A Decomposition Approach to a Stadium Antenna DeploymentProblem

We consider a stadium antenna deployment problem. The stadium is divided into sectors. Several antennas are assigned to each sector. Users should receive a signal of a certain quality from antennas assigned to their sector. The problem is to choose locations of antennas, their types, angles, and assignments to sectors to maximize three quality criteria: the mean signal to interference ratio (SIR), the number of clients with good signal quality, and the assignment consistency. We use a simulation to compute the signal quality. We present a three-stage heuristic approach to the problem. It uses a constructive heuristic, a local improvement procedure, and a decomposition-based MIP heuristic. We carry out numerical experiments on test instances with 94 antennas of 7 types, 19 sectors, and 4426 clients. It is possible to improve the provided baseline solutions in 2 h and obtain solutions comparable to running a metaheuristic package for 24 h.

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Publication Details

Journal
Journal of Applied and Industrial Mathematics
Published
2025-11-02
DOI
https://doi.org/10.1134/s1990478925010144
Primary Topic
Vehicle Routing Optimization Methods
Type
article
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article

A Decomposition Approach to a Stadium Antenna DeploymentProblem

Alexander Yuskov
Journal of Applied and Industrial Mathematics
Vehicle Routing Optimization Methods
article

A Decomposition Approach to a Stadium Antenna DeploymentProblem

Alexander Yuskov
article en

Abstract

We consider a stadium antenna deployment problem. The stadium is divided into sectors. Several antennas are assigned to each sector. Users should receive a signal of a certain quality from antennas assigned to their sector. The problem is to choose locations of antennas, their types, angles, and assignments to sectors to maximize three quality criteria: the mean signal to interference ratio (SIR), the number of clients with good signal quality, and the assignment consistency. We use a simulation to compute the signal quality. We present a three-stage heuristic approach to the problem. It uses a constructive heuristic, a local improvement procedure, and a decomposition-based MIP heuristic. We carry out numerical experiments on test instances with 94 antennas of 7 types, 19 sectors, and 4426 clients. It is possible to improve the provided baseline solutions in 2 h and obtain solutions comparable to running a metaheuristic package for 24 h.

Journal of Applied and Industrial MathematicsVol. 19(1)
Novosibirsk State University (RU)
Openalex Percentile: Top 55%
Vehicle Routing Optimization Methods
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