Multi-stage optimization for mobile radio network planning

Abstract

In this paper, the evolution of mobile radio network is presented. First of all, the network life cycle is considered. A mathematical modeling of these life periods is developed inside an optimization problem: optimal location of base stations. It is a combinatorial optimization problem. A multi-period model is built on a Concentrator Link approach. Finally, three different multi-period techniques are identified, they are based on Genetic Algorithm (GA) to tackle this problem on the design of micro-cellular networks.

Evolutionary algorithms for solving the automatic cell planning problem: a survey

“…Other specific multiobjective algorithms used are SEAMO (Raisanen and Whitaker (2005)) and MOCHC (Nebro et al (2007)). From the point of view of the formulation, the first proposals have adopted a single objective approach in which the different network aspects to be optimized are weighted into a single (aggregative) function (Calégari et al (1997), Chamaret and Condevaux-Lanloy (1998), Lieska et al (1998), Reininger et al (1999)). However, the recent advances in multi- Calégari et al (1996Calégari et al ( , 1997Calégari et al ( , 2001) dGA …”

“…It is worth mentioning here that, even though power tilt and azimuth are actually real-valued parameters, they are usually discretized into a rather small set of values in order to reduce the complexity of the optimization problem. This is the approach used in Altman et al (2002a,b), Cahon et al (2006), Jamaa et al (2004aJamaa et al ( ,b, 2006, Jedidi et al (2004), Meunier et al (2000), Picard et al (2005), Reininger et al (1999), Talbi et al (2007), Talbi and Meunier (2006), Zimmermann et al (2000Zimmermann et al ( , 2003a. The main advantage of this encoding scheme is that EAs are put to work on real solutions so therefore problem-domain specific knowledge can be easily included in the search.…”

“…The main advantage of this operator is that it considers somehow the connection between the sites in a topological way: only nearby sites are modified. However, under this encoding, the classical SPX crossover has also been used by Reininger et al (1999).…”

The use of intelligently deployed fixed relays to improve the performance of a UTRA-TDD system

“…Location optimisation algorithms for cellular planning have been investigated by many authors, e.g. [3]; however these methods are restricted to the optimization of BSs in a cellular environment. In this paper we use an enhancement of a well known optimization algorithm (the Combination Algorithm for Total optimisation -CAT [4]) in order to optimize the number and location of BSs and relay nodes.…”

A permutation-coded evolutionary strategy for multi-objective GSM network planning

“…Given that base station placement problem is NP-Hard (Mathar and Niessen 2000) with 2n solutions given n candidate sites, and the additional problem of configuring antennas at sites adds additional complexity with (m + 1) n solutions (Raisanen 2006) given m possible configuration settings, research in cell planning has included heuristic and meta-heuristic techniques, such as simulated annealing (Akl et al 2001;Anderson and McGeehan 1994;Mathar and Niessen 2000), tabu search (Han et al 2001;Lee and Kang 2000;Vasquez 2001a;Amaldi et al 2001a), and genetic algorithms (Lee and Kang 2000;Laki et al 2001;Han et al 2001;Huang et al 2000;Calegari et al 1997;Molina et al 1999;Meunier et al 2000;Reininger et al 1999), as well as deterministic heuristic algorithms (Chamaret et al 1997;Ibbetson and Lopes 1997;Tcha and Myung 2000;Ganz et al 1997;Tutschku 1998;Molina et al 1999Molina et al , 2000Galota et al 2001;Mathar and Schmeink 2001;Zimmermann et al 2003). Sequential, or greedy, algorithms (Molina et al 1999;Amaldi et al 2001b) have been less well used and then predominantly for comparison purposes.…”