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Beibei Wang

Researcher at University of Maryland, College Park

Publications -  169
Citations -  6904

Beibei Wang is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Wireless & Cognitive radio. The author has an hindex of 33, co-authored 154 publications receiving 5838 citations. Previous affiliations of Beibei Wang include Qualcomm & NTT DoCoMo.

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Advances in cognitive radio networks: A survey

TL;DR: Recent advances in research related to cognitive radios are surveyed, including the fundamentals of cognitive radio technology, architecture of a cognitive radio network and its applications, and important issues in dynamic spectrum allocation and sharing are investigated in detail.
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Game theory for cognitive radio networks: An overview

TL;DR: This tutorial survey provides a comprehensive treatment of game theory with important applications in cognitive radio networks, and will aid the design of efficient, self-enforcing, and distributed spectrum sharing schemes in future wireless networks.
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Distributed Relay Selection and Power Control for Multiuser Cooperative Communication Networks Using Stackelberg Game

TL;DR: This paper proposes a distributed game-theoretical framework over multiuser cooperative communication networks to achieve optimal relay selection and power allocation without knowledge of CSI.
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An anti-jamming stochastic game for cognitive radio networks

TL;DR: The proposed stationary policy in the anti-jamming game is shown to achieve much better performance than the policy obtained from myopic learning, which only maximizes each stage's payoff, and a random defense strategy, since it successfully accommodates the environment dynamics and the strategic behavior of the cognitive attackers.
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Anti-Jamming Games in Multi-Channel Cognitive Radio Networks

TL;DR: This paper derives a channel hopping defense strategy using the Markov decision process approach with the assumption of perfect knowledge, and proposes two learning schemes for secondary users to gain knowledge of adversaries to handle cases without perfect knowledge.