Recent Advances in Computational Optimization pp 87-116 | Cite as
A Model for Wireless-Access Network Topology and a PSO-Based Approach for Its Optimization
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Abstract
By the year 2020, the global network of connected sensors and devices will contain 50 billion connected devices and be the single largest factor in global power consumption. The planet’s ICT infrastructure already exceeds 10% of mankind’s power consumption (tendency: rising). The complexity of designing the topology for extend wireless access to ensure a thorough and economically sound signal coverage in buildings (from a building’s base station to distributed antennas throughout the building, through a complex network of coaxial cables and power splitters) increases exponentially (\({O(n^{n-2})}\)). We present our results from using Particle Swarm Optimization (PSO) to provide near optimal network topology for distributed in-building antenna systems. We use Prüfer code representation to efficiently traverse through different spanning tree solutions. Our approach is scalable and robust, capable of producing I-DAS design advice for buildings beyond one hundred floors. We demonstrate that our model is capable of obtaining optimal solutions for small buildings and near optimal solutions for tall buildings.
Keywords
Global Power Consumption Room Telephone Distributed Antenna Systems (DAS) Individual Floor Total Deployment CostNotes
Acknowledgements
The authors are grateful for the support from the UAE ICT-Fund on the project “Biologically Inspired Network Services”. We acknowledge K. Poon (EBTIC, KUST) for bringing the I-DAS problem to our attention. HH acknowledges the hospitality of the EBTIC Institute and F. Saffre (EBTIC, KUST) during his fellowship 2017.
References
- 1.Adjiashvili, D., Bosio, S., Li, Y., Yuan, D.: Exact and approximation algorithms for optimal equipment selection in deploying in-building distributed antenna systems. IEEE Trans. Mob. Comput. 14(4), 702–713 (2015)CrossRefGoogle Scholar
- 2.Atawia, R., Ashour, M., El Shabrawy, T., Hammad, H.: Indoor distributed antenna system planning with optimized antenna power using genetic algorithm. In: 2013 IEEE 78th Vehicular Technology Conference (VTC Fall), pp. 1–6 (September 2013)Google Scholar
- 3.Atia, D.Y.: Indoor distributed antenna systems deployment optimization with particle swarm optimization. M.Sc. thesis, Khalifa University of Science, Technology and Research (2015)Google Scholar
- 4.Atia, D.Y., Ruta, D., Poon, K., Ouali, A., Isakovic, A.F.: Cost effective, scalable design of indoor distributed antenna systems based on particle swarm optimization and Prüfer strings. In: IEEE Proceedings of 2016 IEEE Congress on Evolutionary Computation, Vancouver, Canada (July 2016)Google Scholar
- 5.Beijner, H.: The importance of in-building solutions in third-generation networks. Ericson Rev. 2, 90 (2004)Google Scholar
- 6.Borchardt, C.W.: über eine Interpolationsformel für eine Art symmetrischer Funktionen und über deren Anwendung. In: Math. Abh. Akad. Wiss. zu Berlin, pp. 1–20. Berlin (1860)Google Scholar
- 7.Cayley, A.: On the theory of the analytical forms called trees. Phil. Mag. 13, 172–6 (1857)CrossRefGoogle Scholar
- 8.Cayley, A.: Volume 13 of Cambridge Library Collection - Mathematics, pp. 26–28. Cambridge University Press, Cambridge (July 2009)Google Scholar
- 9.Chen, L., Yuan, D.: Mathematical modeling for optimal design of in-building distributed antenna systems. Comput. Netw. 57(17), 3428–3445 (2013)CrossRefGoogle Scholar
- 10.Gottlieb, J., Julstrom, B.A., Raidl, G.R., Rothlauf, F.: Prüfer numbers: a poor representation of spanning trees for evolutionary search. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 343–350, San Francisco, CA, USA. Morgan Kaufmann Publishers, Burlington (2001)Google Scholar
- 11.Hildmann, H., Rta, D., Atia, D.Y., Isakovic, A.F.: Using branching-property preserving Prüfer code to encode solutions for particle swarm optimisation. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 429–432 (September 2017)Google Scholar
- 12.Hiltunen, K., Olin, B., Lundevall, M.: Using dedicated in-building systems to improve HSDPA indoor coverage and capacity. In: 61st IEEE Conference on Vehicular Technology, pp. 2379–2383 (2005)Google Scholar
- 13.Julstrom, B.A.: Quick decoding and encoding of Prüfer strings: exercises in data structures (2005)Google Scholar
- 14.Ma, R.-J., Yu, N.-Y., Hu, J.-Y.: Application of particle swarm optimization algorithm in the heating system planning problem. Sci. World J. (2013)Google Scholar
- 15.Marzetta, T.L.: Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans. Wireless Commun. 9(11), 3590–3600 (2010)CrossRefGoogle Scholar
- 16.Micikevičius, P., Caminiti, S., Deo, N.: Linear-time algorithms for encoding trees as sequences of node labels (2007)Google Scholar
- 17.Paulden, T., Smith, D.K.: Developing new locality results for the Prüfer code using a remarkable linear-time decoding algorithm. Electr. J. Comb. 14 (2007)Google Scholar
- 18.Prüfer, H.: Neuer Beweis eines Satzes über Permutationen. Archiv der Mathematik und Physik 27, 742–744 (1918)zbMATHGoogle Scholar
- 19.Ren, H., Liu, N., Pan, C., He, C.: Energy efficiency optimization for MIMO distributed antenna systems. IEEE Trans. Veh. Technol. 99, 1–1 (2016)Google Scholar
- 20.Sun, Q., Jin, S., Wang, J., Zhang, Y., Gao, X., Wong, K.K.: On scheduling for massive distributed MIMO downlink. In: 2013 IEEE Global Communications Conference (GLOBECOM), pp. 4151–4156 (December 2013)Google Scholar
- 21.You, X.H., Wang, D.M., Sheng, B., Gao, X.Q., Zhao, X.S., Chen, M.: Cooperative distributed antenna systems for mobile communications [coordinated and distributed MIMO]. IEEE Wirel. Commun. 17(3), 35–43 (2010)CrossRefGoogle Scholar
- 22.Ross, Y., Watteyne, T.: Reliable, low power wireless sensor networks for the internet of things: making wireless sensors as accessible as web servers. White paper, Linear Technology (December 2013)Google Scholar
- 23.Zhou, L., Li, B., Wang, F.: Particle swarm optimization model of distributed network planning. JNW 8(10), 2263–2268 (2013)CrossRefGoogle Scholar