorea

Personalised recommendations