| Title |
Green And Robust Optimal Design Of Single Frequency Networks By Min-Max Regret And Aco-Based Learning |
| ID_Doc |
28332 |
| Authors |
D'Andreagiovanni F.; Lakhlef H.; Nardin A. |
| Year |
2022 |
| Published |
ISC2 2022 - 8th IEEE International Smart Cities Conference |
| DOI |
http://dx.doi.org/10.1109/ISC255366.2022.9922401 |
| Abstract |
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. © 2022 IEEE. |
| Author Keywords |
DVB-T; Metaheuristics; Network Design; Robust Optimization; Single Frequency Networks |
