Abstract-The radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a noncomparable efficiency. Therefore, the aim of this paper is twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons of efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve the first aim we propose a canonical RND problem formulation driven by two main directives: technology independence and a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis.
RND (Radio Network Design) is an important problem in mobile telecommunications (for example in mobile/cellular telephony), being also relevant in the rising area of sensor networks. This problem consists in covering a certain geographical area by using the smallest number of radio antennas achieving the biggest cover rate. To date, several radio antenna models have been used: square coverage antennas, omnidirectional antennas that cover a circular area, etc. In this work we use omnidirectional antennas. On the other hand, RND is an NP-hard problem; therefore its solution by means of evolutionary algorithms is appropriate. In this work we study different evolutionary approaches to tackle this problem. PBIL (Population-Based Incremental Learning) is based on genetic algorithms and competitive learning (typical in neural networks). DE (Differential Evolution) is a very simple population-based stochastic function minimizer used in a wide range of optimization problems, including multi-objective optimization. SA (Simulated Annealing) is a classic trajectory descent optimization technique. Finally, CHC is a particular class of evolutionary algorithm which does not use mutation and relies instead on incest prevention and disruptive crossover. Due to the complexity of such a large analysis including so many techniques, we have used not only sequential algorithms, but also grid computing with BOINC in order to execute thousands of experiments in only several days using around 100 computers.
The Radio Network Design (RND) problem for wide area networks consists of determining the optimal locations for base station transmitters in order to get a maximum coverage area with a minimum number of transmitters. Because of the grand amount of possible solutions, this problem is most suitable to be tackled with bio-inspired techniques instead of classical approaches. Our recent research work exploited a differential evolution based algorithm to deal with this problem. This paper describes an enhanced implementation of our Differential Evolutionbased algorithm that is able to tackle the RND problem in a most effective and faster way that the one observed in our previous work. This approach includes the comparison of different operators and the usage of an alternate initialisation model.
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