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105
Distributed interference compensation for wireless networks
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 2006
"... 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 ..."
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Cited by 177 (33 self)
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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.
A gametheoretic approach to energyefficient power control in multicarrier CDMA systems
 IEEE Journal on Selected Areas in Communications (JSAC
, 2006
"... Abstract—A gametheoretic model for studying power control in multicarrier codedivision multipleaccess systems is proposed. Power control is modeled as a noncooperative game in which each user decides how much power to transmit over each carrier to maximize its own utility. The utility function co ..."
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Cited by 89 (8 self)
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Abstract—A gametheoretic model for studying power control in multicarrier codedivision multipleaccess systems is proposed. Power control is modeled as a noncooperative game in which each user decides how much power to transmit over each carrier to maximize its own utility. The utility function considered here measures the number of reliable bits transmitted over all the carriers per joule of energy consumed and is particularly suitable for networks where energy efficiency is important. The multidimensional nature of users ’ strategies and the nonquasiconcavity of the utility function make the multicarrier problem much more challenging than the singlecarrier or throughputbasedutility case. It is shown that, for all linear receivers including the matched filter, the decorrelator, and the minimummeansquareerror detector, a user’s utility is maximized when the user transmits only on its “best ” carrier. This is the carrier that requires the least amount of power to achieve a particular target signaltointerferenceplusnoise ratio at the output of the receiver. The existence and uniqueness of Nash equilibrium for the proposed power control game are studied. In particular, conditions are given that must be satisfied by the channel gains for a Nash equilibrium to exist, and the distribution of the users among the carriers at equilibrium is characterized. In addition, an iterative and distributed algorithm for reaching the equilibrium (when it exists) is presented. It is shown that the proposed approach results in significant improvements in the total utility achieved at equilibrium compared with a singlecarrier system and also to a multicarrier system in which each user maximizes its utility over each carrier independently. Index Terms—Energy efficiency, game theory, multicarrier codedivision multipleaccess (CDMA), multiuser detection, Nash equilibrium, power control, utility function. I.
Energyefficient resource allocation in wireless networks: An overview of gametheoretic approaches
 IEEE Signal Process. Magazine
, 2007
"... A gametheoretic model is proposed to study the crosslayer problem of joint power and rate control with quality of service (QoS) constraints in multipleaccess networks. In the proposed game, each user seeks to choose its transmit power and rate in a distributed manner in order to maximize its own ..."
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Cited by 55 (8 self)
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A gametheoretic model is proposed to study the crosslayer problem of joint power and rate control with quality of service (QoS) constraints in multipleaccess networks. In the proposed game, each user seeks to choose its transmit power and rate in a distributed manner in order to maximize its own utility while satisfying 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 queuing delays. The utility function considered here measures energy efficiency and is particularly suitable for wireless networks with energy constraints. The Nash equilibrium solution for the proposed noncooperative game is derived and a closedform 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 competitive multiuser framework, the tradeoffs among throughput, delay, network capacity and energy efficiency are studied. In addition, analytical expressions are given for users ’ delay profiles and the delay performance of the users at Nash equilibrium is quantified.
Adaptation, coordination, and distributed resource allocation in interferencelimited wireless networks.
 Proceeding of the IEEE,
, 2007
"... ABSTRACT  A sensible design of wireless networks involves striking a good balance between an aggressive reuse of the spectral resource throughout the network and managing the resulting cochannel interference. Traditionally, this problem has been tackled using a Bdivide and conquer[ approach. The ..."
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Cited by 34 (3 self)
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ABSTRACT  A sensible design of wireless networks involves striking a good balance between an aggressive reuse of the spectral resource throughout the network and managing the resulting cochannel interference. Traditionally, this problem has been tackled using a Bdivide and conquer[ approach. The latter consists in deploying the network with a static or semidynamic pattern of resource reutilization. The chosen reuse factor, while sacrificing a substantial amount of efficiency, brings the interference to a tolerable level. The resource can then be managed in each cell so as to optimize the per cell capacity using an advanced air interface design. In this paper, we focus our attention on the overall network capacity as a measure of system performance. We consider the problem of resource allocation and adaptive transmission in multicell scenarios. As a key instance, the problem of joint scheduling and power control simultaneously in multiple transmitreceive links, which employ capacityachieving adaptive codes, is studied. In principle, the solution of such an optimization hinges on tough issues such as the computational complexity and the requirement for heavy receivertotransmitter feedback and, for cellular networks, celltocell channel state information (CSI) signaling. We give asymptotic properties pertaining to ratemaximizing power control and scheduling in multicell networks. We then present some promising leads for substantial complexity and signaling reduction via the use of newly developed distributed and game theoretic techniques.
Stochastic learning solution for distributed discrete power control game in wireless data networks
, 2008
"... Distributed power control is an important issue in wireless networks. Recently, noncooperative game theory has been applied to investigate interesting solutions to this problem. The majority of these studies assumes that the transmitter power level can take values in a continuous domain. However, r ..."
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Cited by 28 (0 self)
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Distributed power control is an important issue in wireless networks. Recently, noncooperative game theory has been applied to investigate interesting solutions to this problem. The majority of these studies assumes that the transmitter power level can take values in a continuous domain. However, recent trends such as the GSM standard and Qualcomm’s proposal to the IS95 standard use a finite number of discretized power levels. This motivates the need to investigate solutions for distributed discrete power control which is the primary objective of this paper. We first note that, by simply discretizing, the previously proposed continuous power adaptation techniques will not suffice. This is because a simple discretization does not guarantee convergence and uniqueness. We propose two probabilistic power adaptation algorithms and analyze their theoretical properties along with the numerical behavior. The distributed discrete power control problem is formulated as anperson, nonzero sum game. In this game, each user evaluates a power strategy by computing a utility value. This evaluation is performed using a stochastic iterative procedures. We approximate the discrete power control iterations by an equivalent ordinary differential equation to prove that the proposed stochastic learning power control algorithm converges to a stable Nash equilibrium. Conditions when more than one stable Nash equilibrium or even only mixed equilibrium may exist are also studied. Experimental results are presented for several cases and compared with the continuous power level adaptation solutions.
Wireless Operators in a Shared Spectrum
, 2005
"... So far, cellular networks have been operated in “private” frequency bands. But recently, several researchers and legislators have argued in favor of a more flexible and more efficient management of the spectrum, leading to the possible coexistence of several network operators in a shared frequency b ..."
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Cited by 27 (5 self)
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So far, cellular networks have been operated in “private” frequency bands. But recently, several researchers and legislators have argued in favor of a more flexible and more efficient management of the spectrum, leading to the possible coexistence of several network operators in a shared frequency band. In our paper, we study this situation in detail, assuming that mobile devices can freely roam among the various operators. Free roaming means that the mobile devices measure the signal strength of the pilot signals (i.e., beacon signals) of the base stations and attach to the base station with the strongest pilot signal. We model the behavior of the network operators in a game theoretic setting in which each operator decides the power of the pilot signal of its base stations. We first identify possible Nash equilibria in the theoretical setting in which all base stations are located on the vertices of a twodimensional lattice. We then relax this topological assumption and show that, in the more general case, finding the Nash equilibria is an NPcomplete problem. Finally, we prove that a socially optimal Nash equilibrium exists and that it can be enforced by using punishments.
Balancing Supply and Demand of Bandwidth in Wireless Cellular Networks: Utility Maximization over Powers and Rates
 in Proc. IEEE INFOCOM
, 2004
"... In wireless cellular networks and wireless local area networks, nonlinear network utility maximization need to be conducted over both user rates and transmit powers. For each of the three cases considered in this paper, we present an algorithm that converges to the jointly optimal pair of rate vecto ..."
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Cited by 24 (1 self)
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In wireless cellular networks and wireless local area networks, nonlinear network utility maximization need to be conducted over both user rates and transmit powers. For each of the three cases considered in this paper, we present an algorithm that converges to the jointly optimal pair of rate vector and power vector. For the simple case when data rates are not limited by interferences, for example in singlecell downlink transmissions, Algorithm 1 we propose is an iterative bidding mechanism between the base station and mobile users, where knowledge about channel conditions and individual user utility functions is only needed locally at each user but not needed at the base station.
Downlink power allocation for multiclass wireless systems,” submitted for publication
, 2002
"... In this paper we consider a power allocation problem in multiclass wireless systems. We focus on the downlink of the system. Each mobile has a utility function that characterizes its degree of satisfaction for the received service. The objective is to obtain a power allocation that maximizes the to ..."
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Cited by 24 (1 self)
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In this paper we consider a power allocation problem in multiclass wireless systems. We focus on the downlink of the system. Each mobile has a utility function that characterizes its degree of satisfaction for the received service. The objective is to obtain a power allocation that maximizes the total system utility. Typically, natural utility functions for each mobile are nonconcave. Hence, we cannot use existing convex optimization techniques to derive a global optimal solution. We develop a simple (distributed) algorithm to obtain a power allocation that is asymptotically optimal in the number of mobiles. The algorithm is based on dynamic pricing and consists of two stages. At the mobile selection stage, the basestation selects mobiles to which power is allocated. At the power allocation stage, the basestation allocates power to the selected mobiles. We provide numerical results that illustrate the performance of our scheme. In particular, we show that our algorithm results in system performance that is close to the performance of a global optimal solution in most cases. Index Terms Power allocation, downlink, wireless networks, and nonconvex optimization.
Topology control for maintaining network connectivity and maximizing network capacity under the physical model
 in INFOCOM 2008. The 27th Conference on Computer Communications. IEEE
"... Abstract—In this paper we study the issue of topology control under the physical SignaltoInterferenceNoiseRatio (SINR) model, with the objective of maximizing network capacity. We show that existing graphmodelbased topology control captures interference inadequately under the physical SINR mod ..."
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Cited by 24 (2 self)
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Abstract—In this paper we study the issue of topology control under the physical SignaltoInterferenceNoiseRatio (SINR) model, with the objective of maximizing network capacity. We show that existing graphmodelbased topology control captures interference inadequately under the physical SINR model, and as a result, the interference in the topology thus induced is high and the network capacity attained is low. Towards bridging this gap, we propose a centralized approach, called Spatial Reuse Maximizer (MaxSR), that combines a power control algorithm T4P with a topology control algorithm P4T. T4P optimizes the assignment of transmit power given a fixed topology, where by optimality we mean that the transmit power is so assigned that it minimizes the average interference degree (defined as the number of interferencing nodes that may interfere with the ongoing transmission on a link) in the topology. P4T, on the other hand, constructs, based on the power assignment made in T4P, a new topology by deriving a spanning tree that gives the minimal interference degree. By alternately invoking the two algorithms, the power assignment quickly converges to an operational point that maximizes the network capacity. We formally prove the convergence of MaxSR. We also show via simulation that the topology induced by MaxSR outperforms that derived from existing topology control algorithms by 50%110 % in terms of maximizing the network capacity. I.
Performance Evaluation of Cognitive Radios: Metrics, Utility Functions and Methodology. Invited paper, under review for proceedings of the
 IEEE Special Issue on Cognitive Radio
, 2009
"... Abstract — The selection and design of performance metrics and utility functions is an important, but inadequately addressed issue in the design of cognitive radio networks. Unlike traditional radios, a cognitive radio may change its objectives as radio scenarios vary. Because of the dynamic pairing ..."
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Cited by 22 (17 self)
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Abstract — The selection and design of performance metrics and utility functions is an important, but inadequately addressed issue in the design of cognitive radio networks. Unlike traditional radios, a cognitive radio may change its objectives as radio scenarios vary. Because of the dynamic pairing of objectives and contexts, it is imperative for cognitive radio network designers to have a firm understanding of the interrelationships between goals, performance metrics, utility functions, link/network performance, and operating environments. In this paper, we first overview the hierarchical metrics for evaluating the performance of cognitive radios from the node, network, and application levels. From a gametheoretic viewpoint, we then show that the performance evaluation of cognitive radio networks exhibits the interdependent nature of actions, goals, decisions, observations, and context. We discuss the interrelationships between metrics, utility functions, cognitive engine algorithms and achieved performance. Various testing scenarios need to be employed to comprehensively evaluate the cognitive functionality of cognitive radios. We propose the radio environment mapbased scenariodriven testing (REMSDT) for thorough performance evaluation of cognitive radios. An IEEE 802.22 WRAN cognitive engine testbed is presented to provide further insights into this important problem area. Index Terms — Cognitive radio, cognitive wireless network, game theory, performance evaluation, performance metric, utility function. I.