Showing papers on "Cognitive network published in 2010"

Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret

Abstract: The problem of distributed learning and channel access is considered in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and are estimated using sensing decisions. There is no explicit information exchange or prior agreement among the secondary users. We propose policies for distributed learning and access which achieve order-optimal cognitive system throughput (number of successful secondary transmissions) under self play, i.e., when implemented at all the secondary users. Equivalently, our policies minimize the regret in distributed learning and access. We first consider the scenario when the number of secondary users is known to the policy, and prove that the total regret is logarithmic in the number of transmission slots. Our distributed learning and access policy achieves order-optimal regret by comparing to an asymptotic lower bound for regret under any uniformly-good learning and access policy. We then consider the case when the number of secondary users is fixed but unknown, and is estimated through feedback. We propose a policy in this scenario whose asymptotic sum regret which grows slightly faster than logarithmic in the number of transmission slots.

Cognitive femtocell networks: an overlay architecture for localized dynamic spectrum access [Dynamic Spectrum Management]

Abstract: We define and propose a femtocell-based cognitive radio architecture for enabling multitiered opportunistic access in next-generation broadband wireless systems. This architecture combines the conventional femtocell idea with an infrastructure-based overlay cognitive network paradigm. The cognitive femtocell concept leads to simpler and easier proliferation of cognitive radio into practical systems. We highlight the drawbacks and advantages of the proposed network structure with a discussion on research directions for cognitive femtocell architecture. We also provide experimental results to illustrate a general proof of concept for this new modality.

Power Control and Channel Allocation in Cognitive Radio Networks with Primary Users' Cooperation

Abstract: We consider a point-to-multipoint cognitive radio network that shares a set of channels with a primary network. Within the cognitive radio network, a base station controls and supports a set of fixed-location wireless subscribers. The objective is to maximize the throughput of the cognitive network while not affecting the performance of primary users. Both downlink and uplink transmission scenarios in the cognitive network are considered. For both scenarios, we propose two-phase mixed distributed/centralized control algorithms that require minimal cooperation between cognitive and primary devices. In the first phase, a distributed power updating process is employed at the cognitive and primary nodes to maximize the coverage of the cognitive network while always maintaining the constrained signal to interference plus noise ratio of primary transmissions. In the second phase, centralized channel assignment is carried out within the cognitive network to maximize its throughput. Numerical results are obtained for the behaviors and performance of our proposed algorithms.

Iris: an architecture for cognitive radio networking testbeds

Abstract: Iris is a software architecture for building highly reconfigurable radio networks. It has formed the basis for a wide range of dynamic spectrum access and cognitive radio demonstration systems presented at a number of international conferences between 2007 and 2010. These systems have been developed using heterogeneous processing platforms including general-purpose processors, field-programmable gate arrays and the Cell Broadband Engine. Focusing on runtime reconfiguration, Iris offers support for all layers of the network stack and provides a platform for the development of not only reconfigurable point-to-point radio links but complete networks of cognitive radios. This article provides an overview of Iris, presenting the unique features of the architecture and illustrating how it can be used to develop a cognitive radio testbed.

Securing cognitive radio networks

Abstract: Cognitive radio is a promising technology aiming to improve the utilization of the radio electromagnetic spectrum. A cognitive radio device uses general purpose computer processors that run radio applications software to perform signal processing. The use of this software enables the device to sense and understand its environment and actively change its mode of operation based on its observations. Unfortunately, this solution entails new security challenges. Our objective in this paper is to analyze the security issues of the main recent developments and architectures of cognitive radio networks. We present vulnerabilities inherent to those systems, identify novel types of abuse, classify attacks, and analyze their impact on the operation of cognitive radio-based systems. Moreover, we discuss and propose security solutions to mitigate such threats. Copyright © 2010 John Wiley & Sons, Ltd. Cognitive radio emerges as a promising technology to deal with the scarcity of radio electromagnetic spectrum. Cognitive radio devices run the radio applications software by means of which they are able to sense and understand their environment and change their mode of operation according to their observations. In this paper we present an analysis of the vulnerabilities inherent to these systems, identify the potential threats to cognitive radio networks and propose a set of countermeasures to mitigate them. Copyright © 2010 John Wiley & Sons, Ltd.

Standardization and research in cognitive and dynamic spectrum access networks: IEEE SCC41 efforts and other activities

Abstract: Spectrum crowding, spectrum management, quality of service, and user support are the topics of vigorous research in the cognitive and dynamic spectrum access network communities. As research matures, standardization provides a bridge between research results, implementation, and widespread deployment of such networks. This article reports recent developments within the IEEE Standardization Coordinating Committee 41, "Dynamic Spectrum Access Networks." It outlines possible future standardization topics for IEEE SCC41, in the framework of other related standardization activities, and discusses open research issues that present future challenges for the standardization community.

Brain-computer interfacing based on cognitive control.

Abstract: Objective: Brain– computer interfaces (BCIs) translate deliberate intentions and associated changes in brain activity into action, thereby offering patients with severe paralysis an alternative means of communication with and control over their environment. Such systems are not available yet, partly due to the high performance standard that is required. A major challenge in the development of implantable BCIs is to identify cortical regions and related functions that an individual can reliably and consciously manipulate. Research predominantly focuses on the sensorimotor cortex, which can be activated by imagining motor actions. However, because this region may not provide an optimal solution to all patients, other neuronal networks need to be examined. Therefore, we investigated whether the cognitive control network can be used for BCI purposes. We also determined the feasibility of using functional magnetic resonance imaging (fMRI) for noninvasive localization of the cognitive control network. Methods: Three patients with intractable epilepsy, who were temporarily implanted with subdural grid electrodes for diagnostic purposes, attempted to gain BCI control using the electrocorticographic (ECoG) signal of the left dorsolateral prefrontal cortex (DLPFC). Results: All subjects quickly gained accurate BCI control by modulation of gamma-power of the left DLPFC. Prelocalization of the relevant region was performed with fMRI and was confirmed using the ECoG signals obtained during mental calculation localizer tasks. Interpretation: The results indicate that the cognitive control network is a suitable source of signals for BCI applications. They also demonstrate the feasibility of translating understanding about cognitive networks derived from functional neuroimaging into clinical applications. ANN NEUROL 2010;67:809 – 816

Distributed Power Control for Cognitive User Access based on Primary Link Control Feedback

Abstract: We venture beyond the "listen-before-talk" strategy that is common in many traditional cognitive radio access schemes. We exploit the bi-directional nature of most primary communication systems. By intelligently choosing their transmission parameters based on the observation of primary user (PU) communications, secondary users (SUs) in a cognitive network can achieve higher spectrum usage while limiting their interference to the PU. Specifically, we propose that the SUs listen to the PU's feedback channel to assess their interference on the primary receiver (PU-Rx), and adjust radio power accordingly to satisfy the PU's interference constraint. We investigate both centralized and distributed power control algorithms without active PU cooperation. We show that the PU feedback information inherent in many two-way primary systems can be used as important coordination signal among multiple SUs to distributively achieve a joint performance guarantee on the primary receiver's quality of service.

Cognitive Radio Networks: Architectures, Protocols, and Standards

Abstract: While still in the early stages of research and development, cognitive radio is a highly promising communications paradigm with the ability to effectively address the spectrum insufficiency problem. Written by those pioneering the field, Cognitive Radio Networks: Architectures, Protocols, and Standards offers a complete view of cognitive radioincluding introductory concepts, fundamental techniques, regulations, standards, system implementations, and recent developments. From the physical layer to protocol layer, world-class editors provide comprehensive technical and regulatory guidance across cognitive radio, dynamic spectrum access, and cognitive wireless networks. The book examines routing, Medium Access Control (MAC), cooperation schemes, resource management, mobility, and game theory approach. Organized into three sections for ease of reference: Introduces and addresses the issues in the physical layer, including sensing, capacity, and power control Examines issues in the protocol layers and supplies practical solutions Explores applications, including cognitive radio systems Complete with illustrative figures that allow for complete cross-referencing, this authoritative reference provides readers with the understanding of the fundamental concepts, principles, and framework of cognitive wireless systems needed to initiate the development of future-generation wireless systems and networks.

Cognitive Radio Networking and Security: A Game-Theoretic View

Abstract: With the rapid growth of new wireless devices and applications over the past decade, the demand for wireless radio spectrum is increasing relentlessly. The development of cognitive radio networking provides a framework for making the best possible use of limited spectrum resources, and it is revolutionising the telecommunications industry. This book presents the fundamentals of designing, implementing, and deploying cognitive radio communication and networking systems. Uniquely, it focuses on game theory and its applications to various aspects of cognitive networking. It covers in detail the core aspects of cognitive radio, including cooperation, situational awareness, learning, and security mechanisms and strategies. In addition, it provides novel, state-of-the-art concepts and recent results. This is an ideal reference for researchers, students and professionals in industry who need to learn the applications of game theory to cognitive networking.

Overcoming the Sensing-Throughput Tradeoff in Cognitive Radio Networks

Abstract: In a cognitive radio network that employs opportunistic spectrum access, the users are allowed to access a frequency band only when it is not being used by licensed users. Hence, spectrum sensing is of utmost importance, in order to efficiently exploit the unused spectrum and effectively protect the quality of service of licensed networks. For this reason, a time slot has been allocated for spectrum sensing at the beginning of each frame in the systems proposed so far. During this slot, data transmission is prohibited, which results in the sensing-throughput tradeoff problem. In this paper, we propose a novel cognitive radio system that overcomes the sensing-throughput tradeoff by performing spectrum sensing and data transmission at the same time, which maximizes both the sensing time and the throughput of the cognitive radio network. We introduce a novel receiver and frame structure for cognitive radio and analytically prove that the proposed cognitive radio system exhibits improved throughput and sensing capabilities. Finally, simulation results are provided to validate our theoretical analysis.

Improving reliability of safety applications in vehicle ad hoc networks through the implementation of a cognitive network

Abstract: Researchers have suggested Vehicular Ad hoc Networks as a way to enable car to car communications and to allow for the exchange of safety and other types of information among cars. The Wireless Access in Vehicular Environments (WAVE) protocol stack is standardized by the IEEE, and it allocates spectrum for vehicular communication. In our work we prove that it does not provide sufficient spectrum for reliable exchange of safety information. To alleviate this problem, we present a system that employs cognitive network principles to increase the spectrum allocated to the control channel (CCH) by the WAVE protocols, where all safety information is transmitted. To accomplish this objective, the proposed system relies on sensed data sent by the cars to road side units that in turn forward the aggregated data to a processing unit. The processing unit infers data contention locations and generates spectrum schedules to dispatch to the passing cars. Analysis and simulation results indicate the effectiveness of the system in improving data delivery in vehicular networks and thus increasing the reliability of safety applications.

Docitive networks: an emerging paradigm for dynamic spectrum management [Dynamic Spectrum Management]

Abstract: Prime design goals for next-generation wireless networks to support emerging applications are spectral efficiency and low operational cost. Among a gamut of technical solutions, cognitive approaches have long been perceived as a catalyst for the above goals by facilitating the coexistence of primary and secondary users by means of efficient dynamic spectrum management. While most available techniques today are essentially opportunistic in nature, a truly cognitive device needs to exhibit a certain degree of intelligence to draw optimum decisions based on prior observations and anticipated actions. Said intelligence however, comes along with high complexity and poor convergence, which currently prevents any viable deployment of cognitive networks. We thus introduce an emerging and largely unexplored concept of docitive networks, where nodes effectively teach other nodes with the prime aims of reducing cognitive complexity, speeding up the learning process, and drawing better and more reliable decisions. To this end, we review some important concepts borrowed from the machine learning community for both centralized and decentralized systems, in order to position the emerging docitive with known cognitive approaches. Finally, we validate introduced concepts in the context of a primary digital television system dynamically coexisting with IEEE 802.22 secondary networks. For this scenario, we demonstrate the superiority of various unprecedented docitive over known opportunistic/cognitive algorithms.

Transport Layer Protocols for Cognitive Networks

Abstract: The cognitive radio networks or CogNets poses several new challenges to the transport layer protocols, because of many unique features of cognitive radio based devices used to build them. CogNets not only have inherited all features of wireless networks, but also their link connections are intermittent and discontinuous. Exiting transport layer protocols are too slow to respond quickly for utilizing available link capacity. Furthermore, existing self-timed transport layer protocols are neither designed for nor able to provide efficient reliable end-to-end transport service in CogNets, where wide round trip delay variations naturally occur. We identify (i) requirements of protocols for the transport layer of CogNets, (ii) propose a generic architecture for implementing a family of protocols that fulfill desired requirements, (iii) design, implement, and evaluate a family of best-effort transport protocols for serving delay-tolerant applications. Results obtained from ns-2 network simulator show that the proposed protocols have potential for significantly improving end-to-end throughput. For instance, at 1% and 5% packet loss rates one of the proposed protocol has shown about 21% and 95% increase in end-to-end throughput for file transfer application.

Towards a Real-Time Cognitive Radio Network Testbed: Architecture, Hardware Platform, and Application to Smart Grid

Abstract: A real-time cognitive radio network testbed is being built. This is the first paper to capture the overall picture of this project. Project scope and philosophy, design architecture, hardware platform, and key algorithms are reported. The use of cognitive radio network for smart grid is for the first time proposed in this paper. This unique testbed is ideal for such purpose.

Voice-Service Capacity Analysis for Cognitive Radio Networks

Abstract: In this paper, quality-of-service (QoS) provisioning for voice service in cognitive radio networks is considered. As voice traffic is sensitive to delay, the presence of primary users and the requirement that secondary users should not interfere with them pose many challenges for QoS support in cognitive radio networks. Two cognitive medium-access control (MAC) schemes are proposed in this paper for secondary voice users to access the available channel. One is the contention-based scheme, and the other is the contention-free scheme. An analytical model is developed to obtain the voice-service capacity (i.e., the maximum number of secondary voice users that can be supported with QoS guarantee) of the two proposed schemes, taking into account the impact of the primary users' activity. Both independent and correlated channel busy/idle state models for primary activity are considered. The analytical model is validated by simulations. The analytical results will be useful to support voice service in cognitive radio networks.

Optimal Defense against Jamming Attacks in Cognitive Radio Networks Using the Markov Decision Process Approach

Abstract: Cognitive radio technology has become a promising approach to increase the efficiency of spectrum utilization. Since cognitive radio users are vulnerable to malicious attacks, security countermeasures are crucial to the successful deployment of cognitive radio networks in the future. In this paper, we focus on defending against the jamming attack, one of the major threats to cognitive radio networks, where several malicious attackers intend to jam the secondary user's communication link by injecting interference. We model this scenario into a jamming game, and derive the optimal strategy through the Markov decision process approach. Furthermore, a learning scheme is proposed for the secondary user to observe the wireless environment and estimate parameters such as primary users' access pattern and the number of attackers. Finally, simulation results are presented to verify the performance.

The Cognitive Packet Network

Abstract: Current and future multimedia networks require connections under specific quality of service (QoS) constraints which can no longer be provided by the best-effort Internet. Therefore, ‘smarter’ networks have been proposed in order to cover this need. The cognitive packet network (CPN) is a routing protocol that provides QoS-driven routing and performs self-improvement in a distributed manner, by learning from the experience of special packets, which gather on-line QoS measurements and discover new routes. The CPN was first introduced in 1999 and has been used in several applications since then. Here we provide a comprehensive survey of its variations, applications and experimental performance evaluations.

Transformation of Cognitive Maps

Abstract: Cognitive maps (CMs), fuzzy cognitive maps (FCMs), and dynamical cognitive networks (DCNs) are related tools for modeling the cognition of human beings and facilitating machine inferences accordingly. FCMs extend CMs, and DCNs extend FCMs. Domain experts often face the challenge that CMs/FCMs are not sufficiently capable in many applications and that DCNs are too complex. This paper presents a simplified DCN (sDCN) that extends the modeling capability of FCM/CM, yet maintains simplicity. Additionally, this paper proves that there exists a theoretical equivalence among models in the cognitive map family of CMs, FCMs, and sDCNs. It shows that every sDCN can be represented by an FCM or a CM, and vice versa; similarly, every FCM can be represented by a CM, and vice versa. The result shows that CMs, FCMs, and sDCNs are a family of cognitive models that differs from many extended models. This paper also provides a constructive approach to transforming one cognitive map model into other cognitive map models in the family. Therefore, domain experts are able to model applications with more descriptive sDCNs and leave theoretical analysis to the simpler CM forms. The existence of theoretical transformation links among the models provides strong support for their theoretical analysis and flexibility in their applications.

Designing and deploying a building-wide cognitive radio network testbed

Abstract: Wireless communication technology is constantly advancing with the primary objective being to improve the quality of service for the end user. Cognitive radio is a technology capable of advancing wireless communications to the next generation of intelligent devices. Integrating cognition into wireless applications such as dynamic spectrum access, radio resource management, wireless distributed computing, and even traditional protocol stacks has already been shown to provide benefits related to the communications quality of service. The majority of cognitive radio related research has been limited to theoretical frameworks and simulations or in a few cases, demonstrating prototype DSA devices on a small scale. In order to continue advancing in this area, larger-scale experiments that are reproducible and able to be moved beyond theoretical simulations are required. Virginia Tech has built a testbed for software-defined and cognitive radio related research for the purpose of rapid next-generation communication system prototyping using a medium scale size network of flexible wireless nodes. In this article we present the details of the development, design decision rationale, and deployment of this testbed in hopes that it will be both used by the research community, and duplicated and improved in order to further the development of the many different facets of cognitive radio research.

A Probabilistic Model of Carrier Sensing Based Cognitive Radio

Abstract: We propose a probabilistic model based on stochastic geometry to analyze cognitive radio in a mobile ad hoc network using carrier sensing multiple access. Analytical results are derived on the impact of the interaction between primary and secondary users on their medium access probability, coverage probability and throughput. These results give insight on the guarantees which can be offered to primary users and more generally on the possibilities offered by cognitive radio to improve the effectiveness of spectrum utilization in such networks.

Multiuser diversity gain in cognitive networks

Abstract: Dynamic allocation of resources to the best link in large multiuser networks offers considerable improvement in spectral efficiency. This gain, often referred to as multiuser diversity gain, can be cast as double-logarithmic growth of the network throughput with the number of users. In this paper, we consider large cognitive networks granted concurrent spectrum access with license-holding users. The primary network affords to share its underutilized spectrum bands with the secondary users. We assess the optimal multiuser diversity gain in the cognitive networks by quantifying how the sum-rate throughput of the network scales with the number of secondary users. For this purpose, we look at the optimal pairing of spectrum bands and secondary users, which is supervised by a central entity fully aware of the instantaneous channel conditions, and show that the throughput of the cognitive network scales double-logarithmically with the number of secondary users (N) and linearly with the number of available spectrum bands (M), i.e., M log log N. We then propose a distributed spectrum allocation scheme, which does not necessitate a central controller or any information exchange among different secondary users and still obeys the optimal throughput scaling law. This scheme requires that some secondary transmitter-receiver pairs exchange log M information bits among themselves. We also show that the aggregate amount of information exchange between secondary transmitter-receiver pairs is asymptotically equal to M log M. Finally, we show that our distributed scheme guarantees fairness among the secondary users, meaning that they are equally likely to get access to an available spectrum band.

The price of ignorance: distributed topology control in cognitive networks

Abstract: In a cognitive network, autonomous and adaptive radios select their operating parameters to achieve individual and network-wide goals. The effectiveness of these adaptations depends on the amount of knowledge about the state of the network that is available to the radios. We examine the price of ignorance in topology control in a cognitive network with power- and spectral-efficiency objectives. We propose distributed algorithms that, if radios possess global knowledge, minimize both the maximum transmit power and the spectral footprint of the network. We show that while local (as opposed to global) knowledge has little effect on the maximum transmission power used by the network, it has a significant effect on the spectral performance. Furthermore, we show that due to the high cost of maintaining network knowledge for highly dynamic networks, the cost/performance tradeoff makes it advantageous for radios to operate under some degree of local knowledge, rather than global knowledge.We also propose distributed algorithms for power and frequency adaptations as radios join or leave the network, and assess how partial knowledge impacts the performance of these adaptations.

JENNA: a jamming evasive network-coding neighbor-discovery algorithm for cognitive radio networks [Dynamic Spectrum Management]

Abstract: Cognitive radios operate in a particularly challenging wireless environment. Besides the strict requirements imposed by opportunistic coexistence with licensed users, cognitive radios may have to deal with other concurrent (either malicious or selfish) cognitive radios that aim at gaining access to the available spectrum resources with no regard to fairness or other behavioral etiquettes. By taking advantage of their highly flexible RF front-ends, they are able to mimic a licensed user's behavior or simply jam a given channel with high power. This way these concurrent users (jammers) are capable of interrupting or delaying the neighbor discovery process initiated by a cognitive radio, which is interested in using a portion of the available spectrum for its own data communications. To solve this problem we propose a Jamming Evasive Network-coding Neighbor-Discovery Algorithm (JENNA), which ensures complete neighbor discovery for a cognitive radio network in a distributed and asynchronous way. We compare the proposed algorithm with baseline schemes that represent existing solutions, and validate its feasibility in a single-hop cognitive radio network.

Survey of cognitive radio architectures

Abstract: Cognitive Radio and Cognitive Networking are emerging fields of research that has the potential for transformative changes to the current status quo. Cognitive systems utilize environmental observations such as spectrum or network conditions to change operational configurations in order to optimize performance at individual node or over end-to-end goals. This paper surveys some of these origin cognitive frameworks and correlates these frameworks to cognitive radio implementations of today. Several definitive implementations and cognitive radio architectures are reviewed and compared. This paper also identifies area of need and suggests directions forward for novel research in this area through interdisciplinary collaboration with the cognitive sciences, integrating prediction and proactive operation into cognitive radio/network architectures and identifying less researched artificial intelligence algorithms that show promise towards cognitive radio architecture.

Stochastic Traffic Engineering in Multihop Cognitive Wireless Mesh Networks

Abstract: In this work, the stochastic traffic engineering problem in multihop cognitive wireless mesh networks is addressed. The challenges induced by the random behaviors of the primary users are investigated in a stochastic network utility maximization framework. For the convex stochastic traffic engineering problem, we propose a fully distributed algorithmic solution which provably converges to the global optimum with probability one. We next extend our framework to the cognitive wireless mesh networks with nonconvex utility functions, where a decentralized algorithmic solution, based on learning automata techniques, is proposed. We show that the decentralized solution converges to the global optimum solution asymptotically.

Distributed Compressive Wideband Spectrum Sensing in Cooperative Multi-Hop Cognitive Networks

Abstract: This paper develops a distributed compressed spectrum sensing approach for cooperative wideband multi-hop cognitive radio (CR) networks where both primary users and CR users are active during the sensing stage. Due to the multi-hop nature, each CR needs to sense its individual spectral map that consists of both common spectral components from primary users and individualized spectral innovations arising from emissions of other CRs or interference in its local one-hop region. These CR-dependent spectral innovation components complicate the task of user cooperation for primary user detection. To cope with these difficulties, we adopt the compressed sensing approach at local CRs to attain high-resolution signal recovery at lower-than-Nyquist sampling rates. Each CR alternatively estimates the spectral occupancy of primary and CR users, and exchanges proper information with neighboring CRs to reach global fusion and consensus on the estimated primary user spectrum. The spectral orthogonality between primary users and CR users is exploited to improve the spectral estimation accuracy. Using only one-hop local communications, the proposed distributed algorithm converges fast to the globally optimal solution at low communication and computation load scalable to the network size.

A learner based on neural network for cognitive radio

Abstract: Intelligence is a very important characteristic for cognitive radios (CR). Design of cognitive engine and application of artificial intelligence (AI) techniques are key to the implementation of this characteristic. Machine learning is one of the disciples in AI. This paper will propose a scheme of cognitive engine design, and use a learning algorithm based on neural network (NN) to implement a learner in the cognitive engine. A multilayer perceptron (MLP) neural network model will be introduced to ensure the convergence of the network, and problems on stop condition and overfitting will also be discussed. Finally, performance of the algorithm will be analyzed by simulations.