WCDMA for UMTS: Radio Access for Third Generation Mobile Communications
WCDMA for UMTS: Radio Access for Third Generation Mobile Communications
An efficient heuristic for the expansion problem of cellular wireless networks
Computers and Operations Research
Planning UMTS base station location: optimization models with power control and algorithms
IEEE Transactions on Wireless Communications
Planning reliable UMTS terrestrial access networks
IEEE Communications Magazine
A tabu search algorithm for the global planning problem of third generation mobile networks
Computers and Electrical Engineering
Global planning of 3G networks using simulated annealing
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
A tabu search approach for assigning node Bs to switches in UMTS networks
IEEE Transactions on Wireless Communications
Comparison of different meta-heuristics to solve the global planning problem of UMTS networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Efficient solution of the 3G network planning problem
Computers and Industrial Engineering
Automatic planning of 3G UMTS all-IP release 4 networks with realistic traffic
Computers and Operations Research
International Journal of Autonomous and Adaptive Communications Systems
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In this paper, we first propose a global approach for planning universal mobile telecommunications system (UMTS) networks in the uplink direction. Instead of partitioning the planning problem into several subproblems and solving them successively (sequential approach), we propose a mathematical programming model that addresses it as a whole. This global approach has the advantage of providing better results since, in general, optimal solutions to all subproblems do not provide an optimal solution to the global problem. In order to prove our point, we present a detailed example that compares the global and the sequential approaches. Next, we propose a local search heuristic to find "good" feasible solutions of the global model within a reasonable amount of time. Finally, numerical results for a set of randomly generated problems are presented. The results show that the heuristic produces solutions that are, on average, at 6.53% of the optimal solution, and in the worst case at 31.31% of the optimal solution.