5 research outputs found
Long-Term Energy Constraints and Power Control in Cognitive Radio Networks
When a long-term energy constraint is imposed to a transmitter, the average
energy-efficiency of a transmitter is, in general, not maximized by always
transmitting. In a cognitive radio context, this means that a secondary link
can re-exploit the non-used time-slots. In the case where the secondary link is
imposed to generate no interference on the primary link, a relevant issue is
therefore to know the fraction of time-slots available to the secondary
transmitter, depending on the system parameters. On the other hand, if the
secondary transmitter is modeled as a selfish and free player choosing its
power control policy to maximize its average energy-efficiency, resulting
primary and secondary signals are not necessarily orthogonal and studying the
corresponding Stackelberg game is relevant to know the outcome of this
interactive situation in terms of power control policies.Comment: DSP 2011: 17th International Conference on Digital Signal Processing,
July 2011, Corfu, Greec
Stochastic Differential Games and Energy-Efficient Power Control
One of the contributions of this work is to formulate the problem of
energy-efficient power control in multiple access channels (namely, channels
which comprise several transmitters and one receiver) as a stochastic
differential game. The players are the transmitters who adapt their power level
to the quality of their time-varying link with the receiver, their battery
level, and the strategy updates of the others. The proposed model not only
allows one to take into account long-term strategic interactions but also
long-term energy constraints. A simple sufficient condition for the existence
of a Nash equilibrium in this game is provided and shown to be verified in a
typical scenario. As the uniqueness and determination of equilibria are
difficult issues in general, especially when the number of players goes large,
we move to two special cases: the single player case which gives us some useful
insights of practical interest and allows one to make connections with the case
of large number of players. The latter case is treated with a mean-field game
approach for which reasonable sufficient conditions for convergence and
uniqueness are provided. Remarkably, this recent approach for large system
analysis shows how scalability can be dealt with in large games and only relies
on the individual state information assumption.Comment: The final publication is available at
http://www.springerlink.com/openurl.asp?genre=article\&id=doi:10.1007/s13235-012-0068-
Long-Term Energy Constraints and Power Control in Cognitive Radio Networks
DSP 2011: 17th International Conference on Digital Signal Processing, July 2011, Corfu, GreeceInternational audienceWhen a long-term energy constraint is imposed to a transmitter, the average energy-efficiency of a transmitter is, in general, not maximized by always transmitting. In a cognitive radio context, this means that a secondary link can re-exploit the non-used time-slots. In the case where the secondary link is imposed to generate no interference on the primary link, a relevant issue is therefore to know the fraction of time-slots available to the secondary transmitter, depending on the system parameters. On the other hand, if the secondary transmitter is modeled as a selfish and free player choosing its power control policy to maximize its average energy-efficiency, resulting primary and secondary signals are not necessarily orthogonal and studying the corresponding Stackelberg game is relevant to know the outcome of this interactive situation in terms of power control policies
Théorie des jeux et apprentissage pour les réseaux sans fil distribués
Dans cette thèse, nous étudions des réseaux sans fil dans lesquels les terminaux mobiles sont autonomes dans le choix de leurs configurations de communication. Cette autonomie de décision peut notamment concerner le choix de la technologie d'accès au réseau, le choix du point d'accès, la modulation du signal, les bandes de fréquences occupées, la puissance du signal émis, etc. Typiquement, ces choix de configuration sont réalisés dans le but de maximiser des métriques de performances propres à chaque terminal. Sous l'hypothèse que les terminaux prennent leurs décisions de manière rationnelle afin de maximiser leurs performances, la théorie des jeux s'applique naturellement pour modéliser les interactions entre les décisions des différents terminaux. Plus précisément, l'objectif principal de cette thèse est d'étudier des stratégies d'équilibre de contrôle de puissance d'émission afin de satisfaire des considérations d'efficacité énergétique. Le cadre des jeux stochastiques est particulièrement adapté à ce problème et nous permet notamment de caractériser la région de performance atteignable pour toutes les stratégies de contrôle de puissance qui mènent à un état d'équilibre. Lorsque le nombre de terminaux en jeu est grand, nous faisons appel à la théorie des jeux à champ moyen pour simplifier l'étude du système. Cette théorie nous permet d'étudier non pas les interactions individuelles entre les terminaux, mais l'interaction de chaque terminal avec un champ moyen qui représente l'état global des autres terminaux. Des stratégies de contrôle de puissance optimales du jeu à champ moyen sont étudiées. Une autre partie de la thèse a été consacrée à des problématiques d'apprentissage de points d'équilibre dans les réseaux distribués. En particulier, après avoir caractérisé les positions d'équilibre d'un jeu de positionnement de points d'accès, nous montrons comment des dynamiques de meilleures réponses et d'apprentissage permettent de converger vers un équilibre. Enfin, pour un jeu de contrôle de puissance, la convergence des dynamiques de meilleures réponses vers des points d'équilibre a été étudiée. Il est notamment proposé un algorithme d'adaptation de puissance convergeant vers un équilibre avec une faible connaissance du réseau.In this thesis, we study wireless networks in which mobile terminals are free to choose their communication configuration. Theses configuration choices include access wireless technology, access point association, coding-modulation scheme, occupied bandwidth, power allocation, etc. Typically, these configuration choices are made to maximize some performance metrics associated to every terminals. Under the assumption that mobile terminals take their decisions in a rational manner, game theory can be applied to model the interactions between the terminals. Precisely, the main objective of this thesis is to study energy-efficient power control policies from which no terminal has an interest to deviate. The framework of stochastic games is particularly suited to this problem and allows to characterize the achievable utility region for equilibrium power control strategies. When the number of terminals in the network is large, we invoke mean field game theory to simplify the study of the system. Indeed, in a mean field game, the interactions between a player and all the other players are not considered individually. Instead, one only studies the interactions between each player and a mean field, which is the distribution of the states of all the other players. Optimal power control strategies from the mean field formulation are studied. Another part of this thesis has been focused on learning equilibria in distributed games. In particular, we show how best response dynamics and learning algorithms can converge to an equilibrium in a base station location game. For another scenario, namely a power control problem, we study the convergence of the best response dynamics. In this case, we propose a power control behavioral rule that converges to an equilibrium with very little information about the network.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF