The optimal and distributed provisioning of high throughput in mesh networks is known as a fundamental but hard problem. The situation is exacerbated in a wireless setting due to the interference among local wireless transmissions. In this paper, we propose a cross-layer optimization framework for throughput maximization in wireless mesh networks, in which the data routing problem and the wireless medium contention problem are jointly optimized for multihop multicast. We show that the throughput maximization problem can be decomposed into two subproblems: a data routing subproblem at the network layer, and a power control subproblem at the physical layer with a set of Lagrangian dual variables coordinating interlayer coupling. Various effective solutions are discussed for each subproblem. We emphasize the network coding technique for multicast routing and a game theoretic method for interference management, for which efficient and distributed solutions are derived and illustrated. Finally, we show that the proposed framework can be extended to take into account physical-layer wireless multicast in mesh networks.

The competition among wireless data service providers brings in an option for the customers to switch their providers, due to unsatisfactory service or otherwise. However, the existing resource management algorithms for wireless networks fail to fully capture the far-reaching impact of this competitiveness. From this perspective, we propose an integrated admission and rate control (ARC) framework for CDMA based wireless data networks. The admission control is at the session (macro) level while the rate control is at the link layer packet (micro) level. The ARC framework is based on a novel game theoretic formulation which defines non-cooperative games between the service providers and the customers. A user’s decision to leave or join a provider is based on a finite set of strategies. A service provider can also construct its game strategy set so as to maximize the utility (revenue) yet attaining

We continue the study initiated in [Ro01] on Stackelberg Scheduling Strategies. We are given a set of ¡ independent parallel machines or equivalently a set of ¡ parallel edges on which certain flow has to be sent. Each edge ¢ is endowed with a latency function £¥¤§¦© ¨ �. The setting is that of a non-cooperative game: players choose edges so as minimize their individual latencies. Additionally, there is a single player who control as fraction � of the total flow. The goal is to find a strategy for the leader (i.e. an assignment of flow to indivual links) such that the selfish users react so as to minimize the total latency of the system. Building on the recent results in [Ro01, RT00], we show the following: 1. We devise a fully polynomial approximate Stackelberg scheme: given a performance ¦©������ � requirement, the stackelberg scheme runs in time polynomial ¡ in and and produces an assignment of flows such that the cost of the induced Nash equilibrium is within a ����� factor of the optimum stackelberg �§ � strategy. The result is extended to obtain a polynomial-approximation scheme when instances are restricted to layered directed graphs in which each layer has a bounded number of vertices. 2. We then consider a two round Stackelberg strategy (denoted 2SS). In this strategy, the game consists of three rounds: a move by the leader followed by the moves of all the followers folowed again by a move by the leader who possibly reassigns some of the flows. We show that 2SS always dominates the one round scheme, and for some classes of latency functions, is guaranteed to be closer to the global social optimum. We also consider the variant where the leader plays after the selfish users have routed themselves, and observe that this dominates the one-round scheme. Extensions of the results to the special case when all the latency functions are linear are also presented. Our results extend the earlier results and answer an open question posed by Roughgarden [Ro01].

A game-theoretic model is proposed to study the cross-layer problem of joint power and rate control with quality of service (QoS) constraints in multiple-access networks. In the proposed game, each user seeks to choose its transmit power and rate in a distributed and selfish manner in order to maximize its own utility and at the same time satisfy its QoS requirements. The user’s QoS constraints are specified in terms of the average source rate and an upper bound on the average delay where the delay includes both transmission and queueing delays.. The utility function considered here measures the number of reliable bits transmitted per Joule of energy consumed and is particularly suitable for wireless networks in which energy efficiency is important. The Nash equilibrium solution for the proposed non-cooperative game is derived and a closed-form expression for the utility achieved at equilibrium is obtained. It is shown that the QoS requirements of a user translate into a “size ” for the user which is an indication of the amount of network resources consumed by the user. Using this framework, the tradeoffs among throughput, delay, network capacity and energy efficiency are also studied. In addition, we give analytical expressions for users ’ delay profiles and quantify the delay performance of the users at Nash equilibrium.

Abstract — We consider a distributed power control scheme in a Spread Spectrum (SS) wireless ad hoc network, in which each user announces a price that reflects his current interference level. Given these prices, we present an asynchronous distributed algorithm for updating power levels, and provide conditions under which this algorithm converges to an optimal power allocation. We relate this algorithm to myopic best response updates of a fictitious game, and characterize the algorithm’s convergence using supermodular game theory. I.

Abstract — Distributed opportunistic scheduling is studied for wireless ad-hoc networks, where many links contend for one channel using random access. In such networks, distributed opportunistic scheduling (DOS) involves a process of joint channel probing and distributed scheduling. It has been shown that under perfect channel estimation, the optimal DOS for maximizing the network throughput is a pure threshold policy. In this paper, this formalism is generalized to explore DOS under noisy channel estimation, where the transmission rate needs to be backed off from the estimated rate to reduce the outage. It is shown that the optimal scheduling policy remains to be threshold-based, and that the rate threshold turns out to be a function of the variance of the estimation error and be a functional of the backoff rate function. Since the optimal backoff rate is intractable, a suboptimal linear backoff scheme that backs off the estimated signal-to-noise ratio (SNR) and hence the rate is proposed. The corresponding optimal backoff ratio and rate threshold can be obtained via an iterative algorithm. Finally, simulation results are provided to illustrate the tradeoff caused by increasing training time to improve channel estimation at the cost of probing efficiency. I.

Abstract—In this paper we consider the problem of maximizing the number of supported connections in arbitrary wireless networks where a transmission is supported if and only if the signal-to-interference-plus-noise ratio at the receiver is greater than some threshold. The aim is to choose transmission powers for each connection so as to maximize the number of connections for which this threshold is met. We believe that analyzing this problem is important both in its own right and also because it arises as a subproblem in many other areas of wireless networking. We study both the complexity of the problem and also present some game theoretic results regarding capacity that is achieved by completely distributed algorithms. We also feel that this problem is intriguing since it involves both continuous aspects (i.e. choosing the transmission powers) as well as discrete aspects (i.e. which connections should be supported).

We adopt a game theoretic approach for the design and analysis of distributed resource allocation algorithms in fading multiple access channels. The users are assumed to be selfish, rational, and limited by average power constraints. We show that the sum-rate optimal point on the boundary of the multiple-access channel capacity region is the unique Nash Equilibrium of the corresponding water-filling game. This result sheds a new light on the opportunistic communication principle and argues for the fairness of the sum-rate optimal point, at least from a game theoretic perspective. The base-station is then introduced as a player interested in maximizing a weighted sum of the individual rates. We propose a Stackelberg formulation in which the base-station is the designated game leader. In this set-up, the base-station announces first its strategy defined as the decoding order of the different users, in the successive cancellation receiver, as a function of the channel state. In the second stage, the users compete conditioned on this particular decoding strategy. We show that this formulation allows for achieving all the corner points of the capacity region, in addition to the sum-rate optimal point. On the negative side, we prove the non-existence of a base-station strategy in this formulation that achieves the rest of the boundary points. To overcome this limitation, we present a repeated game approach which achieves the capacity region of the fading multiple access channel. Finally, we extend our study to vector channels highlighting interesting differences between this scenario and the scalar channel case. 1

In this two-parts paper we propose a decentralized strategy, based on a game-theoretic formulation, to find out the optimal precoding/multiplexing matrices for a multipoint-to-multipoint communication system composed of a set of wideband links sharing the same physical resources, i.e., time and bandwidth. We assume, as optimality criterion, the achievement of a Nash equilibrium and consider two alternative optimization problems: 1) the competitive maximization of mutual information on each link, given constraints on the transmit power and on the spectral mask imposed by the radio spectrum regulatory bodies; and 2) the competitive maximization of the transmission rate, using finite order constellations, under the same constraints as above, plus a constraint on the average error probability. In Part I of the paper, we start by showing that the solution set of both noncooperative games is always nonempty and contains only pure strategies. Then, we prove that the optimal precoding/multiplexing scheme for both games leads to a channel diagonalizing structure, so that both matrix-valued problems can be recast in a simpler unified vector power control game, with no performance penalty. Thus, we study this simpler game and derive sufficient conditions ensuring the uniqueness of the Nash equilibrium. Interestingly, although derived under stronger constraints,

Recent publications recognize that decentralized algorithms useful in wireless data applications can be obtained via microeconomics and game theory. In these studies, each agent maximizes, under appropriate rules and constraints, a quality-of-service (QoS) index. A key solution concept is a "Nash equilibrium"; i.e., an allocation from which no agent is better off by unilaterally "deviating". The actual maximization may be made by software which may not be directly "controllable" by a human user. The model and, especially, the chosen QoS index should be as general as possible, so that the derived results be applicable to a wide variety of channel conditions, modulation schemes, and other physical-layer characteristics. Likewise, the chosen index should exhibit predictable and reliable technical behavior, without exacting a high complexity cost. This note describes a model, and particularly, a QoS index which can accommodate a wide variety of physical layer situations. The proposed index is shown to exhibit solid technical behavior, be physically significant, intuitively appealing, and applicable to a wide variety of physical layer situations. A game in which terminals carrying multi-rate traffic seek to maximize this index is analyzed, and closed-form equilibrium conditions and power levels are derived "from first principles". All terminals want the same signal-to-interference ratio (SIR), but some cannot reach the necessary power level. At equilibrium, a number of terminals transmit at full power, and others achieve the same optimal SIR. A basic rationale to search for these equilibria is provided.