Jammer-Type Estimation in LTE With a Smart Jammer Repeated Game

Aziz, Farhan; Shamma, Jeff S.; Stuber, G.L.

doi:10.1109/tvt.2017.2672682

Cited by 29 publications

(17 citation statements)

References 32 publications

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“…Typically, in literature, uncertainty on one parameter is considered. For example, in [3,5,6,9], uncertainty about rival's type was investigated. In [12], uncertainty on fading channel gains was considered in the context of single carrier communications with non-hostile interference caused by selfish users, while, in [2,10], with hostile interference caused by an adversary.…”

Section: Introductionmentioning

confidence: 99%

A Non-zero Sum Power Control Game with Uncertainty

Garnaev

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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We consider the communication between a transmitter (user) and a receiver in the presence of a jammer where the jammer, in contrast to the user, has access to local information about jamming fading gain (reflecting distance of the jammer to the receiver) and jamming cost (reflecting its technical characteristics). The problem is modeled as a Bayesian game. Signal-to-interference-plus-noise ratio (SINR) is considered as user's communication utility. Nash equilibrium as well as Stackelberg equilibrium are derived in closed form and compared.

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Section: Introductionmentioning

confidence: 99%

A Non-zero Sum Power Control Game with Uncertainty

Garnaev

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Typically, jamming problems can be categorized according to two frameworks: (i) maintaining communication reliability and (ii) maintaining communication connectivity. In communication reliability problems, the transmitter's payoff is a function of throughput or Signal-to-Interference-plus-Noise Ratio (SINR) at the receiver, and the transmitter intends to maximize such payoff [3,4,5,6,7,8,9,10,11]. Meanwhile, in communication connectivity problems the transmitter must keep its SINR greater than or equal to a threshold value to ensure a connection can be sustained [12,13,14,15,16,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

A multi-link communication connectivity game under hostile interference

Poor¹,

Mandayam²,

Trappe³

et al. 2021

ITU-J FET

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In this paper, we consider a communication connectivity problem involving a primary user (transmitter, for example, a Ground Control Station (GCS)) servicing a group of secondary users (receivers, for example, drones) under hostile interference. We formulate this multi-link communication connectivity problem, where the channels are affected by Rayleigh fading, as a zero-sum power resource allocation game between a transmitter and an adversary (jammer). The transmitter's objective is to maximize the probability of communication connectivity with all the receivers. It is proven that the problem has unique equilibrium in power allocation strategies, and the equilibrium is derived in closed form. Moreover, we reduce the problem of designing the equilibrium in power resource allocation strategies to the problem of finding a fixed point of a real-valued function. An algorithm based on the bisection method to find the fixed point (and so equilibrium strategies) is developed, and its convergence is proven.

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“…There are many competitive settings in which players have asymmetric information about the underlying state of the game. Examples include cyber security problems [1], [2], resource competitions in air transportation systems [3], [4], national defense [5], [6], economic systems [7], power networks [8] and so on. In these systems, because of the noncooperation between players, a player usually holds private information that is not shared with the other players, which causes the information asymmetry in games.…”

Section: Introductionmentioning

confidence: 99%

“…The fact that the one-stage payoff is unavailable to the uninformed player happens in practice. For example, when jamming wireless sensor networks, the attacker may observe which channel the network uses (action of the informed player), but cannot measure the throughput in the channel (payoff of the informed player) [1], [9].…”

Section: Introductionmentioning

confidence: 99%

Efficient Strategy Computation in Zero-Sum Asymmetric Information Repeated Games

Shamma

2020

IEEE Trans. Automat. Contr.

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Zero-sum asymmetric games model decision making scenarios involving two competing players who have different information about the game being played. A particular case is that of nested information, where one (informed) player has superior information over the other (uninformed) player. This paper considers the case of nested information in repeated zerosum games and studies the computation of strategies for both the informed and uninformed players for finite-horizon and discounted infinite-horizon nested information games. For finitehorizon settings, we exploit that for both players, the security strategy, and also the opponent's corresponding best response depend only on the informed player's history of actions. Using this property, we refine the sequence form, and formulate an LP computation of player strategies that is linear in the size of the uninformed player's action set. For the infinite-horizon discounted game, we construct LP formulations to compute the approximated security strategies for both players, and provide a bound on the performance difference between the approximated security strategies and the security strategies. Finally, we illustrate the results on a network interdiction game between an informed system administrator and uniformed intruder.

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Jammer-Type Estimation in LTE With a Smart Jammer Repeated Game

Cited by 29 publications

References 32 publications

A Non-zero Sum Power Control Game with Uncertainty

A Non-zero Sum Power Control Game with Uncertainty

A multi-link communication connectivity game under hostile interference

Efficient Strategy Computation in Zero-Sum Asymmetric Information Repeated Games

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