Congestion based pricing in a dynamic wireless network

Heikkinen, Tiina

doi:10.1109/vetecs.2001.944543

Cited by 14 publications

(34 citation statements)

References 4 publications

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“…An example of such decentralized networks is a potential future network (ad hoc network), where the nodes are not always connected to a base station [10]. The power updating according to (40) can be seen as best-response (greedy) dynamics in a noncooperative game between the users [18].…”

Section: A Distributed Power Allocation Algorithmmentioning

confidence: 99%

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Optimal power control in a wireless network using a model with stochastic link coefficients

Heikkinen

Prékopa

2005

Naval Research Logistics

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This paper addresses optimal power allocation in a wireless communication network under uncertainty. The paper introduces a framework for optimal transmit power allocation in a wireless network where both the useful and interference coefficients are random. The new approach to power control is based on a stochastic programming formulation with probabilistic SIR constraints. This allows to state the power allocation problem as a convex optimization problem assuming normally or log-normally distributed communication link coefficients. Numerical examples illustrate the performance of the optimal stochastic power allocation. A distributed algorithm for the decentralized solution of the stochastic power allocation problem is discussed.

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Section: A Distributed Power Allocation Algorithmmentioning

confidence: 99%

“…. , m (10) Ϫ4 (using the above approximation). The interference parameters f ij are independent and normally distributed with E(e h ij ) ϭ f ij where f ij is the ijth entry in interference matrix (37), and each h ij is normally distributed.…”

Section: Example With Lognormal Distributionmentioning

confidence: 99%

Optimal power control in a wireless network using a model with stochastic link coefficients

Heikkinen

Prékopa

2005

Naval Research Logistics

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“…[1]) and wireless networks (e.g. [2]- [4]). The problem here differs from much of the previous work because, due to interference, the users' objective functions are coupled with each other, and the overall network objective is not concave in the allocated resource (transmit power).…”

Section: Introductionmentioning

confidence: 99%

A game theoretic analysis of distributed power control for spread spectrum ad hoc networks

Huang

Berry

Honig

2005

Proceedings. International Symposium on Information Theory, 2005. ISIT 2005.

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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.

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“…Specifically, markets have been used in computational grids [1], peer-to-peer systems [16], supply-chain management [11], scheduling [19], congestion control [9], routing [6], workflow automation [10] and recommender systems [18]. Building on this, the solution that we have developed consists of software agents that compete in a marketplace to buy and sell network bandwidth.…”

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confidence: 99%

Scalability and robustness of a network resource allocation system using market-based agents

2006

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Congestion based pricing in a dynamic wireless network

Cited by 14 publications

References 4 publications

Optimal power control in a wireless network using a model with stochastic link coefficients

Optimal power control in a wireless network using a model with stochastic link coefficients

A game theoretic analysis of distributed power control for spread spectrum ad hoc networks

Scalability and robustness of a network resource allocation system using market-based agents

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