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Journal ArticleDOI

Game Theoretic Approaches for Multiple Access in Wireless Networks: A Survey

TLDR
This survey provides a comprehensive review of the game models developed for different multiple access schemes (i.e., contention-free and contention-based random channel access) in wireless networks and outlines several of the key open research directions.
Abstract
Multiple access methods in a wireless network allow multiple nodes to share a set of available channels for data transmission. The nodes can either compete or cooperate with each other to access the channel(s) so that either an individual or a group objective can be achieved. Game theory, which is a mathematical tool developed to understand the interaction among rational entities, can be applied to model and to analyze individual or group behaviour of nodes for multiple access in wireless networks. Game theory also enables us to model the selfish/malicious behaviour of nodes, and subsequently design the punishment or defense mechanisms for robust multiple access in wireless networks. In addition, game models can provide distributed solutions to the multiple access problems, which are based on solid theoretical foundations. In this survey, we provide a comprehensive review of the game models (e.g., noncooperative/cooperative, static/dynamic, and complete/incomplete information) developed for different multiple access schemes (i.e., contention-free and contention-based random channel access) in wireless networks. We consider time-division multiple access (TDMA), frequency-division multiple access (FDMA), and code-division multiple access (CDMA), ALOHA, and carrier sense multiple access (CSMA)-based wireless networks. In addition, game models for multiple access in dynamic spectrum access-based cognitive radio networks are reviewed. The major findings from the game models used for these different access schemes are highlighted. To this end, several of the key open research directions are outlined.

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A Survey on Machine-Learning Techniques in Cognitive Radios

TL;DR: The learning problem in cognitive radios (CRs) is characterized and the importance of artificial intelligence in achieving real cognitive communications systems is stated and the conditions under which each of the techniques may be applied are identified.
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Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

TL;DR: In this article, the authors comprehensively survey the recent advances of C-RANs, including system architectures, key techniques, and open issues, and discuss the system architectures with different functional splits and the corresponding characteristics.
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Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols

TL;DR: A comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies is provided.
Journal ArticleDOI

A Survey on Radio Resource Allocation in Cognitive Radio Sensor Networks

TL;DR: A survey of the recent advances in radio resource allocation in CR sensor networks (CRSNs) is presented and an insight into the related issues and challenges is provided, and future research directions are clearly identified.
References
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TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
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TL;DR: Weibull as discussed by the authors introduces evolutionary game theory, where ideas from evolutionary biology and rationalistic economics meet, emphasizing the links between static and dynamic approaches and non-cooperative game theory.
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Markov games as a framework for multi-agent reinforcement learning

TL;DR: A Q-learning-like algorithm for finding optimal policies and its application to a simple two-player game in which the optimal policy is probabilistic is demonstrated.
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Subjectivity and correlation in randomized strategies

TL;DR: This paper examined the consequences of basing mixed strategies on subjective random devices, i.e. devices on the probabilities of whose outcomes people may disagree (such as horse races, elections, etc.).
Journal ArticleDOI

Effective capacity: a wireless link model for support of quality of service

TL;DR: This paper proposes and develops a link-layer channel model termed effective capacity (EC), which first model a wireless link by two EC functions, namely, the probability of nonempty buffer, and the QoS exponent of a connection, and proposes a simple and efficient algorithm to estimate these EC functions.
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