Abstract—We consider a distributed power control scheme for wireless ad hoc networks, in which each user announces a price that reflects compensation paid by other users for their interference. We present an asynchronous distributed algorithm for updating power levels and prices. By relating this algorithm to myopic best response updates in a fictitious game, we are able to characterize convergence using supermodular game theory. Extensions of this algorithm to a multichannel network are also presented, in which users can allocate their power across multiple frequency bands. Index Terms—Distributed algorithms, game theory, power control, pricing. I.

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In this paper, we address the problem of crosslayer denial of service in wireless data networks. We introduce SPREAD- a novel adaptive diversification approach to provide resiliency against such attacks. SPREAD relies on a mechanismhopping technique. Mechanism-hopping can be seen as a multilayer extension of the frequency-hopping technique for physicallayer protection against narrow-band jamming. In order to analyze the proposed approach, we propose a game-theoretic framework for analyzing the interaction of the communicating nodes and the adversaries and study the various possible strategies. We reason about the advantages of the proposed approach against various types of jammers. We demonstrate the effectiveness of our approach in the case of IEEE802.11 protocol stack by studying the EIFS attack, Packet-Size Game, and Coding-Packet-Size Game. As an example, we show that mechanism-hopping over two instances of IEEE802.11 can achieve a gain in throughput of several orders of magnitude over a single-instance network.

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.

Cognitive radio is frequently touted as a platform for implementing dynamic distributed radio resource management algorithms. In the envisioned scenarios, radios react to measurements of the network state and change their operation according to some goal driven algorithm. Ideally this flexibility and reactivity yields tremendous gains in performance. However, when the adaptations of the radios also change the network state, an interactive decision process is spawned and once desirable algorithms can lead to catastrophic failures when deployed in a network. This document presents techniques for modeling and analyzing the interactions of cognitive radio for the purpose of improving the design of cognitive radio and distributed radio resource management algorithms with particular interest towards characterizing the algorithms ’ steady-state, convergence, and stability properties. This is accomplished by combining traditional engineering and nonlinear programming analysis techniques with techniques from game to create a powerful model based approach that permits rapid characterization of a cognitive radio algorithm’s properties. Insights gleaned from these models are used to establish novel design guidelines for cognitive radio design and

Under certain assumptions in terms of information and models, equilibria correspond to possible stable outcomes in conflicting or cooperative scenarios where intelligent entities (e.g., terminals) interact. For wireless engineers, it is of paramount importance to be able to predict and even ensure such states at which the network will effectively operate. In this article, we provide non-exhaustive methodologies for characterizing equilibria in wireless games in terms of existence, uniqueness, selection and efficiency.

We study competition between wireless devices with incomplete information about their opponents. We model such interactions as Bayesian interference games. Each wireless device selects a power profile over the entire available bandwidth to maximize its data rate (measured via Shannon capacity), which requires mitigating the effect of interference caused by other devices. Such competitive models represent situations in which several wireless devices share spectrum without any central authority or coordinated protocol. In contrast to games where devices have complete information about their opponents, we consider scenarios where the devices are unaware of the interference they cause to other devices. Such games, which are modeled as Bayesian games, can exhibit significantly different equilibria. We first consider a simple scenario where the devices select their power profile simultaneously. In such simultaneous move games, we show that the unique Bayes-Nash equilibrium is where both devices spread their power equally across the entire bandwidth. We then extend this model to a two-tiered spectrum sharing case where users act sequentially. Here one of the devices, called the primary user, is the owner of the spectrum and it selects its power profile first. The second device (called the secondary user) then responds by choosing a power profile to maximize its Shannon capacity. In such sequential move games, we show that there exist equilibria in which the primary user obtains a higher data rate by using only a part of the bandwidth.

The ongoing growth in wireless communication continues to increase demand on the frequency spectrum. The current rigid frequency band allocation policy leads to a significant under-utilization of this scarce resource. However, recent policy changes by the Federal Communications Commission (FCC) and research directions suggested by the Defense Advanced Research Projects Agency (DARPA) have been focusing on wireless devices that can adaptively and intelligently adjust their transmission characteristics, which are known as cognitive radios. This paper suggests a game theoretical approach that allows master-slave cognitive radio pairs to update their transmission powers and frequencies simultaneously. This is shown to lead to an exact potential game, for which it is known that a particular update scheme converges to a Nash Equilibrium (NE). Next, a Stackelberg game model is presented for frequency bands where a licensed user has priority over opportunistic cognitive radios. We suggest a modification to the exact potential game discussed earlier that would allow a Stackelberg leader to charge a virtual price for communicating over a licensed channel. We investigate virtual price update algorithms for the leader and prove the convergence of a specific algorithm. Simulations performed in Matlab verify our convergence results and demonstrate the performance gains over alternative algorithms. 1.

Abstract—We consider a wireless collision channel, shared by a finite number of users who transmit to a common base station. Users are self-optimizing, and each wishes to minimize its average transmission rate (or power investment), subject to minimumthroughput demand. The channel quality between each user and the base station is time-varying, and partially observed by the user in the form of channel state information (CSI) signals. We assume that each user can transmit at a fixed power level and that its transmission decision at each time slot is stationary in the sense that it can depend only on the current CSI. We are interested in properties of the Nash equilibrium of the resulting game between users. We define the feasible region of user’s throughput demands, and show that when the demands are within this region, there exist exactly two Nash equilibrium points, with one strictly better than the other (in terms of invested power) for all users. We further provide some lower bounds on the channel capacity that can be obtained, both in the symmetric and non-symmetric case. Finally, we show that a simple greedy mechanism converges to the best equilibrium point without requiring any coordination between the users.