The displacement of base station in mobile communication with genetic approach
01 Jan 2008-Eurasip Journal on Wireless Communications and Networking (Springer International Publishing)-Vol. 2008, Iss: 1, pp 580761
TL;DR: A genetic algorithm is used as optimization approach and a new representation that describes base station placement, transmitted power with real numbers, and new genetic operators is proposed and introduced that can describe the number of base stations.
Abstract: This paper addresses the displacement of a base station with optimization approach. A genetic algorithm is used as optimization approach. A new representation that describes base station placement, transmitted power with real numbers, and new genetic operators is proposed and introduced. In addition, this new representation can describe the number of base stations. For the positioning of the base station, both coverage and economy efficiency factors were considered. Using the weighted objective function, it is possible to specify the location of the base station, the cell coverage, and its economy efficiency. The economy efficiency indicates a reduction in the number of base stations for cost effectiveness. To test the proposed algorithm, the proposed algorithm was applied to homogeneous traffic environment. Following this, the proposed algorithm was applied to an inhomogeneous traffic density environment in order to test it in actual conditions. The simulation results show that the algorithm enables the finding of a near optimal solution of base station placement, and it determines the efficient number of base stations. Moreover, it can offer a proper solution by adjusting the weighted objective function.
Citations
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27 Apr 2011
TL;DR: This work presents a Genetic Algorithm for solving large instances of the Power, Frequency and Modulation Assignment Problem, arising in the design of wireless networks, and is the first Genetic Al algorithm that is proposed for such problem.
Abstract: Over the last decade, wireless networks have experienced an impressive growth and now play a main role in many telecommunications systems. As a consequence, scarce radio resources, such as frequencies, became congested and the need for effective and efficient assignment methods arose. In this work, we present a Genetic Algorithm for solving large instances of the Power, Frequency and Modulation Assignment Problem, arising in the design of wireless networks. To our best knowledge, this is the first Genetic Algorithm that is proposed for such problem. Compared to previous works, our approach allows a wider exploration of the set of power solutions, while eliminating sources of numerical problems. The performance of the algorithm is assessed by tests over a set of large realistic instances of a Fixed WiMAX Network.
31 citations
10 Dec 2012
TL;DR: This work generalizes the classical network design problem by adding cooperation as an additional decision dimension, develops a strong formulation for the resulting problem, and defines a new hybrid solution algorithm that combines exact large neighborhood search and ant colony optimization.
Abstract: Base station cooperation (BSC) has recently arisen as a promising way to increase the capacity of a wireless network. Implementing BSC adds a new design dimension to the classical wireless network design problem: how to define the subset of base stations (clusters) that coordinate to serve a user. Though the problem of forming clusters has been extensively discussed from a technical point of view, there is still a lack of effective optimization models for its representation and algorithms for its solution. In this work, we make a further step towards filling such gap: (1) we generalize the classical network design problem by adding cooperation as an additional decision dimension; (2) we develop a strong formulation for the resulting problem; (3) we define a new hybrid solution algorithm that combines exact large neighborhood search and ant colony optimization. Finally, we assess the performance of our new model and algorithm on a set of realistic instances of a WiMAX network.
13 citations
20 Dec 2013
TL;DR: In this article, an intelligent based algorithm for optimal BTS site placement has been proposed, which takes into consideration neighbour and regulation considerations objectively while determining cell site, the application will lead to a quantitatively unbiased evaluated decision making process in BTS placement.
Abstract: The increase of the base transceiver station (BTS) in most urban areas can be traced to the drive by network providers to meet demand for coverage and capacity. In traditional network planning, the final decision of BTS placement is taken by a team of radio planners, this decision is not fool proof against regulatory requirements. In this paper, an intelligent based algorithm for optimal BTS site placement has been proposed. The proposed technique takes into consideration neighbour and regulation considerations objectively while determining cell site. The application will lead to a quantitatively unbiased evaluated decision making process in BTS placement. An experimental data of a 2km by 3km territory was simulated for testing the new algorithm, results obtained show a 100% performance of the neighbour constrained algorithm in BTS placement optimization. Results on the application of GA with neighbourhood constraint indicate that the choices of location can be unbiased and optimization of facility placement for network design can be carried out.
9 citations
Cites methods from "The displacement of base station in..."
...The use of artificial intelligence have been undergoing much research and genetic algorithm has been found to be useful for solving this type of NP-hard problem [7,8,11,12]....
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13 Jun 2010
TL;DR: These experiments demonstrate that the IWO algorithm outperforms the algorithms such as Evolutionary Strategies (ES) and Genetic Algorithms (GA) for optimizing the UMTS mobile network.
Abstract: The problem of finding optimal locations of base stations, their pilot powers and channel assignments in UMTS mobile networks belongs to a class of NP-hard problems, and hence, metaheuristics optimization algorithms are widely used for this task. Invasive Weed Optimization (IWO) algorithm is relatively novel and succussed in several real-world applications. Our experiments demonstrate that the IWO algorithm outperforms the algorithms such as Evolutionary Strategies (ES) and Genetic Algorithms (GA) for optimizing the UMTS mobile network.
8 citations
Cites methods from "The displacement of base station in..."
...metaheuristics-based approach to optimization of UMTS networks concerns the application of Genetic Algorithms (GA) [2, 5, 11]....
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TL;DR: A strengthened binary linear programming model is proposed for representing the optimal DVB design problem, including power and scheduling configuration, and a new matheuristic for its solution is proposed, identifying solutions associated with much higher user coverage.
Abstract: Because of the introduction and spread of the second generation of the Digital Video Broadcasting—Terrestrial standard (DVB-T2), already active television broadcasters and new broadcasters that have entered in the market will be required to (re)design their networks. This is generating a new interest for effective and efficient DVB optimization software tools. In this work, we propose a strengthened binary linear programming model for representing the optimal DVB design problem, including power and scheduling configuration, and propose a new matheuristic for its solution. The matheuristic combines a genetic algorithm, adopted to efficiently explore the solution space of power emissions of DVB stations, with relaxation-guided variable fixing and exact large neighborhood searches formulated as integer linear programming (ILP) problems solved exactly. Computational tests on realistic instances show that the new matheuristic performs much better than a state-of-the-art optimization solver, identifying solutions associated with much higher user coverage.
7 citations
Cites methods from "The displacement of base station in..."
...Also, the adoption of bio-inspired and genetic heuristics is not new: if we focus on genetic and evolutionary algorithms, we can report the remarkable cases of: [15], which addresses the problem of the optimal positioning of base stations in a mobile network, encoding the location of base stations in the chromosomes of the genetic algorithm; [16], which addresses the decision problem of how establishing the optimal assignment of users to deployed transmitters, in particular in the context of WiMAX networks, encoding the assignment in the chromosomes; [17], which focuses on the frequency assignment problem (FAP), proposing a permutation-based genetic algorithm to solve minimum span and fixed spectrum variants of the FAP; [18], which focuses on the problem of setting the power emissions of base stations, proposing two distributed power control algorithms that are based on evolutionary computation techniques to fast solve the linear equation systems associated with power updates of the stations; [19], which proposes a genetic algorithm for addressing the joint problem of power, frequency and modulation scheme assignment in fixed networks based on the WiMAX technology....
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TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)
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"The displacement of base station in..." refers methods in this paper
...John Holland’s schema theorem and building-block hypothesis [4] have often been used to explain how the GA works....
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TL;DR: This paper focuses on the radio coverage problem, that is, to cover a maximum surface of a given geographical region at an optimal cost, which is solved with a bioinspired genetic algorithm.
Abstract: Size and complexity of future UMTS radio networks make their planning very difficult. This paper focuses on the radio coverage problem, that is, to cover a maximum surface of a given geographical region at an optimal cost. This combinatorial optimization problem is solved with a bioinspired genetic algorithm. A first prototype runs in parallel on a network of workstations. The obtained results are shown and discussed.
117 citations
"The displacement of base station in..." refers methods in this paper
...References [2, 3] utilized genetic approaches for the network planning....
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...Some research has been reported on methods for automatically determining the best possible base station placement [2, 3]....
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...In [2], a binary string representation, the classic representation method of genetic algorithm, is applied....
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24 Sep 2000
TL;DR: A highly efficient optimization strategy forms the core of the proposed algorithm that determines the number of base stations, their sites, and parameters to achieve a high-quality network that meets the requirements of area coverage, traffic capacity, and interference level.
Abstract: This paper presents an innovative algorithm for automatic base station placement and dimensioning. A highly efficient optimization strategy forms the core of the proposed algorithm that determines the number of base stations, their sites, and parameters to achieve a high-quality network that meets the requirements of area coverage, traffic capacity, and interference level, while trying to minimize system costs, including the frequency and financial costs. First, the hierarchical approach is outlined and it is applied to place base stations (BSs) for a large-scale network design. Also a fuzzy expert system is developed to exploit the expert experience to adjust BS parameters, e.g., the transmitted power, to improve the network performance. Simulation results are presented and analyzed.
50 citations
"The displacement of base station in..." refers background or methods in this paper
...References [2, 3] utilized genetic approaches for the network planning....
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...In order to reduce the computational complexity, a hierarchical approach is considered in [3]....
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...Some research has been reported on methods for automatically determining the best possible base station placement [2, 3]....
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...OVERVIEW OF GENETIC ALGORITHM Like other computational systems inspired by natural systems, genetic algorithms have been used in two ways: as techniques for solving technology problems, and as simplified scientific models that can answer questions about nature [3]....
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