Pricing mechanisms for multi-carrier wireless systems

Chorppath, Anil Kumar; Alpcan, Tansu; Boche, Holger

doi:10.1109/dyspan.2011.5936199

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…Remark For the interference-coupled case, pricing mechanism is proposed in [20]. Let us denote the mechanism as M d for the interference-coupled case.…”

Section: Price Of Malice In Pricing Mechanismsmentioning

confidence: 99%

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Adversarial Behavior in Network Games

2014

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This paper studies the effects of and countermeasures against adversarial behavior in network resource allocation mechanisms such as auctions and pricing schemes. It models the heterogeneous behavior of users, which ranges from altruistic to selfish and to malicious, within the analytical framework of game theory. A mechanism design approach is adopted to quantify the effect of adversarial behavior, which ranges from extreme selfishness to destructive maliciousness. First, the well-known result on the Vicrey-Clarke-Groves (VCG) mechanism losing its efficiency property in the presence of malicious users is extended to the case of divisible resource allocation to motivate the need to quantify the effect of malicious behavior. Then, the Price of Malice of the VCG mechanism and of some other network mechanisms are derived. In this context, the dynamics and convergence properties of an iterative distributed pricing algorithm are analyzed. The resistance of a mechanism to collusions is investigated next, and the effect of collusion of some malicious users is quantified. Subsequently, the assumption that the malicious user has information about the utility function of selfish users is relaxed, and a regression-based iterative learning scheme is presented and applied to both pricing and auction mechanisms. Differentiated pricing as a method to counter adversarial behaviors is proposed and briefly discussed. The results obtained are illustrated with numerical examples and simulations.

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“…Remark For the interference-coupled case, pricing mechanism is proposed in [20]. Let us denote the mechanism as M d for the interference-coupled case.…”

Section: Price Of Malice In Pricing Mechanismsmentioning

confidence: 99%

“…All the users including malicious ones take gradient best response with respect to their cost function in dynamics given by the iterative algorithm. Let the iterative distributed mechanism be M c given in [20] as,…”

Section: Convergence Analysis Of An Iterative Distributed Algorithmmentioning

confidence: 99%

Adversarial Behavior in Network Games

2014

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“…Proof: The prices are obtained as the same way of alignment of user and designer objective through prices as in [13] with the modified designer objective in (21).…”

Section: A Case Of Only 1 User Of Unknown Typementioning

confidence: 99%

“…The prices which are proposed in [13] for social optimum point in the N user case, modified with the energy cost B, are obtained by the matrix equation,…”

Section: Bayesian Pricing Mechanismsmentioning

confidence: 99%

Bayesian mechanisms for wireless network security

Chorppath

Alpcan

Boche

2014

2014 IEEE International Conference on Communications (ICC)

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Strategic users in a wireless network cannot be assumed to follow the network algorithms blindly. Moreover, some of these users could be controlled by powerful Botnets, which aim to use their knowledge about network algorithms to maliciously gain more resources and also to create interference to other users. We consider a scenario; in which a mechanism designer and legitimate users together, in a wireless network, gather probabilistic information about the presence of malicious users and modify their actions accordingly. The probabilistic information is gathered by observing the network over a long time period. We study Bayesian mechanisms, both pricing schemes and auctions, and obtain the Nash Equilibrium (NE) points of the underlying Bayesian games. The NE points provide conditions indicating when it is better for users to to hide or reveal their nature(types). The prices and allocations in the mechanisms are later modified using the Bayesian information about the type of the users. The numerical studies show the NE points and illustrate the results.

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“…Incentives play a large role in reconciling the objectives of selfish and strategic nodes to the global objectives of the network, which may include the efficient allocation of network resources [3]. In [4], a pricing game is considered within a multi-hop relay network where link cost functions depend only on the traffic flow rate.…”

Section: Introductionmentioning

confidence: 99%

Pricing games in multihop wireless networks under interference constraints

Chorppath¹,

Yeh

Boche³

2014

2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

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In this paper, we consider a multihop wireless network, where Femto Base Stations (FBSs) act as relay nodes, and are incentivized to carry traffic from a Macro Base Station (MBS) to Macro Users (MUs). We first examine the the global problem of jointly optimal allocation of traffic flow and transmission power in the multihop wireless network. We then examine a game in which selfish and strategic relays submit charging functions to the source and choose transmission powers over a MAC channel from the relays to the user. Relay charging functions are considered which yield efficient allocation at the Nash Equilibrium (NE) of the game. We observe that for efficiency, relays should be taxed for the interference it creates to other relays. We also observe that inefficient equilibria occur when the charging function is a function only of the traffic flow rate through the relay. Numerical studies demonstrate the variation of inefficiency with network structure.

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Pricing mechanisms for multi-carrier wireless systems

Cited by 7 publications

References 26 publications

Adversarial Behavior in Network Games

Adversarial Behavior in Network Games

Bayesian mechanisms for wireless network security

Pricing games in multihop wireless networks under interference constraints

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