Pierre Coucheney

Fair and Efficient User-Network Association Algorithm for Multi-Technology Wireless Networks

Recent mobile equipment (as well as the norm IEEE 802.21) offers the possibility for users to switch from one technology to another (vertical handover). This allows flexibility in resource assignments and, consequently, increases the potential throughput allocated to each user. In this paper, we design a fully distributed algorithm based on trial and error mechanisms that exploits the benefits of vertical handover by finding fair and efficient assignment schemes. On the one hand, mobiles gradually update the fraction of data packets they send to each network based on the rewards they receive from the stations. On the other hand, network stations send rewards to each mobile that represent the impact each mobile has on the cell throughput. This reward function is closely related to the concept of marginal cost in the pricing literature. Both the station and the mobile algorithms are simple enough to be implemented in current standard equipment. Based on tools from evolutionary games, potential games and replicator dynamics, we analytically show the convergence of the algorithm to fair and efficient solutions. Moreover, we show that after convergence, each user is connected to a single network cell which avoids costly repeated vertical handovers. To achieve fast convergence, several simple heuristics based on this algorithm are proposed and tested. Indeed, for implementation purposes, the number of iterations should remain in the order of a few tens.

Impact of Competition Between ISPs on the Net Neutrality Debate

Network neutrality is the topic of a vivid and very sensitive debate, in both the telecommunication and political worlds, because of its potential impact in everyday life. That debate has been raised by Internet Service Providers (ISPs), complaining that content providers (CPs) congest the network with insufficient monetary compensation, and threatening to impose side payments to CPs in order to support their infrastructure costs. While there have been many studies discussing the advantages and drawbacks of neutrality, there is no game-theoretical work dealing with the observable situation of competitive ISPs in front of a (quasi-)monopolistic CP. Though, this is a typical situation that is condemned by ISPs, and, according to them, another reason of the non-neutrality need. We develop and analyze here a model describing the relations between two competitive ISPs and a single CP, played as a three-level game corresponding to three different time scales. At the largest time scale, side payments (if any) are determined. At a smaller time scale, ISPs decide their (flat-rate) subscription fee (toward users), then the CP chooses the (flat-rate) price to charge users. Users finally select their ISP (if any) using a price-based discrete choice model, and decide whether to also subscribe to the CP service. The game is analyzed by backward induction. As a conclusion, we obtain among other things that non-neutrality may be beneficial to the CP, and not necessarily to ISPs, unless the side payments are decided by ISPs.

Self-optimizing routing in MANETs with multi-class flows

In this paper we show how game theory and Gibbs sampling techniques can be used to design a self-optimizing algorithm for minimizing end-to-end delays for all flows in a multi-class mobile ad hoc network (MANET). This is an improvement over the famed Ad-Hoc On-demand Distance Vector (AODV) protocol, that computes the routes with minimal number of hops for each flow in a multi-flow ad-hoc network. Here, the load of each flow is taken into account to choose the best route (in terms of delays) among a fixed number of routes. The algorithm can be implemented in a fully distributed and asynchronous way and is guaranteed to converge to the global optimal configuration. Numerous numerical experiments show that the gain over AODV, computed over a large number of networks, is quite substantial.

Selection of efficient pure strategies in allocation games

In this work we consider allocation games and we investigate the following question: under what conditions does the replicator dynamics select a pure strategy?

Distributed Optimization in Multi-User MIMO Systems with Imperfect and Delayed Information

In this paper, we analyze the problem of signal covariance optimization in Gaussian multiple-input, multiple-output (MIMO) channels under imperfect (and possibly delayed) channel state information. Starting from the continuous-time dynamics of matrix exponential learning, we develop a distributed optimization algorithm driven by a damping term which ensures the methods stability under stochastic perturbations and asynchronicities of arbitrary magnitude. As opposed to traditional water-filling methods, the algorithms convergence properties (speed and accuracy) can be controlled by tuning the users learning rate and/or the damping parameter. Accordingly, the algorithm converges arbitrarily close to an optimum signal covariance profile within a few iterations, even for large numbers of users and/or antennas per user; furthermore, the quality of the solution obtained remains robust in the presence of imperfect (or delayed) measurements and asynchronous user updates.

Mobile association problem in heterogenous wireless networks with mobility

In this paper, we deal with a dynamic and stochastic admission control and mobile association problem in an heterogeneous wireless network. We extend the usual problem by adding mobility features described by a Markov Modulated Poisson Process. The aim is to optimize the average performance of the system. This dynamic control problem is modeled and solved using a Semi Markov Decision Process (SMDP) framework. We then assess the impact of the mobility and show that (i) our network centric approach outperforms a simple user centric algorithm and (ii) mobility improves the performance of the system when optimal policy of the problem is used.

Influence of Search Neutrality on the Economics of Advertisement-Financed Content

The search neutrality debate questions the ranking methods of search engines. We analyze the issue when content providers offer content for free, but get revenues from advertising. We investigate the noncooperative game among competing content providers under different ranking policies. When the search engine is not involved with high-quality content providers, it should adopt neutral ranking, also maximizing user quality-of-experience. If the search engine controls high-quality content, favoring its ranking and adding advertisement yield a larger revenue. Though user perceived quality may not be impaired, the advertising revenues of the other content providers drastically decrease.

Comparison of search engines non-neutral and neutral behaviors

Network neutrality has recently attracted a lot of attention but search neutrality is also becoming a vivid subject of discussion because a non-neutral search may prevent some relevant content from being accessed by users. We propose in this paper to model two situations of a non-neutral search engine behavior, which can rank the link propositions according to the profit a search can generate for it instead of just relevance: the case when the search engine owns some content, and the case when it imposes a tax on organic links, a bit similarly to what it does for commercial links. We analyze the particular (and deterministic) situation of a single keyword, and describe the problem for the whole potential set of keywords.

Myopic versus clairvoyant admission policies in wireless networks

In this paper, we consider a dynamic scenario in which mobile users with elastic traffic arrive at a wireless heterogeneous system according to a Poisson arrival process. The wireless system consists of a set of overlapping network cells of different technologies. The protocols associated to each cell specify the throughput allocated to each user, given the cell load (i.e. the number of active users and their geographical positions). Meanwhile, mobile users (the players) can choose which cell to associate to. Suppose they collaborate to obtain an efficient and fair share of the global throughput. Then, two families of mechanisms can be considered: (i) either the users bargain at each stage of the system so as to obtain, given the present population, a fair share of the present throughput (repeated single-stage games), or (ii) users bargain so as to obtain a fair expected throughput during the total duration of their call (single stochastic game), taking into account the knowledge of the global (stochastic) arrival process and connection size. In this paper, we numerically compare the two policies and their resulting performance: does forecasting the future significantly impact the optimal mobile-to-cell association?

A game theory-based analysis of search engine non-neutral behavior

In recent years, there has been a rising concern about the policy of major search engines, and more specifically about their ranking in so-called organic results corresponding to keywords searches. The associated proposition is that their behavior should be regulated. The concern comes from search bias, which refers to search rankings based on some principle different from the expected automated relevance. In this paper, we analyze one behavior that results in search bias: the payment by content providers to the search engine in order to improve the chances to be located and accessed by a search engine user. A simple game theory-based model is presented where both a search engine and a content provider interact strategically, and the aggregated behavior of users is modeled by a demand function. The utility of each stakeholder when the search engine is engaged in such a non-neutral behavior is compared with the neutral case when no such side payment is present.