Power optimization of LTE-800 and coexistence with DVB-T services

Denkovska, Marija; Denkovski, Daniel; Atanasovski, Vladimir; Gavrilovska, Liljana

doi:10.1016/j.phycom.2018.04.009

Cited by 1 publication

(1 citation statement)

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“…Neglecting such uncertainty may lead to critical issues, such as infeasibility and suboptimality of design solutions, due to unexpected deviations of the input data representing signal propagation. We remark that, while the deterministic optimal design (i.e., not including the modelling of uncertainty) of SFN networks has received substantial attention (e.g., [3], [12], [14], [19], [29], [30], [32], [34]), the robust design based on using optimization under uncertainty techniques has received less coverage. In this work, we thus propose a new optimization under data uncertainty approach based on min-max regret.…”

Section: Network (Sfnsmentioning

confidence: 99%

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

D’Andreagiovanni

Lakhlef

Nardin

2022

2022 IEEE International Smart Cities Conference (ISC2)

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Notwithstanding the introduction of brand new 5Gbased 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 Optimizationlike 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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