A comparison of local search algorithms for radio link frequency assignment problems
SAC '96 Proceedings of the 1996 ACM symposium on Applied Computing
Tabu Search
Comparison of column generation models for channel assignment in cellular networks
Discrete Applied Mathematics - Special issue on the combinatorial optimization symposium
Computer Vision
Tabu Search for Frequency Assignment in Mobile Radio Networks
Journal of Heuristics
WISE Design of Indoor Wireless Systems: Practical Computation and Optimization
IEEE Computational Science & Engineering
Study of Genetic Search for the Frequency Assignment Problem
AE '95 Selected Papers from the European conference on Artificial Evolution
Optimal location of transmitters for micro-cellular radio communication system design
IEEE Journal on Selected Areas in Communications
ENCON: an evolutionary algorithm for the antenna placement problem
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
A comparison of randomized and evolutionary approaches for optimizing base station site selection
Proceedings of the 2004 ACM symposium on Applied computing
On the optimality of facility location for wireless transmission infrastructure
Computers and Industrial Engineering
Comparison and evaluation of multiple objective genetic algorithms for the antenna placement problem
Mobile Networks and Applications
The infrastructure efficiency of cellular wireless networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Hierarchical parallel approach for GSM mobile network design
Journal of Parallel and Distributed Computing
Designing cellular networks using a parallel hybrid metaheuristic on the computational grid
Computer Communications
Going the last mile: a spatial decision support system for wireless broadband communications
Decision Support Systems
Automated antenna positioning algorithms for wireless fixed-access networks
Journal of Heuristics
Local search study of honeycomb clustering problem for cellular planning
International Journal of Mobile Network Design and Innovation
Optimisation models for GSM radio
International Journal of Mobile Network Design and Innovation
Cost-effective base station deployment approach based on artificial immune systems
Proceedings of the 3rd International Conference on Bio-Inspired Models of Network, Information and Computing Sytems
The infrastructure efficiency of cellular wireless networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Wireless heterogeneous transmitter placement using multiobjective variable-length genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Optimising mobile base station placement using an enhanced Multi-Objective Genetic Algorithm
International Journal of Business Intelligence and Data Mining
Benchmarking a wide spectrum of metaheuristic techniques for the radio network design problem
IEEE Transactions on Evolutionary Computation
A multi-objective evolutionary approach for the antenna positioning problem
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Aware channel assignment algorithm for cognitive networks
CoRoNet '11 Proceedings of the 3rd ACM workshop on Cognitive radio networks
Base Station Placement for Dynamic Traffic Load Using Evolutionary Algorithms
Wireless Personal Communications: An International Journal
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The antenna-positioning problem concerns finding a set of sites for antennas from a set of pre-defined candidate sites, and for each selected site, to determine the number and types of antennas, as well as the associated values for each of the antenna parameters. All these choices must satisfy a set of imperative constraints and optimize a set of objectives. This paper presents a heuristic approach for tackling this complex and highly combinatorial problem. The proposed approach is composed of three phases: a constraint-based pre-processing phase to filter out bad configurations, an optimization phase using tabu search, and a post-optimization phase to improve solutions given by tabu search. To validate the approach, computational results are presented using large and realistic data sets.