The Experts below are selected from a list of 68265 Experts worldwide ranked by ideXlab platform
Jianwei Huang - One of the best experts on this subject based on the ideXlab platform.
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Spatial Spectrum Access Game
IEEE Transactions on Mobile Computing, 2015Co-Authors: Xu Chen, Jianwei HuangAbstract:A key feature of wireless communications is the spatial reuse. However, the spatial aspect is not yet well understood for the purpose of designing efficient Spectrum sharing mechanisms. In this paper, we propose a framework of spatial Spectrum Access games on directed interference graphs, which can model quite general interference relationship with spatial reuse in wireless networks. We show that a pure Nash equilibrium exists for the two classes of games: (1) any spatial Spectrum Access games on directed acyclic graphs, and (2) any games satisfying the congestion property on directed trees and directed forests. Under mild technical conditions, the spatial Spectrum Access games with random backoff and Aloha channel contention mechanisms on undirected graphs also have a pure Nash equilibrium. We also quantify the price of anarchy of the spatial Spectrum Access game. We then propose a distributed learning algorithm, which only utilizes users’ local observations to adaptively adjust the Spectrum Access strategies. We show that the distributed learning algorithm can converge to an approximate mixed-strategy Nash equilibrium for any spatial Spectrum Access games. Numerical results demonstrate that the distributed learning algorithm achieves up to $100$ percent performance improvement over a random Access algorithm.
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Imitative Spectrum Access Mechanism
SpringerBriefs in Electrical and Computer Engineering, 2015Co-Authors: Xu Chen, Jianwei HuangAbstract:In this chapter, we will design distributed Spectrum Access mechanism based on imitation, which is also a common phenomenon in many social animal and human interactions [1].
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Evolutionarily Stable Spectrum Access Mechanism
SpringerBriefs in Electrical and Computer Engineering, 2015Co-Authors: Xu Chen, Jianwei HuangAbstract:In this chapter, motivated by the evolution rule observed in many social animal and human interactions, we propose a new framework of distributed Spectrum Access with and without complete network information (i.e., channel statistics and user selections).
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Spatial Spectrum Access Game
arXiv: Networking and Internet Architecture, 2014Co-Authors: Xu Chen, Jianwei HuangAbstract:A key feature of wireless communications is the spatial reuse. However, the spatial aspect is not yet well understood for the purpose of designing efficient Spectrum sharing mechanisms. In this paper, we propose a framework of spatial Spectrum Access games on directed interference graphs, which can model quite general interference relationship with spatial reuse in wireless networks. We show that a pure Nash equilibrium exists for the two classes of games: (1) any spatial Spectrum Access games on directed acyclic graphs, and (2) any games satisfying the congestion property on directed trees and directed forests. Under mild technical conditions, the spatial Spectrum Access games with random backoff and Aloha channel contention mechanisms on undirected graphs also have a pure Nash equilibrium. We also quantify the price of anarchy of the spatial Spectrum Access game. We then propose a distributed learning algorithm, which only utilizes users' local observations to adaptively adjust the Spectrum Access strategies. We show that the distributed learning algorithm can converge to an approximate mixed-strategy Nash equilibrium for any spatial Spectrum Access games. Numerical results demonstrate that the distributed learning algorithm achieves up to superior performance improvement over a random Access algorithm.
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Evolutionarily Stable Spectrum Access
IEEE Transactions on Mobile Computing, 2013Co-Authors: Xu Chen, Jianwei HuangAbstract:In this paper, we design distributed Spectrum Access mechanisms with both complete and incomplete network information. We propose an evolutionary Spectrum Access mechanism with complete network information, and show that the mechanism achieves an equilibrium that is globally evolutionarily stable. With incomplete network information, we propose a distributed learning mechanism, where each user utilizes local observations to estimate the expected throughput and learns to adjust its Spectrum Access strategy adaptively over time. We show that the learning mechanism converges to the same evolutionary equilibrium on the time average. Numerical results show that the proposed mechanisms achieve up to 35 percent performance improvement over the distributed reinforcement learning mechanism in the literature, and are robust to the perturbations of users' channel selections.
Xu Chen - One of the best experts on this subject based on the ideXlab platform.
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Spatial Spectrum Access Game
IEEE Transactions on Mobile Computing, 2015Co-Authors: Xu Chen, Jianwei HuangAbstract:A key feature of wireless communications is the spatial reuse. However, the spatial aspect is not yet well understood for the purpose of designing efficient Spectrum sharing mechanisms. In this paper, we propose a framework of spatial Spectrum Access games on directed interference graphs, which can model quite general interference relationship with spatial reuse in wireless networks. We show that a pure Nash equilibrium exists for the two classes of games: (1) any spatial Spectrum Access games on directed acyclic graphs, and (2) any games satisfying the congestion property on directed trees and directed forests. Under mild technical conditions, the spatial Spectrum Access games with random backoff and Aloha channel contention mechanisms on undirected graphs also have a pure Nash equilibrium. We also quantify the price of anarchy of the spatial Spectrum Access game. We then propose a distributed learning algorithm, which only utilizes users’ local observations to adaptively adjust the Spectrum Access strategies. We show that the distributed learning algorithm can converge to an approximate mixed-strategy Nash equilibrium for any spatial Spectrum Access games. Numerical results demonstrate that the distributed learning algorithm achieves up to $100$ percent performance improvement over a random Access algorithm.
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Imitative Spectrum Access Mechanism
SpringerBriefs in Electrical and Computer Engineering, 2015Co-Authors: Xu Chen, Jianwei HuangAbstract:In this chapter, we will design distributed Spectrum Access mechanism based on imitation, which is also a common phenomenon in many social animal and human interactions [1].
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Evolutionarily Stable Spectrum Access Mechanism
SpringerBriefs in Electrical and Computer Engineering, 2015Co-Authors: Xu Chen, Jianwei HuangAbstract:In this chapter, motivated by the evolution rule observed in many social animal and human interactions, we propose a new framework of distributed Spectrum Access with and without complete network information (i.e., channel statistics and user selections).
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SGUM-based Database Assisted Spectrum Access
SpringerBriefs in Electrical and Computer Engineering, 2014Co-Authors: Xiaowen Gong, Xu Chen, Lei Yang, Junshan ZhangAbstract:In this chapter, we study the application of the SGUM framework to database assisted Spectrum Access.
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Spatial Spectrum Access Game
arXiv: Networking and Internet Architecture, 2014Co-Authors: Xu Chen, Jianwei HuangAbstract:A key feature of wireless communications is the spatial reuse. However, the spatial aspect is not yet well understood for the purpose of designing efficient Spectrum sharing mechanisms. In this paper, we propose a framework of spatial Spectrum Access games on directed interference graphs, which can model quite general interference relationship with spatial reuse in wireless networks. We show that a pure Nash equilibrium exists for the two classes of games: (1) any spatial Spectrum Access games on directed acyclic graphs, and (2) any games satisfying the congestion property on directed trees and directed forests. Under mild technical conditions, the spatial Spectrum Access games with random backoff and Aloha channel contention mechanisms on undirected graphs also have a pure Nash equilibrium. We also quantify the price of anarchy of the spatial Spectrum Access game. We then propose a distributed learning algorithm, which only utilizes users' local observations to adaptively adjust the Spectrum Access strategies. We show that the distributed learning algorithm can converge to an approximate mixed-strategy Nash equilibrium for any spatial Spectrum Access games. Numerical results demonstrate that the distributed learning algorithm achieves up to superior performance improvement over a random Access algorithm.
Qing Zhao - One of the best experts on this subject based on the ideXlab platform.
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a survey of dynamic Spectrum Access signal processing and networking perspectives
International Conference on Acoustics Speech and Signal Processing, 2007Co-Authors: Qing Zhao, Ananthram SwamiAbstract:In this paper, we provide a survey of dynamic Spectrum Access techniques. Various approaches envisioned for dynamic Spectrum Access are broadly categorized under three models: dynamic exclusive use model, open sharing model, and hierarchical Access model. Based on this taxonomy, we provide an overview of the technical challenges and recent advances under each model.
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A Survey of Dynamic Spectrum Access
IEEE Signal Processing Magazine, 2007Co-Authors: Qing Zhao, Brian M. SadlerAbstract:Compounding the confusion is the use of the broad term cognitive radio as a synonym for dynamic Spectrum Access. As an initial attempt at unifying the terminology, the taxonomy of dynamic Spectrum Access is provided. In this article, an overview of challenges and recent developments in both technological and regulatory aspects of opportunistic Spectrum Access (OSA). The three basic components of OSA are discussed. Spectrum opportunity identification is crucial to OSA in order to achieve nonintrusive communication. The basic functions of the opportunity identification module are identified
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ICASSP (3) - Spectrum Opportunity and Interference Constraint in Opportunistic Spectrum Access
2007 IEEE International Conference on Acoustics Speech and Signal Processing - ICASSP '07, 2007Co-Authors: Qing ZhaoAbstract:In this paper, we study two important concepts in opportunistic Spectrum Access: Spectrum opportunity and interference constraint. We aim to provide a certain level of rigor to these seemingly intuitive terms central to opportunistic Spectrum Access. Their implications in Spectrum opportunity detection and transmission power control of secondary users are discussed.
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A Decision-Theoretic Framework for Opportunistic Spectrum Access
IEEE Wireless Communications, 2007Co-Authors: Qing Zhao, Ananthram SwamiAbstract:Built on a hierarchical Access structure with primary and secondary users, opportunistic Spectrum Access improves Spectrum efficiency while maintaining compatibility with legacy wireless systems. The basic idea is to allow secondary users to exploit instantaneous Spectrum availability while limiting the interference to primary users. In this article, we identify basic components, fundamental trade-offs, and practical constraints in opportunistic Spectrum Access. We introduce a decision-theoretic framework based on the theory of partially observable Markov decision processes. This framework allows us to systematically tackle the optimal integrated design and quantitatively characterize the interaction between signal processing for opportunity identification and networking for opportunity exploitation. A discussion of open problems, potential applications, and recent developments is also provided.
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Dynamic Spectrum Access: Signal Processing, Networking, and Regulatory Policy
arXiv: Networking and Internet Architecture, 2006Co-Authors: Qing Zhao, Brian M. SadlerAbstract:In this article, we first provide a taxonomy of dynamic Spectrum Access. We then focus on opportunistic Spectrum Access, the overlay approach under the hierarchical Access model of dynamic Spectrum Access. we aim to provide an overview of challenges and recent developments in both technological and regulatory aspects of opportunistic Spectrum Access.
Ananthram Swami - One of the best experts on this subject based on the ideXlab platform.
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a survey of dynamic Spectrum Access signal processing and networking perspectives
International Conference on Acoustics Speech and Signal Processing, 2007Co-Authors: Qing Zhao, Ananthram SwamiAbstract:In this paper, we provide a survey of dynamic Spectrum Access techniques. Various approaches envisioned for dynamic Spectrum Access are broadly categorized under three models: dynamic exclusive use model, open sharing model, and hierarchical Access model. Based on this taxonomy, we provide an overview of the technical challenges and recent advances under each model.
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A Decision-Theoretic Framework for Opportunistic Spectrum Access
IEEE Wireless Communications, 2007Co-Authors: Qing Zhao, Ananthram SwamiAbstract:Built on a hierarchical Access structure with primary and secondary users, opportunistic Spectrum Access improves Spectrum efficiency while maintaining compatibility with legacy wireless systems. The basic idea is to allow secondary users to exploit instantaneous Spectrum availability while limiting the interference to primary users. In this article, we identify basic components, fundamental trade-offs, and practical constraints in opportunistic Spectrum Access. We introduce a decision-theoretic framework based on the theory of partially observable Markov decision processes. This framework allows us to systematically tackle the optimal integrated design and quantitatively characterize the interaction between signal processing for opportunity identification and networking for opportunity exploitation. A discussion of open problems, potential applications, and recent developments is also provided.
Kobi Cohen - One of the best experts on this subject based on the ideXlab platform.
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Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access
IEEE Transactions on Wireless Communications, 2019Co-Authors: Oshri Naparstek, Kobi CohenAbstract:We consider the problem of dynamic Spectrum Access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into K orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for Accessing the Spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the Spectrum Access problem is computationally expensive, in general, due to the large-state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic Spectrum Access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to the Spectrum Access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. The experimental results demonstrate the strong performance of the algorithm.
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deep multi user reinforcement learning for distributed dynamic Spectrum Access
arXiv: Networking and Internet Architecture, 2017Co-Authors: Oshri Naparstek, Kobi CohenAbstract:We consider the problem of dynamic Spectrum Access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into K orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for Accessing the Spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the Spectrum Access problem is computationally expensive in general due to the large state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic Spectrum Access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to Spectrum Access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. Experimental results demonstrate strong performance of the algorithm.
