Electrical Vehicles in the Smart Grid: A Mean Field Game Analysis

In this article, we investigate the competitive interaction between electrical vehicles or hybrid oil-electricity vehicles in a Cournot market consisting of electricity transactions to or from an underlying electricity distribution network. We provide a mean field game formulation for this competition, and introduce the set of fundamental differential equations ruling the behavior of the vehicles at the feedback Nash equilibrium, referred here to as the mean field equilibrium. This framework allows for a consistent analysis of the evolution of the price of electricity as well as of the instantaneous electricity demand in the power grid. Simulations precisely quantify those parameters and suggest that significant reduction of the daily electricity peak demand can be achieved by appropriate electricity pricing.Comment: submitted to IEEE Journal on Selected Areas in Communications: Smart Grid Communications Serie

Opportunistic Interference Alignment in MIMO Interference Channels

We present two interference alignment techniques such that an opportunistic point-to-point multiple input multiple output (MIMO) link can reuse, without generating any additional interference, the same frequency band of a similar pre-existing primary link. In this scenario, we exploit the fact that under power constraints, although each radio maximizes independently its rate by water-filling on their channel transfer matrix singular values, frequently, not all of them are used. Therefore, by aligning the interference of the opportunistic radio it is possible to transmit at a significant rate while insuring zero-interference on the pre-existing link. We propose a linear pre-coder for a perfect interference alignment and a power allocation scheme which maximizes the individual data rate of the secondary link. Our numerical results show that significant data rates are achieved even for a reduced number of antennas.Comment: To appear in proc. IEEE PIMRC 2008 - Workshop in Emerging Network Perspectives in Multiuser and Cooperative MIMO (NWMIMO). 5 pages and 4 figure

Game Theory and Femtocell Communications: Making Network Deployment Feasible

9781466600928Femtocell is currently the most promising technology for supporting the increasing demand of data traffic in wireless networks. Femtocells provide an opportunity for enabling innovative mobile applications and services in home and office environments. Femtocell Communications and Technologies: Business Opportunities and Deployment Challenges is an extensive and thoroughly revised version of a collection of review and research based chapters on femtocell technology. This work focuses on mobility and security in femtocell, cognitive femtocell, and standardization and deployment scenarios. Several crucial topics addressed in this book are interference mitigation techniques, network integration option, cognitive optimization, and economic incentives to install femtocells that may have a larger impact on their ultimate success. The book is optimized for use by graduate researchers who are familiar with the fundamentals of wireless communication and cellular concepts

Equilibria of Channel Selection Games in Parallel Multiple Access Channels

International audienceIn this paper, the parallel multiple access channel (MAC) is studied under the assumption that transmitters maximize their individual spectral efficiency by selfishly tuning their power allocation policy. Two particular scenarios are studied: (a) transmitters are allowed to use all the available channels; and (b) transmitters are constrained to use a single channel. Both scenarios are modeled by one-shot games and the corresponding sets of Nash equilibria (NE) are fully characterized under the assumption that the receiver treads the multiple access interference as noise. In both cases, the set of NE is non-empty. In the case in which transmitters use a single channel, an upper bound of the cardinality of the NE set is provided in terms of the number of transmitters and number of channels. In particular, it is shown that in fully loaded networks, the sum spectral efficiency at the NE in scenario (a) is at most equal to the sum spectral efficiency at the NE in scenario (b). A formal proof of this observation, known in general as a Braess Paradox, is provided in the case of 2 transmitters and 2 channels. In general scenarios, we conjecture that the same effect holds as long as the network is kept fully loaded, as shown by numerical examples. Moreover, the price of anarchy and the price of stability in both games is also studied. Interestingly, under certain conditions on the channel gains, Pareto optimality can be achieved at some NE if and only if the number of channels equals or exceeds the number of transmitters. Finally, simulations are presented to verify the theoretical results

A Game Theoretic Framework for Decentralized Power Allocation in IDMA Systems

In this contribution we present a decentralized power allocation algorithm for the uplink interleave division multiple access (IDMA) channel. Within the proposed optimal strategy for power allocation, each user aims at selfishly maximizing its own utility function. An iterative chip by chip (CBC) decoder at the receiver and a rational selfish behavior of all the users according to a classical game-theoretical framework are the underlying assumptions of this work. This approach leads to a power allocation based on a channel inversion policy where the optimal power level is set locally at each terminal based on the knowledge of its own channel realization, the noise level at the receiver and the number of active users in the network.Comment: To appear in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2008

Achieving Pareto Optimal Equilibria in Energy Efficient Clustered Ad Hoc Networks

International audienceIn this paper, a decentralized iterative algorithm able to achieve a Pareto optimal working point in a clustered ad hoc network is analysed. Here, radio devices are assumed to operate above a minimal signal to interference plus noise ratio (SINR) threshold while minimizing the global power consumption. A distributed algorithm, namely the optimal dynamic learning (ODL), is presented and shown to be able to dynamically steer the network to an efficient working point, by exploiting only minimal amount of information. This algorithm aims at implementing a Pareto optimal solution for a large proportion of the time, with high probability. Conversely, existing solutions aim at achieving individually optimal solutions (Nash equilibria), which might be globally inefficient. The gain is shown to be larger when the amount of available radio resource is scarce. Sufficient analytical conditions for ODL to converge to the desired working point are provided, moreover through numerical simulations the ability of the algorithm to configure an interference limited network is shown. The performance of ODL and those of a Nash equilibrium reaching algorithm are numerically compared, and their performance as a function of available resources studied

Distributed Power Allocation with SINR Constraints Using Trial and Error Learning

978-1-4673-0436-8International audienceIn this paper, we address the problem of global transmit power minimization in a self-configuring network where radio devices are subject to operate at a minimum signal to interference plus noise ratio (SINR) level. We model the network as a parallel Gaussian interference channel and we introduce a fully decentralized algorithm (based on trial and error) able to statistically achieve a configuration where the performance demands are met. Contrary to existing solutions, our algorithm requires only local information and can learn stable and efficient working points by using only one bit feedback. We model the network under two different game theoretical frameworks: normal form and satisfaction form. We show that the converging points correspond to equilibrium points, namely Nash and satisfaction equilibrium. Similarly, we provide sufficient conditions for the algorithm to converge in both formulations. Moreover, we provide analytical results to estimate the algorithm's performance, as a function of the network parameters. Finally, numerical results are provided to validate our theoretical conclusions

Learning Coarse Correlated Equilibria in Two-Tier Wireless Networks

978-1-4577-2052-9International audienceIn this paper, we study the strategic coexistence between macro and femto cell tiers from a game theoretic learning perspective. A novel regret-based learning algorithm is proposed whereby cognitive femtocells mitigate their interference toward the macrocell tier, on the downlink. The proposed algorithm is fully decentralized relying only on the signal-to-interferenceplus-noise ratio (SINR) feedback to the corresponding femtocell base stations. Based on these local observations, femto base stations learn the probability distribution of their transmission strategies (power levels and frequency band) by minimizing their regrets for using certain strategies, while adhering to the cross-tier interference constraint. The decentralized regret based learning algorithm is shown to converge to an ǫ-coarse correlated equilibrium (ǫ-CCE) which is a generalization of the classical Nash Equilibrium (NE). Finally, numerical results are shown to corroborate our findings where, quite remarkably, our learning algorithm achieves the same performance as the classical regret matching, but with substantially much less overhead

Opportunistic interference alignment with multiple antenna terminals

Nous considérons le cas de deux liaisons radio multi-entrée multi-sorties (MIMO) point-à-point ayant des priorités différentes et utilisant la même bande de fréquence. Les contraintes que nous nous imposons sont les suivantes: premièrement, la liaison primaire (haute priorité) ne tolère aucune interférence de la part de la liaison secondaire (faible priorité) et doit atteindre son débit maximal; deuxièmement, nous considérons que les émetteurs ne peuvent pas coopérer. Nous présentons une nouvelle technique d'alignement d'interférence non-coopérative qui permet à une liaison opportuniste MIMO point-à-point d'exister de manière transparente avec une liaison primaire. L'idée-clé proposée ici est de remarquer que même si la liaison primaire maximise son débit au sens de Shannon, la stratégie de "water-filling" correspondante, conduit l'émetteur primaire à laisser vacants certains modes singuliers qui peuvent être recyclés par une liaison secondaire. L'émetteur secondaire peut alors exploiter les degrés de liberté correspondant, en utilisant une matrice de précodage appropriée, telle que l'interference générée par la transmission secondaire n'affecte que les directions non-utilisées par la liaison primaire: cette stratégie est nommée alignement d'interférence opportuniste. Contrairement aux techniques de formation de voie, notre technique peut être utilisée même dans le cas où tous les términaux primaires et secondaires utilisent le même nombre d'antennes et les récepteur sont localisés au même endroit dans l'espace. Cet article présente une contribution double. D'une part,nous déterminons la matrice de précodage permettant d'effectuer l'alignement d'interférence opportuniste, ainsi que la méthode d'allocation de puissance correspondante et permettant de maximisant le débit de la liaison secondaire. D'autre part, en utilisant des outils de la théorie des matrices aléatoires et des probabilités libres, nous présentons une analyse asymptotique du débit de la liaison opportuniste lorsque tous les dispositifs sont équipés du même nombre d'antennes et que ce nombre tend vers l'infini. L'analyse montre que le débit converge vers une valeur non-aléatoire prédictible et indépendente de la réalisation instantanée des canaux. Enfin, nous montrons que pour une petite différence de nombre d'antennes entre l'émetteur et le récepteur et dans le régime de rapport signal à bruit (RSB) moyen (typiquement $inférieur à 5$dB), notre technique permet d'obtenir un débit plus élevé que celui des techniques de formation de voie