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Communication Dans Un Congrès Année : 2022

Green and robust optimal design of Single Frequency Networks by min-max regret and ACO-based learning

Résumé

Notwithstanding the introduction of brand new 5G-based wireless services, single frequency networks supporting digital television and radio broadcasting still represent a major source of telecommunications services in modern smart cities. In this work, we propose a robust optimization model for the green design of second generation single frequency networks based on the digital television DVB-T standard, whose ongoing adoption requires to reconfigure and redesign existing networks. Our robust model aims at protecting design solutions against the data uncertainty that naturally affect propagation of signals in a real environment. For reducing conservatism of solutions, we refer to a heuristic min-max regret paradigm and to solve the resulting problem we propose to adopt a hybrid exact-heuristic algorithm based on the combination of an Ant Colony Optimization-like learning procedure, exploiting tight formulations of the optimization model, with an exact large neighborhood search. Results of computational tests considering realistic instances show that the heuristic min-max regret approach can produce solutions characterized by a substantially lower price of robustness without sacrificing protection against data uncertainty.
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Dates et versions

hal-03866765 , version 1 (22-11-2022)

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Citer

Fabio D’andreagiovanni, Hicham Lakhlef, Antonella Nardin. Green and robust optimal design of Single Frequency Networks by min-max regret and ACO-based learning. IEEE International Smart Cities Conference (ISC2 2022), Sep 2022, Paphos, Greece. pp.1-7, ⟨10.1109/ISC255366.2022.9922401⟩. ⟨hal-03866765⟩
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