Fast and efficient energy-oriented cell assignment in heterogeneous networks
- 23 Downloads
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.
KeywordsCell assignment Resource allocation Metaheuristic Energy efficiency Cellular networks Heterogeneous networks Dense networks
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).
- 7.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.
- 8.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.
- 15.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.
- 16.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.
- 17.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.
- 19.Qualcomm. (2013). The 1000x data challenge. https://www.qualcomm.com/invention/1000x.
- 20.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).Google Scholar
- 21.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.
- 22.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.
- 23.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.
- 25.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.
- 26.Van Laarhoven. P. J., & Aarts. E. H. (1987). Simulated annealing. In Simulated annealing: Theory and applications (pp. 7–15). Springer.Google Scholar
- 29.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.CrossRefGoogle Scholar