Showing papers on "Cognitive network published in 2011"

Advances in cognitive radio networks: A survey

Abstract: With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing demand for wireless radio spectrum. However, the fixed spectrum assignment policy becomes a bottleneck for more efficient spectrum utilization, under which a great portion of the licensed spectrum is severely under-utilized. The inefficient usage of the limited spectrum resources urges the spectrum regulatory bodies to review their policy and start to seek for innovative communication technology that can exploit the wireless spectrum in a more intelligent and flexible way. The concept of cognitive radio is proposed to address the issue of spectrum efficiency and has been receiving an increasing attention in recent years, since it equips wireless users the capability to optimally adapt their operating parameters according to the interactions with the surrounding radio environment. There have been many significant developments in the past few years on cognitive radios. This paper surveys recent advances in research related to cognitive radios. The fundamentals of cognitive radio technology, architecture of a cognitive radio network and its applications are first introduced. The existing works in spectrum sensing are reviewed, and important issues in dynamic spectrum allocation and sharing are investigated in detail.

Game Theory in Wireless and Communication Networks: Theory, Models, and Applications

Abstract: This unified treatment of game theory focuses on finding state-of-the-art solutions to issues surrounding the next generation of wireless and communications networks. Future networks will rely on autonomous and distributed architectures to improve the efficiency and flexibility of mobile applications, and game theory provides the ideal framework for designing efficient and robust distributed algorithms. This book enables readers to develop a solid understanding of game theory, its applications and its use as an effective tool for addressing wireless communication and networking problems. The key results and tools of game theory are covered, as are various real-world technologies including 3G networks, wireless LANs, sensor networks, dynamic spectrum access and cognitive networks. The book also covers a wide range of techniques for modeling, designing and analysing communication networks using game theory, as well as state-of-the-art distributed design techniques. This is an ideal resource for communications engineers, researchers, and graduate and undergraduate students.

A Survey on Trust Management for Mobile Ad Hoc Networks

Abstract: Managing trust in a distributed Mobile Ad Hoc Network (MANET) is challenging when collaboration or cooperation is critical to achieving mission and system goals such as reliability, availability, scalability, and reconfigurability. In defining and managing trust in a military MANET, we must consider the interactions between the composite cognitive, social, information and communication networks, and take into account the severe resource constraints (e.g., computing power, energy, bandwidth, time), and dynamics (e.g., topology changes, node mobility, node failure, propagation channel conditions). We seek to combine the notions of "social trust" derived from social networks with "quality-of-service (QoS) trust" derived from information and communication networks to obtain a composite trust metric. We discuss the concepts and properties of trust and derive some unique characteristics of trust in MANETs, drawing upon social notions of trust. We provide a survey of trust management schemes developed for MANETs and discuss generally accepted classifications, potential attacks, performance metrics, and trust metrics in MANETs. Finally, we discuss future research areas on trust management in MANETs based on the concept of social and cognitive networks.

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 and sensing and access decisions are undertaken by them in a completely distributed manner. 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 sum regret in distributed learning and access, which is the loss in secondary throughput due to learning and distributed access. For the scenario when the number of secondary users is known to the policy, we prove that the total regret is logarithmic in the number of transmission slots. This policy achieves order-optimal regret based on a logarithmic 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 at each user through feedback. We propose a policy whose sum regret grows only slightly faster than logarithmic in the number of transmission slots.

Cognitive Network Interference

Abstract: Opportunistic spectrum access creates the opening of under-utilized portions of the licensed spectrum for reuse, provided that the transmissions of secondary radios do not cause harmful interference to primary users. Such a system would require secondary users to be cognitive-they must accurately detect and rapidly react to varying spectrum usage. Therefore, it is important to characterize the effect of cognitive network interference due to such secondary spectrum reuse. In this paper, we propose a new statistical model for aggregate interference of a cognitive network, which accounts for the sensing procedure, secondary spatial reuse protocol, and environment-dependent conditions such as path loss, shadowing, and channel fading. We first derive the characteristic function and cumulants of the cognitive network interference at a primary user. Using the theory of truncated-stable distributions, we then develop the statistical model for the cognitive network interference. We further extend this model to include the effect of power control and demonstrate the use of our model in evaluating the system performance of cognitive networks. Numerical results show the effectiveness of our model for capturing the statistical behavior of the cognitive network interference. This work provides essential understanding of interference for successful deployment of future cognitive networks.

Rendezvous for Cognitive Radios

Abstract: Cognitive radios have been touted as a solution to communicating in a Dynamic Spectrum Access environment. This paper examines how cognitive radios initially find one another among the expanse of ever-changing open spectrum, termed the rendezvous problem. Specifically, it addresses the problem of rendezvous under varying levels of system capabilities, spectrum policies, and environmental conditions. The focus is on rendezvous when there are are no control channels or centralized controllers, which we term the blind rendezvous problem. Under these conditions, a sequence-based and modular clock blind rendezvous algorithms are proposed, and it is shown that the performance of these algorithms compares favorably to that of a random blind rendezvous algorithm. Specifically, the sequence-based algorithm provides a bounded Time To Rendezvous (TTR) and the ability to prioritize channels where rendezvous is more likely to occur; the modular clock algorithm reduces the expected TTR, requires little precoordination among radios attempting to rendezvous, and is robust to radios sensing different sets of available channels.

Distributed Compressive Spectrum Sensing in Cooperative Multihop Cognitive Networks

Abstract: In wideband cognitive radio (CR) networks, spectrum sensing is an essential task for enabling dynamic spectrum sharing, but entails several major technical challenges: very high sampling rates required for wideband processing, limited power and computing resources per CR, frequency-selective wireless fading, and interference due to signal leakage from other coexisting CRs. In this paper, a cooperative approach to wideband spectrum sensing is developed to overcome these challenges. To effectively reduce the data acquisition costs, a compressive sampling mechanism is utilized which exploits the signal sparsity induced by network spectrum under-utilization. To collect spatial diversity against wireless fading, multiple CRs collaborate during the sensing task by enforcing consensus among local spectral estimates; accordingly, a decentralized consensus optimization algorithm is derived to attain high sensing performance at a reasonable computational cost and power overhead. To identify spurious spectral estimates due to interfering CRs, the orthogonality between the spectrum of primary users and that of CRs is imposed as constraints for consensus optimization during distributed collaborative sensing. These decentralized techniques are developed for both cases of with and without channel knowledge. Simulations testify the effectiveness of the proposed cooperative sensing approach in multi-hop CR networks.

Game Theory and Learning for Wireless Networks: Fundamentals and Applications

Abstract: * The first tutorial-style book that gives all the relevant theory at the right level of rigor, for the wireless communications engineer. * Bridges the gap between theory and practice by giving examples and case studies showing how game theory can solve real-word problems. * Contains algorithms and techniques to implement game theory in wireless terminals. Written by leading experts in the field, Game Theory and Learning for Wireless Networks Covers how theory can be used to solve prevalent problems in wireless networks such as power control, resource allocation or medium access control. With the emphasis now on promoting 'green' solutions in the wireless field where power consumption is minimized, there is an added focus on developing network solutions that maximizes the use of the spectrum available. With the growth of distributed wireless networks such as Wi-Fi and the Internet; the push to develop ad hoc and cognitive networks has led to a considerable interest in applying game theory to wireless communication systems. Game Theory and Learning for Wireless Networks is the first comprehensive resource of its kind, and is ideal for wireless communications R&D engineers and graduate students. Samson Lasaulce is a senior CNRS researcher at the Laboratory of Signals and Systems (LSS) at Suplec, Gif-sur-Yvette, France. He is also a part-time professor in the Department of Physics at cole Polytechnique, Palaiseau, France. Hamidou Tembine is a professor in the Department of Telecommunications at Suplec, Gif-sur-Yvette, France. Merouane Debbah is a professor at Suplec, Gif-sur-Yvette, France. He is the holder of the Alcatel-Lucent chair in flexible radio since 2007. The first tutorial style book that gives all the relevant theory, at the right level of rigour, for the wireless communications engineer Bridges the gap between theory and practice by giving examples and case studies showing how game theory can solve real world resource allocation problems Contains algorithms and techniques to implement game theory in wireless terminals

Towards an Understanding of

Abstract: Cognitive systems research aims to understand how cognitive abilities can be created in artificial systems. One key issue is the architecture of the system. It organizes the interplay between the different system elements and thus, determines the principle limits for the performance of the system. In this contri- bution, we focus on important properties of hierarchical cognitive systems. Therefore, we first present a framework for modeling hierarchical systems. Based on this framework, we formulate and discuss some crucial issues that should be treated explicitly in the design of a system. On this basis, we analyze and compare several well-established cognitive architectures with respect to their internal structure. Index Terms—Behavior space, cognitive architecture, hierar- chical architecture, sensor space, system design.

Mobile Adaptive Networks

Abstract: In this paper, we investigate the self-organization and cognitive abilities of adaptive networks when the individual agents are allowed to move in pursuit of a target. The nodes act as adaptive entities with localized processing and are able to respond to stimuli in real-time. We apply adaptive diffusion techniques to guide the self-organization process, including harmonious motion and collision avoidance. We also provide stability and mean-square performance analysis of the proposed strategies, together with computer simulation to illustrate results.

Cognition in Wireless Sensor Networks: A Perspective

Abstract: Wireless Sensor Networks are believed to be the enabling technology for Ambient Intelligence. They hold the promise of delivering to a smart communication paradigm which enables setting up an intelligent network capable of handling applications that evolve from user requirements. Cognitive agents capable of making proactive decisions based on learning, reasoning and information sharing when interspersed in sensor networks, may help achieve end-to-end goals of the network even in the presence of multiple constraints and optimization objectives. Cognitive radio at the physical layer of such agents may enable the opportunistic use of the heterogeneous wireless environment. However, research efforts have been discrete and cognitive techniques have focused on improving specific aspects of the network or benefiting specific applications. The main contribution of this paper is providing the vision and advantage of a holistic approach to cognition in sensor networks, which can be achieved by incorporating learning and reasoning in the upper layers, and opportunistic spectrum access at the physical layer. Rather than providing an ostensive survey of cognitive architectures applicable to sensor networks, this paper provides the reader with a framework based on knowledge and cognition that can help achieve end-to-end goals of application-specific sensor networks.

Opportunistic Cooperation in Cognitive Femtocell Networks

Abstract: We investigate opportunistic cooperation between unlicensed secondary users and legacy primary users in a cognitive radio network. Specifically, we consider a model of a cognitive network where a secondary user can cooperatively transmit with the primary user in order to improve the latter's effective transmission rate. In return, the secondary user gets more opportunities for transmitting its own data when the primary user is idle. This kind of interaction between the primary and secondary users is different from the traditional dynamic spectrum access model in which the secondary users try to avoid interfering with the primary users while seeking transmission opportunities on vacant primary channels. In our model, the secondary users need to balance the desire to cooperate more (to create more transmission opportunities) with the need for maintaining sufficient energy levels for their own transmissions. Such a model is applicable in the emerging area of cognitive femtocell networks. We formulate the problem of maximizing the secondary user throughput subject to a time average power constraint under these settings. This is a constrained Markov Decision Problem and conventional solution techniques based on dynamic programming require either extensive knowledge of the system dynamics or learning based approaches that suffer from large convergence times. However, using the technique of Lyapunov optimization, we design a novel greedy and online control algorithm that overcomes these challenges and is provably optimal.

Cognitive Networks Achieve Throughput Scaling of a Homogeneous Network

Abstract: Two distinct, but overlapping, networks that operate at the same time, space, and frequency is considered. The first network consists of n randomly distributed primary users, which form an ad hoc network. The second network again consists of m randomly distributed ad hoc secondary users or cognitive users. The primary users have priority access to the spectrum and do not need to change their communication protocol in the presence of the secondary users. The secondary users, however, need to adjust their protocol based on knowledge about the locations of the primary users to bring little loss to the primary network's throughput. By introducing preservation regions around primary receivers, a modified multihop routing protocol is proposed for the cognitive users. Assuming m=nβ with β >; 1, it is shown that the secondary network achieves almost the same throughput scaling law as a stand-alone network while the primary network throughput is subject to only a vanishingly small fractional loss. Specifically, the primary network achieves the sum throughput of order n1/2 and, for any δ >; 0, the secondary network achieves the sum throughput of order m1/2-δ with an arbitrarily small fraction of outage. Thus, almost all secondary source-destination pairs can communicate at a rate of order m-1/2-δ.

Constrained Interference Alignment and the Spatial Degrees of Freedom of MIMO Cognitive Networks

Abstract: An interference alignment (IA) scheme is presented that allows multiple opportunistic transmitters (secondary users) to use the same frequency band of a pre-existing primary link without generating any interference. The primary and secondary transmit-receive pairs are equipped with multiple antennas. Under power constraints on the primary transmitter, the rate of the primary user is maximized by water-filling on the singular values of its channel matrix leaving some eigen modes unused, and hence, the secondary users can align their transmitted signals to produce a number of interference-free dimensions at each secondary receiver without causing any interference to the primary user. An outer bound is developed on the degrees of freedom (DoF) of the secondary users. In the case of a symmetric secondary network with time-varying channel coefficients having M antennas at each node and operating in the presence of a primary link with d0 active eigen modes, a precoding scheme is presented for the secondary transmitters that can asymptotically achieve the available (M-d0)+/2 DoF per secondary user. An iterative algorithm is also presented that utilizes channel reciprocity to achieve the proposed cognitive IA scheme. For a cognitive 3-user secondary network with constant channel coefficients, a novel closed-form solution is derived for the precoding matrices of the secondary users.

Interference management using cognitive base-stations for UMTS LTE

Abstract: In this article we demonstrate the benefits of developing cognitive base-stations in a UMTS Long Term Evolution (LTE) network. Two types of cognitive base-stations are considered: the macro-cell evolved-NodeB (eNB) and the femtocell Home evolved NodeBs (HeNB). In the context of an isolated cell or a multi-cell LTE network, the insufficiency of traditional interference management schemes is shown. Implementation of cognitive tasks such as radio scene analysis and dynamic resource access are then introduced. We argue that such cognitive basestations can exploit their knowledge of the radio scene to intelligently allocate resources and to mitigate prohibitive Co-Channel Interference (CCI). Given the distributed architecture of LTE networks, we will elaborate on cognitive interference mitigation solutions and further propose two different Game Theoretical mechanisms to achieve CCI mitigation in a distributed manner.

Asymmetric Cooperative Communications Based Spectrum Leasing via Auctions in Cognitive Radio Networks

Abstract: Dynamic spectrum leasing (DSL) was proposed recently as a new paradigm for dynamic spectrum sharing (DSS) in cognitive radio networks (CRN's). In this paper, we propose a new way to encourage primary users to lease their spectrum: The secondary users (SU's) place bids indicating how much power they are willing to spend for relaying the primary signals to their destinations. In this formulation, the primary users achieve power savings due to asymmetric cooperation. We propose and analyze both a centralized and a distributed decision-making architecture for the secondary CRN. In the centralized architecture, a Secondary System Decision Center (SSDC) selects a bid for each primary channel based on optimal channel assignment for SU's. In the decentralized cognitive network architecture, we formulate an auction game-based protocol in which each SU independently places bids for each primary channel and receivers of each primary link pick the bid that will lead to the most power savings. A simple and robust distributed reinforcement learning mechanism is developed to allow the users to revise their bids and to increase their rewards. The performance results show the significant impact of reinforcement learning in both improving spectrum utilization and meeting individual SU performance requirements.

Modulation of steady state functional connectivity in the default mode and working memory networks by cognitive load

Abstract: Interregional correlations between blood oxygen level dependent (BOLD) magnetic resonance imaging (fMRI) signals in the resting state have been interpreted as measures of connectivity across the brain. Here we investigate whether such connectivity in the working memory and default mode networks is modulated by changes in cognitive load. Functional connectivity was measured in a steady-state verbal identity N-back task for three different conditions (N = 1, 2, and 3) as well as in the resting state. We found that as cognitive load increases, the functional connectivity within both the working memory the default mode network increases. In order to test whether functional connectivity between the working memory and the default mode networks changed, we constructed maps of functional connectivity to the working memory network as a whole and found that increasingly negative correlations emerged in a dorsal region of the posterior cingulate cortex. These results provide further evidence that low frequency fluctuations in BOLD signals reflect variations in neural activity and suggests interaction between the default mode network and other cognitive networks.

Distributed Resource Allocation for Cognitive Radio Networks With Spectrum-Sharing Constraints

Abstract: This paper presents new design formulations that aim at optimizing the performance of an orthogonal frequency-division multiple-access (OFDMA) ad hoc cognitive radio network through joint subcarrier assignment and power allocation. Aside from an important constraint on the tolerable interference induced to primary networks, to efficiently implement spectrum-sharing control within the unlicensed network, the optimization problems considered here strictly enforce upper and lower bounds on the total amount of temporarily available bandwidth that is granted to individual secondary users. These new requirements are of particular relevance in cognitive radio settings, where the spectral activities of primary users are highly dynamic, leaving little opportunity for secondary access. A dual decomposition framework is then developed for two criteria (throughput maximization and power minimization), which gives rise to the realization of distributed solutions. Because the proposed distributed protocols require very limited cooperation among the participating network elements, they are particularly applicable to ad hoc cognitive networks, where centralized processing and control are certainly inaccessible. In this paper, we recommend that the network collaboration is made possible through the implementation of virtual timers at individual secondary users and through the exchange of pertinent information over a common reserved channel. It is shown that not only is the computational complexity of the devised algorithms affordable but that the performance of these algorithms in practical scenarios attains the actual global optimum as well. The potential of the proposed approaches is thoroughly verified by asymptotic complexity analysis and numerical results.

Indirect Reciprocity Game Modelling for Cooperation Stimulation in Cognitive Networks

Abstract: In cognitive networks, since nodes generally belong to different authorities and pursue different goals, they will not cooperate with others unless cooperation can improve their own performance. Thus, how to stimulate cooperation among nodes in cognitive networks is very important. However, most of existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, according to the well-known Prisoner's Dilemma and the backward induction principle, the unique Nash equilibrium (NE) is to always play non-cooperatively. In this paper, we propose a cooperation stimulation scheme for the scenario where the number of interactions between any pair of players are finite. The proposed algorithm is based on indirect reciprocity game modelling where the key concept is "I help you not because you have helped me but because you have helped others". We formulate the problem of finding the optimal action rule as a Markov Decision Process (MDP) and propose a modified value iteration algorithm to find the optimal action rule. Using the packet forwarding game as an example, we show that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy (ESS). Finally, simulations are shown to verify the efficiency and effectiveness of the proposed algorithm.

Coolest Path: Spectrum Mobility Aware Routing Metrics in Cognitive Ad Hoc Networks

Abstract: Cognitive Radio (CR) emerges as a promising solution to current unbalanced spectrum utilization. The cognitive ad hoc network can take advantage of dynamic spectrum access and spectrum diversity over wide spectrum. It could achieve higher network capacity compared to traditional ad hoc networks, thus supporting bandwidth-demanding applications. A cognitive radio operates over wide spectrum with unpredictable channel availability. Moreover, the transmission opportunity of a cognitive node is not guaranteed due to the presence of primary users (PUs). These two unique features define new routing problems in cognitive ad hoc networks. To better characterize the unique features of cognitive radio networks, we propose new routing metrics, including accumulated spectrum temperature, highest spectrum temperature, and mixed spectrum temperature to account for the time-varying spectrum availability. The proposed metrics favor the "coolest'' path, or the path with the most balanced and/or the lowest spectrum utilization by the primary users. We also study the computational complexity of the routing algorithm in cognitive ad hoc networks. Experiment results on our USRP-2 testbed show that the proposed metrics are capable of capturing the fluctuation of spectrum availability and suitable for cognitive ad hoc networks.

A Hybrid Approach for Radio Resource Management in Heterogeneous Cognitive Networks

Abstract: Distributing Radio Resource Management (RRM) in heterogeneous wireless networks is an important research and development axis that aims at reducing network complexity. In this context, RRM decision making can be delegated to mobiles by incorporating cognitive capabilities into mobile handsets, resulting in the reduction of signalling and processing burden. This may however result in inefficiencies (such as those known as the "tragedy of commons") that are inherent to equilibria in non-cooperative games. Due to the concern for efficiency, centralized network architectures and protocols keep being considered and being compared to decentralized ones. From the point of view of the network architecture, this implies the co-existence of network-centric and terminal-centric RRM schemes. Instead of taking part within the debate among the supporters of each solution, we propose in this paper hybrid schemes where the wireless users are assisted in their decisions by the network that broadcasts aggregated load information. At some system's states, the network manager may impose his decisions on the network users. In other states the mobiles may take autonomous actions in reaction to information sent by the network. In order to improve the performance of the non-cooperative scenario, we investigate the properties of an alternative solution concept named Stackelberg game, in which the network tries to control the users' behavior by broadcasting appropriate information, expected to maximize its utility, while individual users maximize their own utility. We derive analytically the utilities related to the Quality of Service (QoS) perceived by mobile users and develop a Bayesian framework to obtain the equilibria. Numerical results illustrate the advantages of using our hybrid game framework in an association problem in a network composed of HSDPA and 3G LTE system that serve streaming and elastic flows.

Network Virtualization for Future Mobile Networks: General Architecture and Applications

Abstract: Based on the expected future requirements this paper describes a general network architecture enabled by network virtualization. This architecture consists of three major building blocks which we call virtualized physical resources, virtual resource manager and virtual network controller. Such an architecture will facilitate network sharing deployments, which might exist in the form of network consolidation or service specific networks. Furthermore the ability of our framework to combine control over various domains allows a resource optimization across IT and network infrastructure, multiple network layers and heterogeneous networks.

CoRoute: a new cognitive anypath vehicular routing protocol

Abstract: Vehicular communications promise to bring us safer driving and better traffic control. Dedicated short range communications and IEEE 802.11p are now well established standards for vehicular communications. Channels for those systems, however, are of limited capacity and are dedicated to safety applications. Thus, they are not sufficient to support the broad range of services envisioned in VANETs. It is anticipated that vehicles will utilize Wi-Fi (802.11 a/b/g) to acquire more capacity. Unfortunately, the Wi-Fi channels in urban areas are already quite heavily subscribed by residential customers. To evade the residential load, in this paper, we propose CoVanet, a cognitive vehicular ad hoc network architecture that allows vehicle radios to access the Wi-Fi channels ‘opportunistically’, finding least loaded channels. In CoVanet, network topology and channel environment change frequently because of high node mobility. The main contribution of this work is a cognitive ad hoc vehicular routing protocol (CoRoute). CoRoute is a hybrid that utilizes both geographical location and sensed channel information to establish stable, minimum delay routes to the destination. Simulation results show that CoRoute is robust to mobility and to external interference and improves packet delivery ratio on average by 70% with respect to routing without cognitive radios. Copyright © 2011 John Wiley & Sons, Ltd.

Throughput and delay scaling of general cognitive networks

Abstract: There has been recent interest within the networking research community to understand how performance scales in cognitive networks with overlapping n primary nodes and m secondary nodes. Two important metrics, i.e., throughput and delay, are studied in this paper. We first propose a simple and extendable decision model, i.e., the hybrid protocol model, for the secondary nodes to exploit spatial gap among primary transmissions for frequency reuse. Then a framework for general cognitive networks is established based on the hybrid protocol model to analyze the occurrence of transmission opportunities for secondary nodes. We show that in the case that the transmission range of the secondary network is smaller than that of the primary network in order, as long as the primary network operates in a generalized round-robin TDMA fashion, the hybrid protocol model suffice to guide the secondary network to achieve the same throughput and delay scaling as a standalone network, without harming the transmissions of the primary network. Our approach is general in the sense that we only make a few weak assumptions on both networks, and therefore obtains a wide variety of results. We show secondary networks can obtain the same order of throughput and delay as standalone networks when primary networks are classic static networks, networks with random walk mobility, hybrid networks, multicast networks, hierarchically cooperative networks or clustered networks. Our work presents a relatively complete picture of the performance scaling of cognitive networks and provides fundamental insight on the design of them.

Optimal scheduling in cooperate-to-join Cognitive Radio Networks

Abstract: Optimal transmission scheduling in wireless cognitive networks is considered under the spectrum leasing model.We propose a cooperative scheme in which secondary nodes share the time slot with primary nodes in return for cooperation. Cooperation is feasible only if the system's performance is improved over the non-cooperative case. First, we investigate a scenario where secondary users are interested in immediate rewards. Then, we formulate another problem where the secondary users are guaranteed a portion of the primary utility, on a long term basis, in return for cooperation. In both scenarios, our proposed schemes are shown to outperform non-cooperative scheduling schemes, in terms of both individual and total expected utility, for a given set of feasible constraints. Based on Lyapunov Optimization techniques, we show that our schemes are arbitrarily close to the optimal performance at the price of reduced convergence rate.

Enabling coexistence of multiple cognitive networks in TV white space

Abstract: We address the problem of coexistence among wireless networks in TV white space. We present a standard independent framework to enable exchange of information relevant for coexistence based on two mechanisms: centralized and distributed. Both mechanisms introduce the use of multiradio cluster-head equipment (CHE) as a physical entity that acquires relevant information, identifies coexistence opportunities, and implements autonomous coexistence decisions. The major conceptual difference between them lies in the fact that the centralized mechanism utilizes coexistence database(s) as a repository for coexistence related information, where CHEs need to access before making coexistence decisions. On the other hand, the distributed mechanism utilizes a broadcast channel to distribute beacons and directly convey coexistence information between CHEs. Furthermore, we give a concise overview of the current activities in international standardization bodies toward the realization of communications in TVWS along with measures taken to provide coexistence between secondary cognitive networks.

Data delivery guarantees in congested Vehicular ad hoc networks using cognitive networks

Abstract: The Wireless Access in Vehicular Environments (WAVE) protocol stack is one of the most important protocols used to allocate spectrum for vehicular communication. In a previous work, we proved that WAVE does not provide sufficient spectrum for reliable exchange of safety information. More specifically, safety message delay is not acceptable and exceeds application requirements. In this paper, we propose a system that provides Data delivery guarantees using Cognitive networks principles in congested Vehicular ad hoc networks. We will refer to our system as DCV. Our goal is to ensure that all safety packets get generated and transmitted during the same interval. The system monitors the contention delay experienced by cars on the control channel where all safety packets should be transmitted. If the sensed contention delay exceeds delay threshold γ, the Road Side Unit (RSU) needs to increase the spectrum allocated to the control channel using cognitive networks. The RSU employs a feedback control design where additional bandwidth is added to drive the contention delay below the delay threshold γ used as reference input for the controller. Analysis and simulations indicate the effectiveness of the system in providing data delivery guarantees in vehicular networks and thus increasing safety measures on the road.

Minimal time broadcasting in cognitive radio networks

Abstract: This paper addresses time-efficient broadcast scheduling problem in Cognitive Radio (CR) Networks. Cognitive Radio is a promising technology that enables the use of unused spectrum in an opportunistic manner. Because of the unique characteristics of CR technology, the broadcast scheduling problem in CR networks needs unique solutions. Even for single channel wireless networks, finding a minimum-length broadcast schedule is an NP-hard problem. In addition, the multi-channel nature of the CR networks, especially the non-uniform channel availability, makes it a more complex problem to solve. In this paper, we first present an Integer Linear Programming formulation (ILP) to determine the minimum broadcast schedule length for a CR network. We then present two heuristics to construct minimal length broadcast schedules. Comparison of optimal results (found by solving the ILP formulation) with the result of the heuristics through simulation shows that both heuristics produce schedules of either optimal or very closer to optimal lengths.