The cell assignment problem is combinatorial, with increased complexity when it is tackled considering resource allocation. This paper models joint cell assignment and resource allocation for cellular heterogeneous networks, and formalizes cell assignment as an optimization problem. Exact algorithms can find optimal solutions to the cell assignment problem, but their execution time increases drastically with realistic network deployments. In turn, heuristics are able to find solutions in reasonable execution times, but they get usually stuck in local optima, thus failing to find optimal solutions. Metaheuristic approaches have been successful in finding solutions closer to the optimum one to combinatorial problems for large instances. In this paper we propose a fast and efficient heuristic that yields very competitive cell assignment solutions compared to those obtained with three of the most widely-used metaheuristics, which are known to find solutions close to the optimum due to the nature of their search space exploration. Our heuristic approach adds energy expenditure reduction in its algorithmic design. Through simulation and formal statistical analysis, the proposed scheme has been proved to produce efficient assignments in terms of the number of served users, resource allocation and energy savings, while being an order of magnitude faster than metaheuritsic-based approaches.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Abdelnasser, A., & Hossain, E. (2016). Resource allocation for an ofdma cloud-ran of small cells underlaying a macrocell. IEEE Transactions on Mobile Computing, 15(11), 2837–2850. https://doi.org/10.1109/TMC.2015.2513052.
Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C. K., et al. (2014). What will 5G be? IEEE Journal on Selected Areas in Communications, 32(6), 1065–1082. https://doi.org/10.1109/JSAC.2014.2328098.
Bhushan, N., Li, J., Malladi, D., Gilmore, R., Brenner, D., Damnjanovic, A., et al. (2014). Network densification: The dominant theme for wireless evolution into 5G. IEEE Communications Magazine, 52(2), 82–89. https://doi.org/10.1109/MCOM.2014.6736747.
Bland, J. M., & Altman, D. G. (1995). Multiple significance tests: The bonferroni method. BMJ, 310(6973), 170.
Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3), 268–308. https://doi.org/10.1145/937503.937505.
Boche, H., Naik, S., & Alpcan, T. (2011). Characterization of convex and concave resource allocation problems in interference coupled wireless systems. IEEE Transactions on Signal Processing, 59(5), 2382–2394. https://doi.org/10.1109/TSP.2011.2112652.
Bu, T., Li, L., & Ramjee, R. (2006). Generalized proportional fair scheduling in third generation wireless data networks. In Proceedings IEEE INFOCOM 2006. 25TH IEEE international conference on computer communications (pp. 1–12). https://doi.org/10.1109/INFOCOM.2006.145.
CISCO. (2017). Cisco visual networking index: Global mobile data traffic forecast update. www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-741490.pdf.
Coello, C. A. C., Lamont, G. B., Van Veldhuizen, D. A., et al. (2007). Evolutionary algorithms for solving multi-objective problems (Vol. 5). New York: Springer.
Dréo, J., Pétrowski, A., Siarry, P., & Taillard, E. (2006). Metaheuristics for hard optimization: Methods and case studies. Berlin: Springer.
Feng, M., Mao, S., & Jiang, T. (2017). Base station on-off switching in 5G wireless networks: Approaches and challenges. IEEE Wireless Communications, 24(4), 46–54. https://doi.org/10.1109/MWC.2017.1600353.
Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200), 675–701.
Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76(2), 60–68.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99.
Mesodiakaki, A., Adelantado, F., Antonopoulos, A., Alonso, L., & Verikoukis, C. (2016). Energy and spectrum efficient user association in 5G heterogeneous networks. In 2016 IEEE 27th annual international symposium on personal, indoor, and mobile radio communications (PIMRC) (pp. 1–6). https://doi.org/10.1109/PIMRC.2016.7794877.
Mesodiakaki, A., Zola, E., & Kassler, A. (2017). User association in 5g heterogeneous networks with mesh millimeter wave backhaul links. In 2017 IEEE 18th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM) (pp. 1–6). https://doi.org/10.1109/WoWMoM.2017.7974342.
Mogensen, P., Na, W., Kovacs, I. Z., Frederiksen, F., Pokhariyal, A., Pedersen, K. I., Kolding, T., Hugl, K., & Kuusela, M. (2007). LTE capacity compared to the shannon bound. In Vehicular technology conference, 2007. VTC2007-Spring. IEEE 65th (pp. 1234–1238). https://doi.org/10.1109/VETECS.2007.260.
Peng, M., Wang, C., Lau, V., & Poor, H. V. (2015). Fronthaul-constrained cloud radio access networks: Insights and challenges. IEEE Wireless Communications, 22(2), 152–160. https://doi.org/10.1109/MWC.2015.7096298.
Qualcomm. (2013). The 1000x data challenge. https://www.qualcomm.com/invention/1000x.
Ravanshid, A., Rost, P., Michalopoulos, D. S., Phan, V. V., Bakker, H., Aziz, D., Tayade, S., Schotten, H. D., Wong, S., & Holland, O. (2016). Multi-connectivity Functional Architectures in 5G. In 2016 IEEE international conference on communications workshops (ICC) (pp. 187–192).
Rubio-Loyola, J., Gonzalez-Hernandez, L., Diez, L., Agüero, R., & Serrat, J. (2014). An energy-oriented optimization algorithm for solving the cell assignment problem in 4G-LTE communication networks. In 2014 IFIP Wireless Days (WD) (pp. 1–4). https://doi.org/10.1109/WD.2014.7020851.
Seng, S., Li, X., Ji, H., & Zhang, H. (2018). Joint access selection and heterogeneous resources allocation in UDNS with mec based on non-orthogonal multiple access. In 2018 IEEE international conference on communications workshops (ICC Workshops) (pp. 1–6). https://doi.org/10.1109/ICCW.2018.8403502.
Siomina, I., & Yuan, D. (2012). Load balancing in heterogeneous LTE: Range optimization via cell offset and load-coupling characterization. In 2012 IEEE international conference on communications (ICC) (pp. 1357–1361). https://doi.org/10.1109/ICC.2012.6364075.
Talbi, E. G. (2009). Metaheuristics: From design to implementation (Vol. 74). Hoboken: Wiley.
Tan, Z., Li, X., Yu, F. R., Chen, L., Ji, H., & Leung, V. C. M. (2017). Joint access selection and resource allocation in cache-enabled hcns with D2D communications. In 2017 IEEE wireless communications and networking conference (WCNC) (pp. 1–6). https://doi.org/10.1109/WCNC.2017.7925732.
Van Laarhoven. P. J., & Aarts. E. H. (1987). Simulated annealing. In Simulated annealing: Theory and applications (pp. 7–15). Springer.
Wu, D., Wu, Q., Xu, Y., & Liang, Y. (2017). Qoe and energy aware resource allocation in small cell networks with power selection, load management, and channel allocation. IEEE Transactions on Vehicular Technology, 66(8), 7461–7473. https://doi.org/10.1109/TVT.2017.2650949.
Wu, D., Nie, X., Asmare, E., Arkhipov, D., Qin, Z., Li, R., et al. (2018). Towards distributed SDN: Mobility management and flow scheduling in software defined urban IOT. IEEE Transactions on Parallel and Distributed Systems,. https://doi.org/10.1109/TPDS.2018.2883438.
Xiao, Z., Li, T., Ding, W., Wang, D., & Zhang, J. (2016a). Dynamic pci allocation on avoiding handover confusion via cell status prediction in lte heterogeneous small cell networks. Wireless Communications and Mobile Computing, 16(14), 1972–1986. https://doi.org/10.1002/wcm.2662.
Xiao, Z., Liu, H., Havyarimana, V., Li, T., & Wang, D. (2016b). Analytical study on multi-tier 5G heterogeneous small cell networks: Coverage performance and energy efficiency. Sensors16(11). https://doi.org/10.3390/s16111854. http://www.mdpi.com/1424-8220/16/11/1854.
Ye, Q., Rong, B., Chen, Y., Al-Shalash, M., Caramanis, C., & Andrews, J. G. (2013). User association for load balancing in heterogeneous cellular networks. IEEE Transactions on Wireless Communications, 12(6), 2706–2716. https://doi.org/10.1109/TWC.2013.040413.120676.
Zhao, N., Yu, F. R., & Leung, V. C. M. (2015). Opportunistic communications in interference alignment networks with wireless power transfer. IEEE Wireless Communications, 22(1), 88–95. https://doi.org/10.1109/MWC.2015.7054723.
Zhao, N., Yu, F. R., Sun, H., & Li, M. (2016). Adaptive power allocation schemes for spectrum sharing in interference-alignment-based cognitive radio networks. IEEE Transactions on Vehicular Technology, 65(5), 3700–3714. https://doi.org/10.1109/TVT.2015.2440428.
Zhao, N., Cao, Y., Yu, F. R., Chen, Y., Jin, M., & Leung, V. C. M. (2018). Artificial noise assisted secure interference networks with wireless power transfer. IEEE Transactions on Vehicular Technology, 67(2), 1087–1098. https://doi.org/10.1109/TVT.2017.2700475.
This paper has been supported by the National Council of Research and Technology (CONACYT) through Grant FONCICYT/272278 and the ERANetLAC (Network of the European Union, Latin America, and the Caribbean Countries) Project ELAC2015/T100761. This paper is partially supported also by the ADVICE Project, TEC2015-71329 (MINECO/FEDER) and the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 777067 (NECOS Project).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Rubio-Loyola, J., Aguilar-Fuster, C., Diez, L. et al. Fast and efficient energy-oriented cell assignment in heterogeneous networks. Wireless Netw 26, 3119–3137 (2020). https://doi.org/10.1007/s11276-019-02047-x
- Cell assignment
- Resource allocation
- Energy efficiency
- Cellular networks
- Heterogeneous networks
- Dense networks