Showing papers on "Cognitive network published in 2007"
TL;DR: A practical algorithm is proposed which allows cooperation between cognitive users in random networks and develops sufficient conditions for agility gain when the cognitive population is arbitrarily large.
Abstract: In cognitive radio networks, cognitive (unlicensed) users need to continuously monitor spectrum for the presence of primary (licensed) users. In this paper, we illustrate the benefits of cooperation in cognitive radio. We show that by allowing the cognitive users operating in the same band to cooperate we can reduce the detection time and thus increase the overall agility. We first consider a two-user cognitive radio network and show how the inherent asymmetry in the network can be exploited to increase the agility. We show that our cooperation scheme increases the agility of the cognitive users by as much as 35%. We then extend our cooperation scheme to multicarrier networks with two users per carrier and analyze asymptotic agility gain. In Part II of our paper [1], we investigate multiuser single carrier networks. We develop a decentralized cooperation protocol which ensures agility gain for arbitrarily large cognitive network population.
931 citations
Book•
21 Jun 2007
TL;DR: This book presents the experimental evidence of these 'scale-free networks' and provides students and researchers with a corpus of theoretical results and algorithms to analyse and understand these features.
Abstract: 1. Introduction to Graphs 2. Graph structure: Communities 3. Scale-invariance 4. The Origin of power law functions 5. Graph Generating Models 6. Networks in the cell 7. Ecological networks 8. Geophysical networks 9. Technological networks: Internet and WWW 10. Collaborative Relational and Cognitive Networks 11. Financial networks
770 citations
TL;DR: The trade-off between regulation and autonomy inherent in the design and performance of cognitive networks is examined through a simple example, which shows that the optimal amount of licensing is equal to the duty cycle of the traffic arrivals.
Abstract: Cognitive radios are promising solutions to the problem of overcrowded spectrum. In this article we explore the throughput potential of cognitive communication. Different interpretations of cognitive radio that underlay, overlay, and interweave the transmissions of the cognitive user with those of licensed users are described. Considering opportunistic communication as a baseline, we investigate the throughput improvements offered by the overlay methods. Channel selection techniques for opportunistic access such as frequency hopping, frequency tracking, and frequency coding are presented. The trade-off between regulation and autonomy inherent in the design and performance of cognitive networks is examined through a simple example, which shows that the optimal amount of licensing is equal to the duty cycle of the traffic arrivals
379 citations
TL;DR: The goal is to incorporate the results of the learning engine into a predicate calculus-based reasoning engine so that radios can remember lessons learned in the past and act quickly in the future.
Abstract: Cognitive radio offers the promise of intelligent radios that can learn from and adapt to their environment. To date, most cognitive radio research has focused on policy-based radios that are hard-coded with a list of rules on how the radio should behave in certain scenarios. Some work has been done on radios with learning engines tailored for very specific applications. This article describes a concrete model for a generic cognitive radio to utilize a learning engine. The goal is to incorporate the results of the learning engine into a predicate calculus-based reasoning engine so that radios can remember lessons learned in the past and act quickly in the future. We also investigate the differences between reasoning and learning, and the fundamentals of when a particular application requires learning, and when simple reasoning is sufficient. The basic architecture is consistent with cognitive engines seen in AI research. The focus of this article is not to propose new machine learning algorithms, but rather to formalize their application to cognitive radio and develop a framework from within which they can be useful. We describe how our generic cognitive engine can tackle problems such as capacity maximization and dynamic spectrum access.
340 citations
01 Apr 2007
TL;DR: The proposed book includes a set of research and survey articles featuring the recent advances in theory and applications of cognitive radio technology for the next generation (e.g., fourth generation) wireless communication networks.
Abstract: The proposed book includes a set of research and survey articles featuring the recent advances in theory and applications of cognitive radio technology for the next generation (e.g., fourth generation) wireless communication networks. Cognitive radio has emerged as a promising technology for maximizing the utilization of the limited radio bandwidth while accommodating the increasing amount of services and applications in the wireless networks. A cognitive radio transceiver is able to adapt to the dynamic radio environment and the network parameters to maximize the utilization of the limited radio resources while at the same time providing flexibility in wireless access. Development of cognitive radio technology has to deal with technical and practical considerations as well as regulatory requirements, and therefore, there is an increasing interest on this technology among the researchers and the spectrum policy makers. The contributed articles cover both the theoretical concepts (e.g., information-theoretic analysis) and system-level implementation issues. Therefore, the book provides a unified view on the state of the art of cognitive radio technology. The topics include information-theoretic analysis of cognitive radio systems, challenges and issues in designing cognitive radio systems, architectures and protocols for cognitive wireless networks, distributed adaptation and optimization methods, real-time spectrum sensing and channel allocation, cognitive machine learning techniques, interoperability and co-existence issues, spectrum awareness and dynamic channel selection, cross-layer optimization of cognitive radio systems, cognitive radio test-beds and hardware prototypes, regulatory issues on spectrum sharing, and applications of cognitive radio networks. The book starts with the essential background on cognitive radio techniques and systems (through one/two survey articles), and then it presents advanced level materials in a step-by-step fashion so that the readers can follow the book easily. The rich set of references in each of the articles will be invaluable to the researchers. The book is useful to both researchers and practitioners in this area. Also, it can be adopted as a graduate-level textbook for an advanced course on wireless communication networks.
339 citations
01 Jan 2007
TL;DR: This chapter introduces Adaptive, Aware, and Cognitive Radios, a methodology for enabling Cognitive Radio through Sensing, Awareness, and Measurements and discusses cross-layer Adaptation and Optimization.
Abstract: Preface. Chapter 1: Introducing Adaptive, Aware, and Cognitive Radios Bruce Fette. Chapter 2: Cognitive Networks Ryan W. Thomas, Daniel H. Friend, Luiz A. DaSilva, Allen B. MacKenzie. Chapter 3: Cognitive Radio Architecture Joseph Mitola III. Chapter 4: Software Defined Radio Architectures for Cognitive radios H. Arslan, H. celebi. Chapter 5: Value-Creation and Migration in Adaptive and Cognitive Radio Systems Keith E. Nolan, Francis J. Mullany, Eamonn Ambrose, Linda E. Doyle. Chapter 6: Codes and Games for Dynamic Spectrum Access Yiping Xing, Harikeshwar Kushwaha, K.P. Subbalakshmi, R. Chandramouli. Chapter 7: Efficiency and Coexistence Strategies for Cognitive Radio Sai Shankar N. Chapter 8: Enabling Cognitive Radio Through Sensing, Awareness, and Measurements H. Arslan, S. yarkan. Chapter 9: Spectrum Sensing for Cognitive Radio Applications H. Arslan, T. Yucek. Chapter 10: Location Information Management Systems for Cognitive Wireless Networks H. Arslan, H. Celebi. Chapter 11: OFDM for Cognitive Radio: Merits and Challenges H. Arslan, H. A. Mahmoud, T.Yucek. Chapter 12: UWB Cognitive Radio H. Arslan, M.E. Sahin. Chapter 13: Applications of Cognitive radio H. Arslan, S. Ahmed. Chapter 14: Cross-layer Adaptation and Optimization for Cognitive Radio H. Arslan, S. Yarkan. Index.
333 citations
TL;DR: The approach aims at improving the radio awareness with respect to stand alone scenario by using distributed detection theory for cooperative spectrum sensing in peer-to-peer cognitive networks.
Abstract: Cognitive radios is emerging in research laboratories as a promising wireless paradigm, which will integrate benefits of software defined radio with a complete aware communication behavior. To reach this goal many issues remain still open, such as powerful algorithms for sensing the external environment. This paper presents a further step in the direction of allowing cooperative spectrum sensing in peer-to-peer cognitive networks by using distributed detection theory. The approach aims at improving the radio awareness with respect to stand alone scenario as it is shown with theoretical and experimental results
258 citations
01 Apr 2007
TL;DR: This paper focuses on the implementation of the CPC information delivery and proposes the use of an on-demand CPC, which requires a significantly lower bit rate than the broadcast approach to achieve similar performance.
Abstract: This paper addresses the implementation of the cognitive pilot channel (CPC), which has been recently proposed as a solution to assist the mobile reconfigurable and cognitive terminals in heterogeneous wireless scenarios with different access networks available and varying spectrum allocations. The paper describes the operation of the CPC and the different approaches existing in the literature depending on how it is mapped onto specific radio resources. Then, it focuses on the implementation of the CPC information delivery and proposes the use of an on-demand CPC, which requires a significantly lower bit rate than the broadcast approach to achieve similar performance.
247 citations
01 Nov 2007
TL;DR: The approach to developing and implementing a cognitive radio, an intelligent software package controlling a software defined radio platform, and performing real-time control of the radio platform by the cognitive engine are discussed.
Abstract: Opportunistic unlicensed access to the (temporarily) unused frequency bands across the licensed radio spectrum is currently being investigated as a means to mitigate the spectrum scarcity. Such opportunistic access calls for the implementation of safeguards so that the ongoing licensed operations are not interfered with. Among different candidates, sensing-based access, where the secondary (unlicensed) users transmit if they sense the primary (licensed) band to be free, is particularly appealing due to its low deployment cost and its compatibility with legacy primary systems. Incorporating spectral awareness functionality into the radio transceivers is a major step towards the realization of the cognitive radios. In this paper performance of spectrum-sensing cognitive radios is studied under channel fading. In particular, it is shown that due to the uncertainty resulting from fading, local signal processing alone may be inadequate to meet the performance requirements. To remedy this issue, cooperation among secondary users is proposed and studied in this paper. Moreover, we characterize and study a tradeoff between local processing and cooperation, which should be balanced in order to maximize the spectrum utilization. Copyright © 2007 John Wiley & Sons, Ltd.
247 citations
Book•
01 Sep 2007
TL;DR: This book discusses distributed learning and Reasoning in Cognitive Networks: Methods and Design Decisions, Machine Learning for Cognitive Networks, and the Semantic Side of Cognitive Radio.
Abstract: Contributors. Foreword 1. Foreword 2. Preface. Acknowledgements. Introduction. Chapter 1: Biologically Inspired Networking. Chapter 2: The Role of Autonomic Networking in Cognitive Networks. Chapter 3: Adaptive Networks. Chapter 4: Self-Managing Networks. Chapter 5: Machine Learning for Cognitive Networks: Technology Assessment and Research Challenges. Chapter 6: Cross-Layer Design and Optimization in Wireless Networks. Chapter 7: Cognitive Radio Architecture. Chapter 8: The Wisdom of Crowds: Cognitive Ad hoc Networks. Chapter 9: Distributed Learning and Reasoning in Cognitive Networks: Methods and Design Decisions. Chapter 10: The Semantic Side of Cognitive Radio. Chapter 11: Security Issues in Cognitive Radio Networks. Chapter 12: Intrusion Detection in Cognitive Networks. Chapter 13: Erasure Tolerant Coding for Cognitive Radios. Index.
210 citations
TL;DR: This study unveils that location information can be used in cognitive wireless networks to optimize network performance by presenting some representative location-assisted network optimization applications.
Abstract: Location awareness is an essential characteristic of cognitive radios as well as networks. In this article a location awareness engine architecture is proposed for the realization of location awareness in cognitive radios and networks. The proposed architecture consists of location estimation and/or sensing, seamless positioning and interoperability, statistical learning and tracking, security and privacy, mobility management, and location-based applications. However, the focus of this article is on location-based applications where we demonstrate the utilization of location information in cognitive wireless networks by presenting some representative location-assisted network optimization applications such as location-assisted spectrum management, network planning and expansion, and handover. Our study unveils that location information can be used in cognitive wireless networks to optimize network performance. Possible solutions to the implementation issues are proposed, and the remaining open issues are also addressed.
24 Jun 2007
TL;DR: This paper investigates a simple wireless network, where one secondary transmitter has the option to relay traffic of the primary and discusses the advantage and limits of cognitive relaying.
Abstract: Cooperation is increasingly regarded as a key technology for tackling the challenges of a practical implementation of cognitive radio. In this paper, we first give a brief overview of the envisioned applications of cooperative technology to cognitive radio, distinguishing among cooperative sensing for detection of the primary activity, cooperative transmission between secondary nodes and cooperative transmission of primary traffic by secondary users (cognitive relaying). Then, we focus on the latter scenario and investigate a simple wireless network, where one secondary transmitter has the option to relay traffic of the primary. Assuming that the primary is oblivious to the presence of the secondary (thus excluding the possibility of spectrum leasing), the secondary transmitter optimizes transmission/ relaying parameters towards the goal of maximizing the rate towards the secondary receiver. Numerical results are provided in order to discuss the advantage and limits of cognitive relaying.
Book•
01 Jan 2007
TL;DR: Cognitive Radio is a Hybrid Technology Involving Software Defined Radio (SDR) As Applied to Spread Spectrum Communications and its possible functions include the ability of a Transceiver to Determine its Geographic Location and identify and authorize its user.
Abstract: What Is Cognitive Radio (CR)? Definition From WhatIs.com In Its Most Basic Form, CR Is A Hybrid Technology Involving Software Defined Radio (SDR) As Applied To Spread Spectrum Communications. Possible Functions Of Cognitive Radio Include The Ability Of A Transceiver To Determine Its Geographic Location, Identify And Authorize Its User, Encrypt Or Decrypt Signals, Sense Neighboring Wireless Devices In Operation, And Adjust Output Power And Modulation ... Apr 15th, 2019
Dissertation•
01 Jan 2007TL;DR: This dissertation provides the theory, design, and implementation of a cognitive engine, the enabling technology of cognitive radio, which provides a general framework to build and test cognitive engine algorithms and components such as sensing technology, optimization routines, and learning algorithms.
Abstract: This dissertation provides the theory, design, and implementation of a cognitive engine, the enabling technology of cognitive radio. A cognitive radio is a wireless communications device capable of sensing the environment and making decisions on how to use the available radio resources to enable communications with a certain quality of service. The cognitive engine, the intelligent system behind the cognitive radio, combines sensing, learning, and optimization algorithms to control and adapt the radio system from the physical layer and up the communication stack. The cognitive engine presented here provides a general framework to build and test cognitive engine algorithms and components such as sensing technology, optimization routines, and learning algorithms. The cognitive engine platform allows easy development of new components and algorithms to enhance the cognitive radio capabilities. It is shown in this dissertation that the platform can easily be used on a simulation system and then moved to a real radio system.
The dissertation includes discussions of both theory and implementation of the cognitive engine. The need for and implementation of all of the cognitive components is strongly featured as well as the specific issues related to the development of algorithms for cognitive radio behavior. The discussion of the theory focuses largely on developing the optimization space to intelligently and successfully design waveforms for particular quality of service needs under given environmental conditions. The analysis develops the problem into a multi-objective optimization process to optimize and trade-off of services between objectives that measure performance, such as bit error rate, data rate, and power consumption. The discussion of the multi-objective optimization provides the foundation for the analysis of radio systems in this respect, and through this, methods and considerations for future developments. The theoretical work also investigates the use of learning to enhance the cognitive engine’s capabilities through feed-back, learning, and knowledge representation.
The results of this work include the analysis of cognitive radio design and implementation and the functional cognitive engine that is shown to work in both simulation and on-line experiments. Throughout, examples and explanations of building and interfacing cognitive components to the cognitive engine enable the use and extension of the cognitive engine for future work.
10 Jun 2007
TL;DR: A joint power control and admission control procedure is suggested such that the priority of the primary users is always ensured and the effectiveness of the proposed schemes is demonstrated.
Abstract: While FCC proposes spectrum sharing between a legacy TV system and a cognitive radio network to increase spectrum utilization, one of the major concerns is that the interference from the cognitive radio network should not violate the QoS requirements of the primary users. In this paper, we consider the scenario where the cognitive radio network is formed by secondary users with low power personal/portable devices and when both systems are operating simultaneously. A power control problem is formulated for the cognitive radio network to maximize the energy efficiency of the secondary users and guarantee the QoS of both the primary users and the secondary users. The feasibility condition of the problem is derived and both centralized and distributed solutions are provided. Because the co-channel interference are from heterogeneous systems, a joint power control and admission control procedure is suggested such that the priority of the primary users is always ensured. The simulation results demonstrate the effectiveness of the proposed schemes.
24 Jun 2007
TL;DR: A cluster-based approach is proposed to form a mesh network in the context of cognitive radio scenario and a topology management algorithm is developed to optimize the cluster configuration with regard to the network topology.
Abstract: As the radio spectrum usage paradigm shifting from the traditional command and control allocation scheme to the open spectrum allocation scheme, wireless mesh networks meet new opportunities and challenges. The open spectrum allocation scheme has potential to provide those networks more capacity, and make them more flexible and reliable. However, the freedom brought by the new spectrum usage paradigm introduces spectrum management and network coordination challenges. In this paper, we study the network formation problem in cognitive radio based mesh networks. A cluster-based approach is proposed to form a mesh network in the context of cognitive radio scenario. Moreover, a topology management algorithm is developed to optimize the cluster configuration with regard to the network topology. The prominent feature of the proposed approach lies in the capability to adapt the cluster configuration to network and radio environment changes.
26 Dec 2007
TL;DR: A stochastic channel selection algorithm based on the learning automata techniques is proposed which adjusts the probability of selecting each available channel and converges to the e-optimal solution asymptotically.
Abstract: In this paper, we investigate the channel selection strategy for secondary users in cognitive radio networks. We claim that in order to avoid the costly channel switchings, a secondary user may desire an optimal channel which maximizes the probability of successful transmissions, rather than consistently adapting channels to the random environment. We propose a stochastic channel selection algorithm based on the learning automata techniques. This algorithm adjusts the probability of selecting each available channel and converges to the e-optimal solution asymptotically.
04 Dec 2007
TL;DR: This paper develops a fixed and a variable relay sensing scheme to improve the spectrum sensing capabilities of centralized cognitive radio networks and introduces a useful metric to measure the performance of fixed relay and variable relay schemes.
Abstract: Cognitive radio networks need to continuously monitor spectrum to detect the presence of the licensed users. In this paper, we have exploited spatial diversity in multiuser networks to improve the spectrum sensing capabilities of centralized cognitive radio (CR) networks. We develop a fixed and a variable relay sensing scheme. The fixed relay scheme employs a relay that is fixed in location to help the cognitive network base station detect the presence of the primary user. The variable relay sensing scheme employs cognitive users distributed at various locations as relays to sense data and to improve the detection capabilities. We theoretically prove that the proposed variable relay sensing scheme effectively reduces the average detection time which is also illustrated by an insightful example. Finally, we introduce a useful metric to measure the performance of fixed relay and variable relay schemes.
01 Aug 2007
TL;DR: This work considers a cognitive network: n pairs of cognitive transmitter and receiver wish to communicate simultaneously in the presence of a single primary transmitter-receiver link, and explores the optimal radius of the primary exclusive region, the region in which no secondary cognitive users may transmit, such that the outage constraint on the primary user is satisfied.
Abstract: Opportunistic secondary spectrum usage has the potential to dramatically increase spectral efficiency and rates of a network of secondary cognitive users. In this work we consider a cognitive network: n pairs of cognitive transmitter and receiver wish to communicate simultaneously in the presence of a single primary transmitter-receiver link. We assume each cognitive transmitter-receiver pair communicates in a realistic single-hop fashion, as cognitive links are likely to be highly localized in space. We first show that under an outage constraint on the primary link's capacity, provided that the density of the cognitive users is constant, the sum-rate of the n cognitive links scales linearly with n as n ? ?. This scaling is in contrast to the sum-rate scaling of ?n seen in multi-hop ad-hoc networks. We then explore the optimal radius of the primary exclusive region: the region in which no secondary cognitive users may transmit, such that the outage constraint on the primary user is satisfied. We obtain bounds that help the design of this primary exclusive region, outside of which cognitive radios may freely transmit.
26 Dec 2007
TL;DR: Results of feasibility studies on a software defined cognitive radio (SDCR) terminal that can access to the CWC are shown, which includes the configuration of the SDCR terminal and a measurement data for spectrum sensing period and reconfiguration period.
Abstract: This paper introduces the concept, features and architecture of a software defined cognitive radio system: Cognitive Wireless Clouds (CWC) that can realize user-centric and scalable network based on unique cognitive spectrum access, cross-network signaling, network optimization, and fast reconfiguration methods. Then, this paper shows results of feasibility studies on a software defined cognitive radio (SDCR) terminal that can access to the CWC. This includes the configuration of the SDCR terminal and a measurement data for spectrum sensing period and reconfiguration period by using software packages of W-CDMA and IEEE802.11a.
01 Mar 2007
TL;DR: This work investigates multiuser uplink scheduling with quality-of-service (QoS) provisioning for multiple cognitive users working at the same area with a primary user and proposes a satisfactory tradeoff between maximizing the system capacity, achieving fairness among cognitive users, and satisfying delay constraints to individual cognitive user.
Abstract: Scheduling schemes have been extensively studied in the framework of cellular networks, but the emergence of new system concepts, such as cognitive radio, brings this topic into the focus of research again. In this paper, we investigate multiuser uplink scheduling with quality-of-service (QoS) provisioning for multiple cognitive users working at the same area with a primary user. In this work, the proposed scheduling schemes attempt to provide a satisfactory tradeoff between maximizing the system capacity, achieving fairness among cognitive users, minimizing the interference to the primary user, and satisfying delay constraints to individual cognitive user. Simulation results are presented to evaluate the performance of the proposed scheduling schemes.
25 Jul 2007
24 Jun 2007
TL;DR: The proposed distributed cognitive network access scheme is shown to outperform state-of-the-art solutions in several multi-technology and multi-application scenarios, while at the same time achieving similar performance to application-specific omniscient schemes that are introduced in this paper as a benchmark.
Abstract: In this paper, we consider a scenario in which wireless users want to connect to the Internet using one of several available network access opportunities, possibly using different radio technologies. We propose a distributed cognitive network access scheme with the aim of providing the best quality of service with respect to both radio link and core network performance and user application requirements. Knowledge of the service quality experienced by active connections is shared, and prospective users use Fuzzy Logic techniques to process cross-layer communication quality metrics and to estimate the expected transport-layer performance. These estimates are compared to the Quality of Service requirements of the application, and Fuzzy Decision Making is used to choose the most suitable access opportunity. The proposed scheme is compared by simulation to commonly used algorithms as well as omniscient decision schemes in a variety of scenarios, and is shown to have superior performance and much better flexibility.
15 Apr 2007
TL;DR: The rationale for why cognitive dynamic systems need to study, and the issues involved in dynamic spectrum management and transmit-power control, which are of particular importance to cognitive radio are addressed.
Abstract: The first half of the paper addresses the rationale for why we need to study cognitive dynamic systems, with particular reference to two wireless applications: cogitative radio for communication, and cognitive radar for remote sensing. The second half of the paper discusses the issues involved in dynamic spectrum management and transmit-power control, which are of particular importance to cognitive radio. The iterative water-filling algorithm, in a noncooperative radio environment is discussed, and its virtues and limitations are highlighted.
01 Jan 2007
TL;DR: It is shown that an iterative algorithm for channel scheduling and power allocation can be implemented, which converges to a pure strategy Nash equilibrium solution, i.e., a deterministic choice of channels and transmission powers for all users.
Abstract: In this paper, we propose a game theoretic solution for joint channel selection and power allocation in cognitive radio networks. Our proposed algorithm enforces cooperation among nodes in an effort to reduce the overall energy consumption in the network. For designing the power control, we consider both the case in which no transmission power constraints are imposed, as well as the more practical case, in which the maximum transmission power is limited. We show that an iterative algorithm for channel scheduling and power allocation can be implemented, which converges to a pure strategy Nash equilibrium solution, i.e., a deterministic choice of channels and transmission powers for all users. Our simulation results also show that, while both channel allocation and power control can independently improve the system performance, there is a significant gain for the joint algorithm.
24 Jun 2007
TL;DR: This paper presents a cognitive network approach to achieving the objectives of power and spectrum management as a two phased non-cooperative game and uses the properties of potential game theory to ensure the existence of, and convergence to, a desirable Nash equilibrium.
Abstract: Wireless topology control is the process of structuring the connectivity between network nodes to achieve some network-wide goal This paper presents a cognitive network approach to achieving the objectives of power and spectrum management We cast the problem as a two phased non-cooperative game and use the properties of potential game theory to ensure the existence of, and convergence to, a desirable Nash equilibrium Although this is a multi-objective optimization and the spectrum management problem is NP-hard, this selfish cognitive network constructs a topology that minimizes the maximum transmission power while simultaneously using, on average, less than 12% extra spectrum, as compared to the ideal solution
01 Jan 2007
TL;DR: The distributed constraint optimization problem (DCOP) in cognitive radio networks is addressed and the effectiveness of DCOP algorithms to find the optimal radio resource assignment through communications between distributed agents is studied.
Abstract: This paper investigates cooperative radio resource management for multiple cognitive radio networks in interference environments. The objective of this research is to manage shared radio resources fairly among multiple non- cooperative cognitive radio networks to optimize the overall performance. We emphasize the underlying predictability of network conditions and promote management solutions tailored to different interference environments. A multi-agent-system- based approach is proposed to achieve information sharing and decision distribution among multiple cognitive radio networks in a distributed manner. We address the distributed constraint optimization problem (DCOP) in cognitive radio networks and study the effectiveness of DCOP algorithms to find the optimal radio resource assignment through communications between distributed agents.
01 Dec 2007
TL;DR: Several key architectural issues for cognitive radio engine based on Neural Network are discussed, including knowledge base information model and learning model Neural Network design.
Abstract: This paper introduces the research of cognitive engine and application of artificial intelligence techniques in cognitive radio The limitation of CR engine based on GA is analyzed, propose for improvement is proposed The decision maker of CR engine should consider both the changeable factors and the unchangeable factors such as cost, bandwidth, signal rate and ARQ Based on Neural Network, the method of evaluating and learning best decision is proposed Several key architectural issues for cognitive radio engine based on Neural Network are discussed, including knowledge base information model and learning model Neural Network design
24 Jun 2007
TL;DR: An architecture for adaptive cognitive radio networks based on the concept of a "global control plane" using a predetermined common coordination channel for spectrum etiquette, network establishment and adaptation to changing interference environments is presented.
Abstract: This paper presents an architecture for adaptive cognitive radio networks based on the concept of a "global control plane". The proposed control architecture uses a predetermined common coordination channel for spectrum etiquette, network establishment and adaptation to changing interference environments. The focus of this work is on design and evaluation of three key components of the control protocol - bootstrapping, discovery and naming/addressing. The bootstrapping protocol uses beacons to inform neighboring nodes about a node's PHY/MAC capabilities and current status. The network discovery protocol helps nodes to obtain a global view of reachability and end-to-end paths in the network by exchanging and propagating local link states. Further, nodes obtain their IP addresses and perform name to network address translations using a distributed naming/addressing scheme. An ns2 simulation model of the cognitive radio network with global control has been developed and used to evaluate performance in terms of network setup time, control overhead and achievable data throughput.
TL;DR: An overview of current beyond 3G trends in3GPP is given, and a new 3GPP study item on network composition is introduced, addressing a dynamic, generic establishment of control-plane interworking between the heterogeneous network types of today.
Abstract: The 3GPP network specification is currently undergoing major updates toward beyond 3G. The evolved 3GPP network will support interworking with multiple including non-3GPP - radio access networks, and support mobility between them. It will furthermore support personal area networks and moving networks. Generally, 3GPP is moving in the direction of an all-IP network. This article gives an overview of current beyond 3G trends in 3GPP, and particularly introduces a new 3GPP study item on network composition. The concept of network composition was developed by the EU project Ambient Networks. Whereas 3GPP until now assumes static networking relations, network composition addresses a dynamic, generic establishment of control-plane interworking between the heterogeneous network types of today, such as 3GPP core networks, non-3GPP operator networks, heterogeneous access networks, and personal area networks