Randomized heuristics for handover minimization in mobility networks
A mobile device connects to the cell tower (base station) from which it receives the strongest signal. As the device moves it may connect to a series of towers. The process in which the device changes the base station it is connected to is called handover. A cell tower is connected to a radio network controller (RNC) which controls many of its operations, including handover. Each cell tower handles an amount of traffic and each radio network controller has capacity to handle a maximum amount of traffic from all base stations connected to it. Handovers between base stations connected to different RNCs tend to fail more often than handovers between base stations connected to the same RNC. Handover failures result in dropped connections and therefore should be minimized. The Handover Minimization Problem is to assign towers to RNCs such that RNC capacity is not violated and the number of handovers between base stations connected to different RNCs is minimized. We describe an integer programming formulation for the handover minimization problem and show that state-of-the-art integer programming solvers can solve only very small instances of the problem. We propose several randomized heuristics for finding approximate solutions of this problem, including a GRASP with path-relinking for the generalized quadratic assignment problem, a GRASP with evolutionary path-relinking, and a biased random-key genetic algorithm. Computational results are presented.
KeywordsMobility networks Handover minimization Randomized heuristics GRASP Biased random-key genetic algorithm.
This research was done while L.F. Morán-Mirabal and R.M.A. Silva were Visiting Scholars at AT&T Labs Research in Florham Park, New Jersey. The research of L.F. Morán-Mirabal was partially funded by Tecnológico de Monterrey Research Fund CAT128. The research of R.M.A Silva was partially supported by the Brazilian National Council for Scientific and Technological Development (CNPq), the Foundation for Support of Research of the State of Minas Gerais (FAPEMIG), the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES), the Office for Research and Graduate Studies of the Federal University of Pernambuco (PROPESQ), and the Foundation for Support of Science and Technology of the State of Pernambuco (FACEPE).
- Glover, F.: Tabu search and adaptive memory programming—advances, applications and challenges. In: Barr, R.S., Helgason, R.V., Kennington, J.L. (eds.) Interfaces in Computer Science and Operations Research, pp. 1–75. Boston, Kluwer Academic Publishers (1996)Google Scholar
- Morán-Mirabal, L.F., González-Velarde, J.L., Resende, M.G.C.: Automatic tuning of GRASP with evolutionary path-relinking. In: Blesa, M.J. (ed.) Hybrid Metaheuristics, vol. 7919, pp. 62–77. Lecture Notes in Computer Science, Heidelberg (2013)Google Scholar
- Resende, M.G.C., Ribeiro, C.C., Glover, F., Martí, R.: Scatter search and path-relinking: Fundamentals, advances, and applications. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics, vol. 146 of International Series in Operations Research & Management Science, 2nd edn, pp. 87–107. Springer, Boston (2010)Google Scholar
- Spears, W.M., DeJong, K.A.: On the virtues of parameterized uniform crossover. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 230–236 (1991)Google Scholar
- Toso, R.F., Resende, M.G.C.: A C++ application programming interface for biased random-key genetic algorithms. Technical report, AT &T Labs Research. Florham Park, NJ (2012)Google Scholar
- University of Wisconsin. Condor high throughput computing, 2012. http://research.cs.wisc.edu/condor. Accessed 25 June 2012