Proceedings ArticleDOI
Genetic approach with a new representation for base station placement in mobile communications
Jin Kyu Han,Byoung Seong Park,Yong Seok Choi,Han Kyu Park +3 more
- Vol. 4, pp 2703-2707
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TLDR
A new representation describing base station placement with a real number is proposed, and new genetic operators are introduced that can describe not only the locations of the base stations but also the number of those.Abstract:
In this paper, we find the best base station placement using a genetic approach. A new representation describing base station placement with a real number is proposed, and new genetic operators are introduced. This new representation can describe not only the locations of the base stations but also the number of those. Considering both coverage and economic efficiency, we also suggest a weighted objective function. Our algorithm is applied to an obvious optimization problem and then is verified. Moreover, our approach is tried in an inhomogeneous traffic density environment. The simulation result proves that the algorithm enables one to find near optimal base station placement and the efficient number of base stations.read more
Citations
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Proceedings ArticleDOI
Layout optimization for a wireless sensor network using a multi-objective genetic algorithm
Damien B. Jourdan,O. L. de Weck +1 more
TL;DR: This paper examines the optimization of wireless sensor network layouts by benchmarking a multi objective genetic algorithm (MOGA) for the sensor placement, where the two competing objectives considered are the total sensor coverage and the lifetime of the network.
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On the design of smart parking networks in the smart cities: an optimal sensor placement model
TL;DR: This paper proposes an exact solution to the node placement problem using single-step and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of libraries and reveals that the solution outperforms a random placement in terms of both energy consumption, delay and throughput achieved by a smart parking network.
Proceedings ArticleDOI
Genetic Approach for Network Planning in the RFID Systems
TL;DR: A genetic approach for tackling the complex optimization problem of choosing the optimum locations for readers (antennas) in a RFID communications system is presented and computational results are presented for a typical test scenario.
Journal ArticleDOI
Comparison and evaluation of multiple objective genetic algorithms for the antenna placement problem
TL;DR: This study uses a greedy algorithm to select and configure base station locations and compares the ability of four state-of-the-art multiple objective genetic algorithms to find an optimal ordering of potential base stations.
Journal ArticleDOI
Wireless Heterogeneous Transmitter Placement Using Multiobjective Variable-Length Genetic Algorithm
TL;DR: The proposed algorithm can achieve the optimal number of transmitters with coverage exceeding 98% on average for six benchmarks, and preferable experimental results demonstrate the high capability of the proposed algorithm for the wireless heterogeneous transmitter placement problem.
References
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Book
Adaptation in natural and artificial systems
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.
Journal ArticleDOI
Empirical formula for propagation loss in land mobile radio services
TL;DR: An empirical formula for propagation loss is derived from Okumura's report in order to put his propagation prediction method to computational use.
Proceedings ArticleDOI
Genetic approach to radio network optimization for mobile systems
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.
Proceedings ArticleDOI
Automatic base station placement and dimensioning for mobile network planning
X. Huang,U. Behr,W. Wiesbeck +2 more
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.
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