Robust optimisation of green wireless LANs under rate uncertainty and user mobility

We present a robust optimisation approach to energy savings in wireless local area networks, that incorporates both link capacity fluctuations and user mobility under Bertsimas and Sim's robust optimization paradigm. Preliminary computational results are discussed

Planning Solar in Energy-managed Cellular Networks

There has been a lot of interest recently on the energy efficiency and environmental impact of wireless networks. Given that the base stations are the network elements that use most of this energy, much research has dealt with ways to reduce the energy used by the base stations by turning them off during periods of low load. In addition to this, installing a solar harvesting sys- tem composed of solar panels, batteries, charge con- trollers and inverters is another way to further reduce the network environmental impact and some research has been dealing with this for individual base stations. In this paper, we show that both techniques are tightly coupled. We propose a mathematical model that captures the synergy between solar installation over a network and the dynamic operation of energy-managed base stations. We study the interactions between the two methods for networks of hundreds of base stations and show that the order in which each method is intro- duced into the system does make a difference in terms of cost and performance. We also show that installing solar is not always the best solution even when the unit cost of the solar energy is smaller than the grid cost. We conclude that planning the solar installation and energy management of the base stations have to be done jointly

A data driven approach in less expensive robust transmitting coverage and power optimization

This paper aims the development of a new reduced-cost algorithm for a multi-objective robust transmitter placement under uncertainty. Toward this end, we propose a new hybrid Kriging/Grey Wolf Optimizer (GWO) approach combined with robust design optimization to estimate the set of Pareto frontier by searching robustness as well as accuracy (lower objective function) in a design space. We consider minimization of the energy power consumption for transmitting as well as maximization of signal coverage in a multi-objective robust optimization model. The reliability of the model to control signal overlap for multiple transmitting antennas is also provided. To smooth computational cost, the proposed method instead of evaluating all receiver test points in each optimization iteration approximates signal coverages using Kriging interpolation to obtain optimal transmitter positions. The results demonstrate the utility and the efficiency of the proposed method in rendering the robust optimal design and analyzing the sensitivity of the transmitter placement problem under practically less-expensive computational efforts (350% and 320% less than computational time elapsed using standalone GWO and NSGAII respectively)

Planification et gestion de réseaux cellulaires avec énergie solaire

RÉSUMÉ Ce mémoire vise à étudier deux stratégies pour réduire les gaz à e˙et de serre dans les réseaux de télécommunications cellulaires : l’installation de panneaux solaires sur les stations de base et l’allocation dynamique des usagers aux stations. Les panneaux solaires permettent le remplacement énergétique par une énergie verte, ce qui réduira les gaz à effet de serre alors que l’allocation dynamique permet de mettre en veille certaines stations à certains moments de la journée, ce qui réduit la consommation énergétique. L’objectif principal de ce mémoire est de déterminer les interactions qu’il peut y avoir entre l’utilisation de l’énergie solaire et la gestion des stations de base. Pour répondre à cet objectif, deux modèles de réseau avec alimentation hybride ont été développés. Le premier modèle optimise l’énergie dans le réseau en considérant que la charge des usagers est constante à travers les années. La fonction objectif à minimiser est la somme du coût de capital des équipements solaires et du coût d’énergie pour l’opération du réseau. L’étude de ce modèle porte principalement sur l’interaction entre l’installation du solaire et la mise en veille dynamique des stations de base. On conclut qu’il y a une interaction marquée entre l’utilisation de l’énergie solaire et la gestion dynamique avec mode veille des stations du réseau. Plus particulièrement, l’ordre dans lequel chacune des méthode est introduite dans le réseau va avoir une influence sur les performances et son coût optimal. Le deuxième modèle permet, entre autres, d’avoir une croissance du trafic de données au fil des années. Ce modèle sert à étudier l’ajout de l’équipement solaire dans un réseau où il faut aussi rajouter des stations de base. On conclut avec ce deuxième modèle qu’il est important de repousser le plus tard possible l’installation de nouvelles stations peu importe qu’il y ait du solaire ou non.----------ABSTRACT The research done in this master’s thesis has the goal to diminish greenhouse gases in cellular telecommunications networks. This is done by adding solar equipment to dynamic networks where base stations can be turned o˙. Two mathematical models that capture the synergy between solar installation over a network and the dynamic operation of energy-managed base stations are presented. The first model optimizes the energy management in a network where the users’ load is constant through the years. The objective to be minimized is the sum of the capital cost needed for the solar equipment plus the cost of energy obtained from the electric grid. This model gives us insights on the synergy between planning of solar equipment in the network and switching o˙ the base stations. Notably, it is shown that the order in which these technologies are introduced makes a significant difference to the optimized objective function cost. Thus, this model emphasizes the fact that there is a strong correlation between the solar installation and the management of the base stations. Another result is that the solar equipment is not installed on every base station, even when the cost of solar energy is smaller than the cost of the grid. The second model optimizes a network where traÿc grows every year. This means that the model has to install new base stations and decide where to install them. Furthermore, the base stations now have different levels of transmission power instead of just being on or o˙. Finally, the functionality to install di˙erent kinds of solar equipment with different sizings is added. This complexity makes this model a lot more complex to solve and smaller networks are thus used. It is concluded that planning the installation of base stations throughout the years is much more important to reduce the total cost than installing solar