A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks

Abstract

Because of the introduction and spread of the second generation of the Digital Video Broadcasting—Terrestrial standard (DVB-T2), already active television broadcasters and new broadcasters that have entered in the market will be required to (re)design their networks. This is generating a new interest for effective and efficient DVB optimization software tools. In this work, we propose a strengthened binary linear programming model for representing the optimal DVB design problem, including power and scheduling configuration, and propose a new matheuristic for its solution. The matheuristic combines a genetic algorithm, adopted to efficiently explore the solution space of power emissions of DVB stations, with relaxation-guided variable fixing and exact large neighborhood searches formulated as integer linear programming (ILP) problems solved exactly. Computational tests on realistic instances show that the new matheuristic performs much better than a state-of-the-art optimization solver, identifying solutions associated with much higher user coverage.

A compact formulation for the base station deployment problem in wireless networks

“…Several approaches have been proposed to solve the WND problem, both exact MIP‐based [1, 2, 6, 7, 10, 12‐17, 20, 27] and heuristic [8, 11, 25, 26].…”

“…Some contributions investigate the aspect of numerical problems [7, 10‐15, 25] that arise in the natural formulations of WND due to a constraints matrix with coefficients that greatly vary in their order of magnitude. The numerical issues make the problem intractable for real‐world instances, which are typically large.…”

“…A constructive heuristic followed by an improving local search is proposed. In [13], a matheuristic is investigated, which combines a genetic algorithm exploiting a suitable linear relaxation of the problem and an integer linear programming heuristic, improving the solutions found with the relaxation.…”

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

“…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.…”

Green Design of Single Frequency Networks by Multiband Robustness and a Hybrid Metaheuristic

“…We remark that, while the deterministic (i.e., not considering data uncertainty) optimal design of wireless networks based on signal-to-interference ratios has received wide attention, the use of optimization under uncertainty techniques, such as Robust Optimization and Stochastic Programming, has received less attention and has especially considered the effects of traffic uncertainty This is also true for the case of DVB-T, in which optimization approaches have neglected data uncertainty (e.g., [10,11,16,18]). To the best of our knowledge, this is the first work that discusses the adaption of a robust optimization approach to DVB-T and presents a hybrid metaheuristic for the solution of the resulting complex problem.…”