Evolutionary Iterated Local Search meta‐heuristic for the antenna positioning problem in cellular networks
Radio network planning is a core problem in cellular networks. It includes coverage, capacity and parameter planning. This paper investigates the Antenna Positioning Problem (APP) which is a main task in cellular networks planning. The aim is to find a trade‐off between maximizing coverage and minim...
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| Veröffentlicht in: | Computational intelligence 2022-06, Vol.38 (3), p.1183-1214 |
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| container_title | Computational intelligence |
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| creator | Benmezal, Larbi Benhamou, Belaid Boughaci, Dalila |
| description | Radio network planning is a core problem in cellular networks. It includes coverage, capacity and parameter planning. This paper investigates the Antenna Positioning Problem (APP) which is a main task in cellular networks planning. The aim is to find a trade‐off between maximizing coverage and minimizing costs. APP is the task of selecting a subset of potential locations where installing the base stations to cover the entire area. In theory, the APP is NP‐hard. To solve it in practice, we propose a new meta‐heuristic called Evolutionary Iterated Local Search that merges the local search method and some evolutionary operations of crossover and mutation. The proposed method is implemented and evaluated on realistic, synthetic and random instances of the problem of different sizes. The numerical results and the comparison with the state‐of‐the‐art show that the proposed method succeeds in finding good results for the considered problem. |
| doi_str_mv | 10.1111/coin.12454 |
| format | Article |
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It includes coverage, capacity and parameter planning. This paper investigates the Antenna Positioning Problem (APP) which is a main task in cellular networks planning. The aim is to find a trade‐off between maximizing coverage and minimizing costs. APP is the task of selecting a subset of potential locations where installing the base stations to cover the entire area. In theory, the APP is NP‐hard. To solve it in practice, we propose a new meta‐heuristic called Evolutionary Iterated Local Search that merges the local search method and some evolutionary operations of crossover and mutation. The proposed method is implemented and evaluated on realistic, synthetic and random instances of the problem of different sizes. 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| ispartof | Computational intelligence, 2022-06, Vol.38 (3), p.1183-1214 |
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| source | EBSCOhost Business Source Complete; Access via Wiley Online Library |
| subjects | Antenna Positioning Problem Antennas Artificial Intelligence Cellular communication Cellular radio Computer Science Evolution evolutionary meta‐heuristic Heuristic local search Mutation network design Networks optimization Search methods |
| title | Evolutionary Iterated Local Search meta‐heuristic for the antenna positioning problem in cellular networks |
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