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 %.













Similar content being viewed by others
Notes
Although typically joint optimization of several parameters results in higher gains, the associated complexity or computational cost results in feasibility issues.
The framework proposed in this article is partially based on the scheme originally presented in [37].
A solution \({\mathbf{x}}_{1}\) is preferred to (dominates in the Pareto sense) another solution \({\mathbf{x}}_{2}, ({\mathbf{x}}_{1}\succ{\mathbf{x}}_{2})\), if \({\mathbf{x}}_{1}\) is better than \({\mathbf{x}}_{2}\) in at least one criterion and no worse in the remaining ones.
In LTE, a Resource Block (RB) is composed of a set of 12 contiguous subcarriers [38].
If particular spatial traffic distributions are required to be considered, the extension of the proposed multiobjective framework is straightforward by weighting different zones of the coverage area. This can be done by assigning different probabilities to individual pixels instead of assuming that every pixel has the same probability. Therefore, f 1 and f 2 would be computed according to the general definition of the expected value.
References
Accenture. (2012). Mobile Web Watch 2012. Available online at http://www.accenture.com.
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.
Brand, A., & Aghvami, H. (2002). Multiple access protocols for mobile communications: GPRS, UMTS and beyond (1st ed.). London: John Wiley & Sons, Ltd.
Hu, H., Zhang, J., Zheng, X., Yang, Y., & Wu, P. (2010). Self-configuration and self-optimization for LTE networks. IEEE Communications Magazine, 48(2), 94–100
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), 36–45
Weise, T. (2009). Global optimization algorithms—Theory and application (2nd ed.). Self-Published, Jun. 26, 2009, online available at http://www.it-weise.de/.
Group Radio Access Network. (2010). TS 36.423, TS 32.102, 3GPP GRAN, Jun 2010, v9.3.0.
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).
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.
Feng, S., & Seidel, E. (2008). Self-Organizing Networks (SON) in 3GPP Long Term Evolution. Nomor research, Technical Report.
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).
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.
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.
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).
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.
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.
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), 1676–1677
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.
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).
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.
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.
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.
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).
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).
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).
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.
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).
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.
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).
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), 937–946
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) ,753–769
Soret, B., Wang, H., Pedersen, K., & Rosa, C. (2013). Multicell cooperation for lte-advanced heterogeneous network scenarios. IEEE Wireless Communications, 20(1), 27–34
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), 2726–2737
Joung, J., & Sun, S. (2012). Power efficient resource allocation for downlink ofdma relay cellular networks. IEEE Transactions on Signal Processing, 60(5), 2447–2459
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), 2789–2807
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), 2746–2757
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.
Group Radio Access Network. (2008). TS 36.201: LTE physical layer—General description, 3GPP, Dec 2008, v8.2.0.
Group Radio Access Network. (2010). TS 36.213: Physical layer procedures, 3GPP GRAN, Jun 2010, v9.2.0.
Sawaragi Y., Hirotaka I., & Tanino T. (1985). Theory of multiobjective optimization (1st ed.). London: Academic Press, Inc.
Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3), 268–368
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.
Gale, D. (2007). Linear programming and the simplex method. Notices of the AMS, 54(3), 364–369
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.
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.
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), 182–197
Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2), 173–195
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.
Group Radio Access Network. (2000). TR 25.942: RF system scenarios. 3GPP, Feb 2000, v2.1.3.
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.
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.
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.
Group Radio Access Network. (2008). TS 25.201: Physical layer—General description. 3GPP, May 2008, v8.1.0.
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.
Spall, J. C. (2003) Introduction to stochastic search and optimization (1st ed.). New York: Wiley-Interscience
Yang, Q., & Ding, S. (2007). Novel algorithm to calculate hypervolume indicator of Pareto approximation set. Advanced Intelligent Computing Theories and Applications, 2, 235–244
Zitzler, E., & Thiele, L. (1998). Multiobjective optimization using evolutionary algorithms—A comparative case study. In Parallel problem solving from nature V, Springer, pp. 292–301
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.
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.
Group Radio Access Network. (2011). TS 36.331: Radio Resource Control (RRC) Protocol Specification, 3GPP, Jun 2011, v8.14.0.
Acknowledgments
This work has been funded through the project TEC2011-27723-C02-01 (Spanish Industry Ministry) and the European Regional Development Fund (ERDF).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
González G., D., García-Lozano, M., Ruiz, S. et al. A metaheuristic-based downlink power allocation for LTE/LTE-A cellular deployments. Wireless Netw 20, 1369–1386 (2014). https://doi.org/10.1007/s11276-013-0659-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-013-0659-9