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Journal ArticleDOI

Location-Oriented Evolutionary Games for Price-Elastic Spectrum Sharing

TL;DR: A model where geographic information, including licensed areas of primary users (PUs) and locations of secondary users (SUs), plays an important role in the spectrum sharing system and the existence and uniqueness of the evolutionary stable strategy quota vector of each PU is proved.
Abstract: For a spectrum sharing system using economic approaches, conventional models without geographic considerations are oversimplified. In this paper, we develop a model where geographic information, including licensed areas of primary users (PUs) and locations of secondary users (SUs), plays an important role in the spectrum sharing system. We consider a multi-price policy and the pricing power of non-cooperative PUs in multiple geographic areas. Meanwhile, the value assessment of a channel is price-related and the demand from the SUs is price-elastic. To maximize the payoffs of the PUs, we propose a unique quota transaction process. By applying an evolutionary procedure defined as replicator dynamics, we prove the existence and uniqueness of the evolutionary stable strategy quota vector of each PU, which leads to the optimal payoff for each PU selling channels without reserve. In the scenario of selling channels with reserve, we predict the channel prices for the PUs leading to the optimal supplies of the PUs and hence the optimal payoffs. Furthermore, we introduce a grouping mechanism to simplify the process. In our simulation, the effectiveness of the learning processes designed for the two scenarios is verified and our spectrum sharing scheme is shown efficient in utilizing the frequency resources.
Citations
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Journal ArticleDOI
TL;DR: The SpecPSO is proposed for optimizing handovers using supervised machine learning technique for performing dynamic handover by adapting to the environment and make smart decisions compared to the traditional cooperative spectrum sensing (CSS) techniques.
Abstract: Cognitive communication model perform the investigation and surveillance of spectrum in cognitive radio networks abetment in advertent primary users (PUs) and in turn help in allocation of transmission space for secondary users (SUs). In effective performance of regulation of wireless channel handover strategy in cognitive computing systems, new computing models are desired in operating set of tasks to process business model, and interact naturally with humans or machine rather being programmed. Cognitive wireless network are trained via artificial intelligence (AI) and machine learning (ML) algorithms for dynamic processing of spectrum handovers. They assist human experts in making enhanced decisions by penetrating into the complexity of the handovers. This paper focuses on learning and reasoning features of cognitive radio (CR) by analyzing primary user (PU) and secondary user (SU) data communication using home location register (HLR) and visitor location register (VLR) database respectively. The SpecPSO is proposed for optimizing handovers using supervised machine learning technique for performing dynamic handover by adapting to the environment and make smart decisions compared to the traditional cooperative spectrum sensing (CSS) techniques.

287 citations


Cites background from "Location-Oriented Evolutionary Game..."

  • ...The artificial neural network (ANN) topology is trained with two types of learning algorithms [25] called supervised and unsupervised....

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Posted Content
TL;DR: The state-of-the-art of relevant techniques are described, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum and energy-efficient communications in dynamic wireless environments.
Abstract: The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.

56 citations


Cites background from "Location-Oriented Evolutionary Game..."

  • ...In [112], an evolutionary game is applied to modeling the pricing competitions among the primary users when the demands of the secondary users are related to channel prices....

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Book ChapterDOI
01 Jan 2019
TL;DR: It is found that cooperative spectrum sensing is not only advantageous but is also essential to avoid interference with any primary network users, and a dynamic technique called CUSUM algorithm is devised.
Abstract: Cognitive radio systems require the absorption of cooperative spectrum sensing among cognitive network users to increase the reliability of detection. We have found that cooperative spectrum sensing is not only advantageous but is also essential to avoid interference with any primary network users. Interference by licensed users becomes a chief concern and issue, which affects primary as well as secondary users leading to restrictions in spectrum sensing in cognitive radios. Cognitive radio spectrum sensing ability to identify and make use of vacant spaces in the spectrum without causing any interference to the primary user is elaborately studied. An overview about spectrum sensing is given, and an effective system model based on conventional and cooperative sensing model is proposed. It is reviewed along with various cooperative handover factors, and a dynamic technique called CUSUM algorithm is devised. The efficiency of the proposed cooperative CUSUM spectrum sensing algorithm performs better than existing optimal rules based on a single observation spectrum sensing techniques under cooperative networks.

46 citations

Journal ArticleDOI
TL;DR: It is proved that the so-called distributed replicator equation (DRE) converges to an optimal global outcome by means of the local-information exchange subject to the topological constraints of the graph.
Abstract: In a multi-agent framework, distributed optimization problems are generally described as the minimization of a global objective function, where each agent can get information only from a neighborhood defined by a network topology. To solve the problem, this work presents an information-constrained strategy based on population dynamics, where payoff functions and tasks are assigned to each node in a connected graph. We prove that the so-called distributed replicator equation (DRE) converges to an optimal global outcome by means of the local-information exchange subject to the topological constraints of the graph. To show the application of the proposed strategy, we implement the DRE to solve an economic dispatch problem with distributed generation. We also present some simulation results to illustrate the theoretic optimality and stability of the equilibrium points and the effects of typical network topologies on the convergence rate of the algorithm.

20 citations

Journal ArticleDOI
TL;DR: This article provides a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms, and elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects.
Abstract: The exponential growth of Internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to different potential security and privacy issues, requiring protection mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based methods have frequently been proposed to address those issues. In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms. In particular, we elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects, including ML based cognitive radio networks (CRNs), ML based database assisted SS networks, ML based LTE-U networks, ML based ambient backscatter networks, and other ML based SS solutions. We also present security issues from the physical layer and corresponding defending strategies based on ML algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and privacy issues. Finally, extensive discussions on open challenges for ML based SS are also given. This comprehensive review is intended to provide the foundation for and facilitate future studies on exploring the potential of emerging ML for coping with increasingly complex SS and their security problems.

10 citations

References
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Book
01 Jan 2005
TL;DR: In this paper, the authors propose a multiuser communication architecture for point-to-point wireless networks with additive Gaussian noise detection and estimation in the context of MIMO networks.
Abstract: 1. Introduction 2. The wireless channel 3. Point-to-point communication: detection, diversity and channel uncertainty 4. Cellular systems: multiple access and interference management 5. Capacity of wireless channels 6. Multiuser capacity and opportunistic communication 7. MIMO I: spatial multiplexing and channel modeling 8. MIMO II: capacity and multiplexing architectures 9. MIMO III: diversity-multiplexing tradeoff and universal space-time codes 10. MIMO IV: multiuser communication A. Detection and estimation in additive Gaussian noise B. Information theory background.

8,084 citations

Book
01 Jan 1998
TL;DR: Fudenberg and Levine as discussed by the authors developed an alternative explanation that equilibrium arises as the long-run outcome of a process in which less than fully rational players grope for optimality over time.
Abstract: In economics, most noncooperative game theory has focused on equilibrium in games, especially Nash equilibrium and its refinements. The traditional explanation for when and why equilibrium arises is that it results from analysis and introspection by the players in a situation where the rules of the game, the rationality of the players, and the players' payoff functions are all common knowledge. Both conceptually and empirically, this theory has many problems. In The Theory of Learning in Games Drew Fudenberg and David Levine develop an alternative explanation that equilibrium arises as the long-run outcome of a process in which less than fully rational players grope for optimality over time. The models they explore provide a foundation for equilibrium theory and suggest useful ways for economists to evaluate and modify traditional equilibrium concepts.

3,254 citations

Book
14 Aug 1995
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.
Abstract: This text introduces current evolutionary game theory -- where ideas from evolutionary biology and rationalistic economics meet -- emphasizing the links between static and dynamic approaches and noncooperative game theory. Much of the text is devoted to the key concepts of evolutionary stability and replicator dynamics. The former highlights the role of mutations and the latter the mechanisms of selection. Moreover, set-valued static and dynamic stability concepts, as well as processes of social evolution, are discussed. Separate background chapters are devoted to noncooperative game theory and the theory of ordinary differential equations. There are examples throughout as well as individual chapter summaries. Because evolutionary game theory is a fast-moving field that is itself branching out and rapidly evolving, Jorgen Weibull has judiciously focused on clarifying and explaining core elements of the theory in an up-to-date, comprehensive, and self-contained treatment. The result is a text for second-year graduate students in economic theory, other social sciences, and evolutionary biology. The book goes beyond filling the gap between texts by Maynard-Smith and Hofbauer and Sigmund that are currently being used in the field. Evolutionary Game Theory will also serve as an introduction for those embarking on research in this area as well as a reference for those already familiar with the field. Weibull provides an overview of the developments that have taken place in this branch of game theory, discusses the mathematical tools needed to understand the area, describes both the motivation and intuition for the concepts involved, and explains why and how it is relevant to economics.

3,119 citations

Book
16 Oct 1992
TL;DR: This important study shows that an understanding of product differentiation is crucial to understanding how modern market economies function and that differentiated markets can be analyzed using discrete choice models of consumer behavior.
Abstract: Product differentiation - in quality, packaging, design, color, and style - has an important impact on consumer choice It also provides a rich source of data that has been largely unexplored because there has been no generally accepted way to model the information available This important study shows that an understanding of product differentiation is crucial to understanding how modern market economies function and that differentiated markets can be analyzed using discrete choice models of consumer behavior It provides a valuable synthesis of existing, often highly technical work in both differentiated markets and discrete choice models and extends this work to establish a coherent theoretical underpinning for research in imperfect competition The discrete choice approach provides an ideal framework for describing the demands for differentiated products and can be used for studying most product differentiation models in the literature By introducing extra dimensions of product heterogeneity, the framework also provides richer models of firm location Discrete Choice Theory of Product Differentiation introduces students and researchers to the field, starting at the beginning and moving through to frontier research The first four chapters detail the consumer-theoretic foundations underlying choice probability systems (including an overview of the main models used in the psychological theory of choice), while the next four chapters apply the probabilistic choice approach to oligopoly models of product differentiation, product selection, and location choice The final chapter suggests various extensions of the models presented as well topics for further research

2,280 citations

Journal ArticleDOI
TL;DR: An overview of major challenges in two-tier networks is provided and some pricing schemes for different types of device relaying are proposed.
Abstract: In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular bandwidth and all communications take place through the base stations. In this article, we envision a two-tier cellular network that involves a macrocell tier (i.e., BS-to-device communications) and a device tier (i.e., device-to-device communications). Device terminal relaying makes it possible for devices in a network to function as transmission relays for each other and realize a massive ad hoc mesh network. This is obviously a dramatic departure from the conventional cellular architecture and brings unique technical challenges. In such a two-tier cellular system, since the user data is routed through other users? devices, security must be maintained for privacy. To ensure minimal impact on the performance of existing macrocell BSs, the two-tier network needs to be designed with smart interference management strategies and appropriate resource allocation schemes. Furthermore, novel pricing models should be designed to tempt devices to participate in this type of communication. Our article provides an overview of these major challenges in two-tier networks and proposes some pricing schemes for different types of device relaying.

981 citations


"Location-Oriented Evolutionary Game..." refers background in this paper

  • ...Our demand model is applicable to both oligopoly and monopoly markets, two common market structures for spectrum sharing [18]–[20]....

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