TY - JOUR

T1 - A Decomposition Approach to a Stadium Antenna Deployment Problem

AU - Yuskov, A. D.

N1 - Yuskov A.D. A Decomposition Approach to a Stadium Antenna Deployment Problem // Journal of Applied and Industrial Mathematics. – 2025. – Vol. 19. - No. 1. – P. 169-180. – DOI 10.1134/S1990478925010144. – EDN JWFKFY. This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

PY - 2025

Y1 - 2025

N2 - 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 qualityfrom antennas assigned to their sector. The problem is to choose locations of antennas, theirtypes, angles, and assignments to sectors to maximize three quality criteria: the mean signal tointerference ratio (SIR), the number of clients with good signal quality, and the assignmentconsistency. We use a simulation to compute the signal quality. We present a three-stage heuristicapproach to the problem. It uses a constructive heuristic, a local improvement procedure, anda decomposition-based MIP heuristic. We carry out numerical experiments on test instances with94 antennas of 7 types, 19 sectors, and 4426 clients. It is possible to improve the provided baselinesolutions in 2 h and obtain solutions comparable to running a metaheuristic package for 24 h.

AB - 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 qualityfrom antennas assigned to their sector. The problem is to choose locations of antennas, theirtypes, angles, and assignments to sectors to maximize three quality criteria: the mean signal tointerference ratio (SIR), the number of clients with good signal quality, and the assignmentconsistency. We use a simulation to compute the signal quality. We present a three-stage heuristicapproach to the problem. It uses a constructive heuristic, a local improvement procedure, anda decomposition-based MIP heuristic. We carry out numerical experiments on test instances with94 antennas of 7 types, 19 sectors, and 4426 clients. It is possible to improve the provided baselinesolutions in 2 h and obtain solutions comparable to running a metaheuristic package for 24 h.

KW - SINR

KW - black box optimization

KW - matheuristics

KW - signal quality

KW - wireless network

KW - ОПТИМИЗАЦИЯ «ЧЁРНОГО ЯЩИКА»

KW - МЕТАЭВРИСТИКА

KW - БЕСПРОВОДНАЯ СЕТЬ

KW - КАЧЕСТВО СИГНАЛА

KW - SINR

UR - https://www.scopus.com/pages/publications/105020673560

UR - https://www.elibrary.ru/item.asp?id=83155459

UR - https://www.elibrary.ru/item.asp?id=82904679

UR - https://www.mendeley.com/catalogue/2021bf6a-6f6d-34a2-a032-5fab968c4034/

U2 - 10.1134/S1990478925010144

DO - 10.1134/S1990478925010144

M3 - Article

VL - 19

SP - 169

EP - 180

JO - Journal of Applied and Industrial Mathematics

JF - Journal of Applied and Industrial Mathematics

SN - 1990-4789

IS - 1

M1 - 14

ER -