Showing papers on "Cognitive network published in 2015"
TL;DR: A general probable 5G cellular network architecture is proposed, which shows that D2D, small cell access points, network cloud, and the Internet of Things can be a part of 5G Cellular network architecture.
Abstract: In the near future, i.e., beyond 4G, some of the prime objectives or demands that need to be addressed are increased capacity, improved data rate, decreased latency, and better quality of service. To meet these demands, drastic improvements need to be made in cellular network architecture. This paper presents the results of a detailed survey on the fifth generation (5G) cellular network architecture and some of the key emerging technologies that are helpful in improving the architecture and meeting the demands of users. In this detailed survey, the prime focus is on the 5G cellular network architecture, massive multiple input multiple output technology, and device-to-device communication (D2D). Along with this, some of the emerging technologies that are addressed in this paper include interference management, spectrum sharing with cognitive radio, ultra-dense networks, multi-radio access technology association, full duplex radios, millimeter wave solutions for 5G cellular networks, and cloud technologies for 5G radio access networks and software defined networks. In this paper, a general probable 5G cellular network architecture is proposed, which shows that D2D, small cell access points, network cloud, and the Internet of Things can be a part of 5G cellular network architecture. A detailed survey is included regarding current research projects being conducted in different countries by research groups and institutions that are working on 5G technologies.
1,899 citations
TL;DR: It is shown that numerous open challenges, such as efficient SI suppression, high-performance FD MAC-layer protocol design, low power consumption, and hybrid FD/HD designs, have to be tackled before successfully implementing FD-based systems.
Abstract: The wireless research community aspires to conceive full duplex operation by supporting concurrent transmission and reception in a single time/frequency channel for the sake of improving the attainable spectral efficiency by a factor of two as compared to the family of conventional half duplex wireless systems. The main challenge encountered in implementing FD wireless devices is that of finding techniques for mitigating the performance degradation imposed by self-interference. In this article, we investigate the potential FD techniques, including passive suppression, active analog cancellation, and active digital cancellation, and highlight their pros and cons. Furthermore, the troubles of FD medium access control protocol design are discussed for addressing the problems such as the resultant end-to-end delay and network congestion. Additionally, an opportunistic decode-andforward- based relay selection scheme is analyzed in underlay cognitive networks communicating over independent and identically distributed Rayleigh and Nakagami-m fading channels in the context of FD relaying. We demonstrate that the outage probability of multi-relay cooperative communication links can be substantially reduced. Finally, we discuss the challenges imposed by the aforementioned techniques and a range of critical issues associated with practical FD implementations. It is shown that numerous open challenges, such as efficient SI suppression, high-performance FD MAC-layer protocol design, low power consumption, and hybrid FD/HD designs, have to be tackled before successfully implementing FD-based systems.
646 citations
TL;DR: The methodology of network science as applied to the particular case of neuroimaging data is described and its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory are reviewed.
Abstract: Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time dynamics, and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.
355 citations
TL;DR: This paper presents the authors' recent work in this area, which includes a centralized cognitive medium access control (MAC) protocol, a distributed cognitive MAC protocol, and a specially designed routing protocol for cognitive M2M networks.
Abstract: Machine-to-machine (M2M) communications enables networked devices to exchange information among each other as well as with business application servers and therefore creates what is known as the Internet-of-Things (IoT). The research community has a consensus for the need of a standardized protocol stack for M2M communications. On the other hand, cognitive radio technology is very promising for M2M communications due to a number of factors. It is expected that cognitive M2M communications will be indispensable in order to realize the vision of IoT. However cognitive M2M communications requires a cognitive radio-enabled protocol stack in addition to the fundamental requirements of energy efficiency, reliability, and Internet connectivity. The main objective of this paper is to provide the state of the art in cognitive M2M communications from a protocol stack perspective. This paper covers the emerging standardization efforts and the latest developments on protocols for cognitive M2M networks. In addition, this paper also presents the authors’ recent work in this area, which includes a centralized cognitive medium access control (MAC) protocol, a distributed cognitive MAC protocol, and a specially designed routing protocol for cognitive M2M networks. These protocols explicitly account for the peculiarities of cognitive radio environments. Performance evaluation demonstrates that the proposed protocols not only ensure protection to the primary users (PUs) but also fulfil the utility requirements of the secondary M2M networks.
310 citations
TL;DR: A survey of the recent advances in radio resource allocation in CR sensor networks (CRSNs) is presented and an insight into the related issues and challenges is provided, and future research directions are clearly identified.
Abstract: Wireless sensor networks (WSNs) use the unlicensed industrial, scientific, and medical (ISM) band for transmissions. However, with the increasing usage and demand of these networks, the currently available ISM band does not suffice for their transmissions. This spectrum insufficiency problem has been overcome by incorporating the opportunistic spectrum access capability of cognitive radio (CR) into the existing WSN, thus giving birth to CR sensor networks (CRSNs). The sensor nodes in CRSNs depend on power sources that have limited power supply capabilities. Therefore, advanced and intelligent radio resource allocation schemes are very essential to perform dynamic and efficient spectrum allocation among sensor nodes and to optimize the energy consumption of each individual node in the network. Radio resource allocation schemes aim to ensure QoS guarantee, maximize the network lifetime, reduce the internode and internetwork interferences, etc. In this paper, we present a survey of the recent advances in radio resource allocation in CRSNs. Radio resource allocation schemes in CRSNs are classified into three major categories, i.e., centralized, cluster-based, and distributed. The schemes are further divided into several classes on the basis of performance optimization criteria that include energy efficiency, throughput maximization, QoS assurance, interference avoidance, fairness and priority consideration, and hand-off reduction. An insight into the related issues and challenges is provided, and future research directions are clearly identified.
246 citations
TL;DR: Novel methods in network science are developed and applied to quantify how patterns of functional connectivity between brain regions reconfigure as human subjects perform 64 different tasks, providing a new conceptual framework for understanding the dynamic integration and recruitment of cognitive systems in enabling behavioral adaptability across both task and rest conditions.
Abstract: One of the most remarkable features of the human brain is its ability to adapt rapidly and efficiently to external task demands. Novel and non-routine tasks, for example, are implemented faster than structural connections can be formed. The neural underpinnings of these dynamics are far from understood. Here we develop and apply novel methods in network science to quantify how patterns of functional connectivity between brain regions reconfigure as human subjects perform 64 different tasks. By applying dynamic community detection algorithms, we identify groups of brain regions that form putative functional communities, and we uncover changes in these groups across the 64-task battery. We summarize these reconfiguration patterns by quantifying the probability that two brain regions engage in the same network community (or putative functional module) across tasks. These tools enable us to demonstrate that classically defined cognitive systems—including visual, sensorimotor, auditory, default mode, fronto-parietal, cingulo-opercular and salience systems—engage dynamically in cohesive network communities across tasks. We define the network role that a cognitive system plays in these dynamics along the following two dimensions: (i) stability vs. flexibility and (ii) connected vs. isolated. The role of each system is therefore summarized by how stably that system is recruited over the 64 tasks, and how consistently that system interacts with other systems. Using this cartography, classically defined cognitive systems can be categorized as ephemeral integrators, stable loners, and anything in between. Our results provide a new conceptual framework for understanding the dynamic integration and recruitment of cognitive systems in enabling behavioral adaptability across both task and rest conditions. This work has important implications for understanding cognitive network reconfiguration during different task sets and its relationship to cognitive effort, individual variation in cognitive performance, and fatigue.
154 citations
TL;DR: The findings suggest that dense and clustered connectivity between the hub nodes belonging to different modules is the "network fingerprint" of cognition that might facilitate global integration of information and provide a substrate for a "global workspace" necessary for cognition and consciousness to occur.
148 citations
TL;DR: The cognitive radio networks, resources, objectives, constraints, and challenges are presented, and a survey on the state-of-the-art of machine-learning techniques in cognitive radios is presented.
Abstract: Cognitive radios are expected to play a major role towards meeting the exploding traffic demand over wireless systems. A cognitive radio node senses the environment, analyzes the outdoor parameters, and then makes decisions for dynamic time-frequency-space resource allocation and management to improve the utilization of the radio spectrum. For efficient real-time process, the cognitive radio is usually combined with artificial intelligence and machine-learning techniques so that an adaptive and intelligent allocation is achieved. This paper firstly presents the cognitive radio networks, resources, objectives, constraints, and challenges. Then, it introduces artificial intelligence and machine-learning techniques and emphasizes the role of learning in cognitive radios. Then, a survey on the state-of-the-art of machine-learning techniques in cognitive radios is presented. The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. This paper also discusses the cognitive radio implementation and the learning challenges foreseen in cognitive radio applications.
115 citations
TL;DR: This paper introduces an innovative system concept called COgnition-BAsed NETworkS (COBANETS), which develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation.
Abstract: In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication networks.
108 citations
TL;DR: This paper considered the security threats from passive and active attacks in CRNs, and the PHY layer security is presented in the view of passive attacks, and it is a compelling idea of using the physical properties of the radio channel to help provide secure wireless communications.
Abstract: In the last decade, cognitive radio (CR) has emerged as a major next generation wireless networking technology, which is the most promising candidate solution to solve the spectrum scarcity and improve the spectrum utilization. However, there exist enormous challenges for the open and random access environment of CRNs, where the unlicensed secondary users (SUs) can use the channels that are not currently used by the licensed primary users (PUs) via spectrum-sensing technology. Because of this access method, some malicious users may access the cognitive network arbitrarily and launch some special attacks, such as primary user emulation attack, falsifying data or denial of service attack, which will cause serious damage to the cognitive radio network. In addition to the specific security threats of cognitive network, CRNs also face up to the conventional security threats, such as eavesdropping, tampering, imitation, forgery, and noncooperation etc‥ Hence, Cognitive radio networks have much more risks than traditional wireless networks with its special network model. In this paper, we considered the security threats from passive and active attacks. Firstly, the PHY layer security is presented in the view of passive attacks, and it is a compelling idea of using the physical properties of the radio channel to help provide secure wireless communications. Moreover, malicious user detection is introduced in the view of active attacks by means of the signal detection techniques to decrease the interference and the probabilities of false alarm and missed detection. Finally, we discuss the general countermeasures of security threats in three phases. In particular, we discuss the far reaching effect of defensive strategy against attacks in CRNs.
79 citations
TL;DR: In this article, two planning approaches (i.e., genetic-based and graph-based) are proposed that accommodate cognitive radio technology to improve user throughput by eliminating communication interference, and the proposed algorithms with spectrum cognition improve network performance in terms of throughput and signal-to-interference-plus-noise ratio.
Abstract: Mobile communication is facing new challenges to the soaring traffic demand of numerous user devices; thus, the notion of the small cell has been proposed and realized in recent years. However, licensed spectrum has been occupied by various underlying access technologies, so the deployment of small cells needs a sophisticated planning algorithm. In this article, we provide an overview of reconfigurable radio and small cell technologies, then introduce the tentative network architecture for 5G. Two planning approaches (i.e., genetic-based and graphbased) are proposed that accommodate cognitive radio technology to improve user throughput by eliminating communication interference. Since cognitive radio networking provides frequency allocation with cognition cycle for better spectral efficiency, we tackle the deployment of ultradense small cells and consider the coordination of unlicensed spectrum at the same time. Results show that the proposed algorithms with spectrum cognition improve network performance in terms of throughput and signal-to-interference- plus-noise ratio. Specifically, the genetic- based algorithm increases 232 percent in throughput and 150 percent in signal-to-interference- plus-noise ratio compared to the graphbased algorithm. Finally, we conclude this article by discussing potential challenges and opportunities.
TL;DR: This paper enhances RPL for cognitive radio enabled AMI networks with novel modifications to RPL in order to address the routing challenges in cognitive radio environments along with protecting the primary users as well as meeting the utility requirements of secondary network.
Abstract: It is expected that the use of cognitive radio for smart grid communication will be indispensable in near future. Recently, IETF has standardized RPL (routing protocol for low power and lossy networks), which is expected to be the standard routing protocol for majority of applications including advanced metering infrastructure (AMI) networks. Our objective in this paper is to enhance RPL for cognitive radio enabled AMI networks. Our enhanced protocol provides novel modifications to RPL in order to address the routing challenges in cognitive radio environments along with protecting the primary users as well as meeting the utility requirements of secondary network. System level performance evaluation shows the effectiveness of proposed protocol as a viable solution for practical cognitive AMI networks.
TL;DR: Two classes of spectrum-supply chain networks based on two regimes, one allows open-access to the spectrum, and the other is a market-driven regime are discussed, which allow for analysis of both equilibrium and transient behaviors.
Abstract: Cognitive radio provides a basis for addressing the practical issue of spectrum scarcity. This issue has been raised due to the continuing advances in wireless technology, which has led to ever-increasing demand for larger bandwidth. The issue of spectrum scarcity has been exacerbated due to inefficient use of the electromagnetic spectrum. Adopting the novel idea of cognitive radio for secondary usage of underutilized spectrum results in the existence of two worlds of wireless communications going on side by side: the legacy wireless world and the cognitive wireless world. Spectrum holes (i.e., the unused spectrum subbands) are the medium, through which these two worlds dynamically interact. Releasing subbands by primary users allows the cognitive radio users to sustain communication and perform their normal tasks. Combination of the two wireless worlds can be viewed as a spectrum-supply chain network, in which the legacy owners and their customers (primary users) play the role of the suppliers and cognitive radios (secondary users) play the role of consumers. This paper discusses two classes of spectrum-supply chain networks based on two regimes, one allows open-access to the spectrum, and the other is a market-driven regime. Each one of them has its own merits and suitability for a different environment; therefore, they have complementary roles. Analytic models are developed for these two classes of networks, which allow for analysis of both equilibrium and transient behaviors.
TL;DR: A new joint estimation paradigm, namely deep sensing, is proposed for such challenging spectrum and location awareness applications that the mutual interruption between the two unknown quantities is fully considered and, therefore, the PU's emission state is identified by estimating its moving positions jointly.
Abstract: Spectrum sensing based dynamic spectrum sharing is one of the key innovative techniques in future 5G communications. When realistic mobile scenarios are concerned, the location of primary user (PU) is of great significance to reliable spectrum detections and cognitive network enhancements. Given the dynamic disappearance of its emission signals, the passive locations tracking of PU, nevertheless, remains dramatically different from existing positioning problems. In this investigation, a new joint estimation paradigm, namely deep sensing , is proposed for such challenging spectrum and location awareness applications. A major advantage of this new sensing scheme is that the mutual interruption between the two unknown quantities is fully considered and, therefore, the PU's emission state is identified by estimating its moving positions jointly. Taking both PU's unknown states and its evolving positions into account, a unified mathematical model is formulated relying on a dynamic state-space approach. To implement the new sensing framework, a random finite set (RFS) based Bernoulli filtering algorithm is then suggested to recursively estimate unknown PU states accompanying its time-varying locations. Meanwhile, the sequential importance sampling is used to approximate intractable posterior densities numerically. Furthermore, an adaptive horizon expanding mechanism is specially designed to avoid the mis-tracking aroused by the intermittent disappearance of PU. Experimental simulations demonstrate that, even with mobile PUs, spectrum sensing can be realized effectively by tracking its locations incessantly. The location information, as an extra gift, may be utilized by cognitive performance optimizations.
TL;DR: The novel closed-form expression for the outage probability (OP) of the cognitive network are derived and the asymptotic OP expression at high SNR is developed to reveal the diversity order and code gain of the considered network.
Abstract: This letter investigates the outage performance of a multi-antenna cognitive decode-and-forward (DF) hybrid satellite-terrestrial relay network (HSTRN) with beamforming (BF), where the secondary source and relay can coexist with the primary user (PU) as long as their interference imposed to PU is below a predefined threshold. Specifically, the novel closed-form expression for the outage probability (OP) of the cognitive network are derived. Furthermore, the asymptotic OP expression at high SNR is developed to reveal the diversity order and code gain of the considered network. Finally, simulation results are provided to demonstrate the validity of the theoretical results, and show the effect of antenna number and interfering satellite link on the performance of the cognitive network.
TL;DR: A probabilistic approach in determining the initial and target channels for the handoff procedure in a single secondary user network is proposed and results confirm the validity of the analytical approach.
Abstract: Spectrum mobility in cognitive radio networks not only enables the secondary users to guarantee the desired QoS of the primary users but also grants an efficient exploitation of the available spectrum holes in the network. In this paper, we propose a probabilistic approach in determining the initial and target channels for the handoff procedure in a single secondary user network. To characterize the network, a queuing theoretical framework is introduced, and “stay” and “change” handoff policies are both addressed. The performance of the secondary user in terms of average sojourn and extended service times for secondary connections is analyzed, and convex optimization problems with the objective of minimizing those times as well as analytical solutions are presented. Simulation results confirm the validity of our analytical approach.
15 May 2015
TL;DR: Key processes in network intelligence, such as reasoning, learning, and context awareness, are presented to illustrate how these methods can take reconfiguration to a new level and offer a unifying framework for research in reconfigurable wireless networks.
Abstract: Driven by the advent of sophisticated and ubiquitous applications, and the ever-growing need for information, wireless networks are without a doubt steadily evolving into profoundly more complex and dynamic systems. The user demands are progressively rampant, while application requirements continue to expand in both range and diversity. Future wireless networks, therefore, must be equipped with the ability to handle numerous, albeit challenging, requirements. Network reconfiguration, considered as a prominent network paradigm, is envisioned to play a key role in leveraging future network performance and considerably advancing current user experiences. This paper presents a comprehensive overview of reconfigurable wireless networks and an in-depth analysis of reconfiguration at all layers of the protocol stack. Such networks characteristically possess the ability to reconfigure and adapt their hardware and software components and architectures, thus enabling flexible delivery of broad services, as well as sustaining robust operation under highly dynamic conditions. The paper offers a unifying framework for research in reconfigurable wireless networks. This should provide the reader with a holistic view of concepts, methods, and strategies in reconfigurable wireless networks. Focus is given to reconfigurable systems in relatively new and emerging research areas such as cognitive radio networks, cross-layer reconfiguration, and software-defined networks. In addition, modern networks have to be intelligent and capable of self-organization. Thus, this paper discusses the concept of network intelligence as a means to enable reconfiguration in highly complex and dynamic networks. Key processes in network intelligence, such as reasoning, learning, and context awareness, are presented to illustrate how these methods can take reconfiguration to a new level. Finally, the paper is supported with several examples and case studies showing the tremendous impact of reconfiguration on wireless networks.
22 Jul 2015
TL;DR: The architecture for cognitive manufacturing system employing benefits of Industrial Internet and Cognitive Control is proposed, enabling new level of adaptability and re-configurability in the system by self-X capabilities.
Abstract: Considering constantly increasing demand for shift from mass production to mass customization and the need to maintain high level of automation despite permanent changes in manufacturing technologies and tools new approaches and solutions have to be provided in manufacturing. Cyber-Physical Systems and Industrial Internet of Things are enabling smart manufacturing to tackle the challenge of data processing, integration and interpretation, but beyond uniformed data collection and visualization. The cognitive approach is argued to introduce brain and biologically-inspired algorithms capable to better adapt industrial systems for unforeseen conditions. Such approach should provide flexible and robust solution for manufacturing systems, enabling new level of adaptability and re-configurability in the system by self-X capabilities. In this paper contemporary solutions applicable for introduction of cognitive capabilities in manufacturing systems are studied and the architecture for cognitive manufacturing system employing benefits of Industrial Internet and Cognitive Control is proposed.
TL;DR: The state of the art of cognitive radio ad-hoc network architecture is surveyed in this paper, where the paper specifies the formation mechanisms and performance evaluations of the studied architectures.
Abstract: Combating the growing necessity of radio spectrum, which is a limited natural resource, proper utilization of the radio spectrum is a must. Cognitive radio network (CRN) plays a vibrant role to solve this spectrum scarcity problem. Cognitive radio uses an open spectrum allocation technique to make more efficient utilization of the wireless radio spectrum and reduces the bottleneck on the frequency bands. Thus, accessible spectrum information is required for communication in CRN, which can be acquired by using spectrum database or by spectrum sensing. In addition, a robust architecture with appropriate communication protocol is preconditioned in the deployment of CRN. The state of the art of cognitive radio ad-hoc network architecture is surveyed in this paper, where the paper specifies the formation mechanisms and performance evaluations of the studied architectures. The reviewed papers have addressed some vital issues for the concrete deployment of cognitive radio ad-hoc network; however, there remain some issues that need to be addressed. Thus, this paper conveys a thorough and abstract understanding of cognitive radio ad-hoc network architecture, and also points out some open research issues in this area.
TL;DR: A cognitive medium access control (MAC) protocol is designed, keeping in view the unique features of M2M devices and smart grid communication requirements, and the use of packet reservation multiple access (PRMA) is proposed.
Abstract: It is expected that cognitive machine-to-machine (M2M) communications in smart grid networks will be indispensable in the near future. In this paper, after discussing the motivation of cognitive M2M communications in a smart grid network, we design a cognitive medium access control (MAC) protocol, keeping in view the unique features of M2M devices and smart grid communication requirements. We propose the use of packet reservation multiple access (PRMA), carry out its feasibility study, and adapt and significantly enhance it with modifications particularly tailored for M2M environments. The proposed protocol is centralized in nature and utilizes a specialized frame structure for supporting the coexistence of the cognitive M2M network with the primary network. This frame structure is further optimized, considering different tradeoffs pertaining to primary network protection and the utility of a secondary M2M network. Performance evaluation is carried out by analytical modeling and simulation studies. We also present a case study for application of the proposed protocol in advanced metering infrastructure (AMI) networks under the dynamics of power systems.
TL;DR: It is shown that the blocking ratio of LTE-CR can be reduced notably compared to that of conventional LTE without CR, and the average user throughput of LTE can be improved with near-zero blocking ratio by offloading users to complementary cognitive spectra.
Abstract: Network access plays an important role in LTE cognitive radio (LTE-CR) cellular networks in determining users’ experiences. An overview is first carried out on network access schemes in existing cognitive cellular networks such as IEEE 802.22 and IEEE 1900.4, based on which it can be seen that cognitive pilot channel (CPC)-based network access is a promising scheme for LTECR, which may provide fast network access and put no stringent requirements on terminals. Next, considering the implementation issues of CPC in practical systems, a CPC-based backward- compatible network access scheme should be designed for LTE-CR to facilitate the application of CR in LTE networks, which could exploit existing LTE structures and technologies to carry CPC information. To achieve this, a new system information block (SIB) should be designed to carry CPC on a current physical downlink shared channel with little standards effort. Moreover, load awareness is introduced so that LTE-CR is activated only when the system load is high. The complete process of this SIB-CPC-based backward-compatible and loadaware network access is described, and its performance is evaluated via simulations. It is shown that the blocking ratio of LTE-CR can be reduced notably compared to that of conventional LTE without CR. Moreover, by selecting an appropriate load threshold to activate LTE-CR, the average user throughput of LTE can be improved with near-zero blocking ratio by offloading users to complementary cognitive spectra.
TL;DR: An analytic performance evaluation of the bit error rate (BER) of underlay decode-and-forward cognitive networks with best relay selection over Rayleigh multipath fading channels and a novel and highly accurate closed-form approximate BER expression is derived for the specific case where relays are located relatively close to each other.
Abstract: This paper provides an analytic performance evaluation of the bit error rate (BER) of underlay decode-and-forward cognitive networks with best relay selection over Rayleigh multipath fading channels. A generalized BER expression valid for arbitrary operational parameters is firstly presented in the form of a single integral, which is then employed for determining the diversity order and coding gain for different best relay selection scenarios. Furthermore, a novel and highly accurate closed-form approximate BER expression is derived for the specific case where relays are located relatively close to each other. The presented results are rather convenient to handle both analytically and numerically, while they are shown to be in good agreement with results from respective computer simulations. In addition, it is shown that as in the case of conventional relaying networks, the behaviour of underlay relaying cognitive networks with best relay selection depends significantly on the number of involved relays.
TL;DR: An impressive design of a low complexity and high efficiency dynamic spectrum access technique for cognitive radio networks that does not require central controllers nor the pre-establishment and maintenance of common control channels, yet can provide throughput and fairness levels that approach the performance of centralized systems.
Abstract: Cognitive radio is an emerging wireless technology that is envisaged as a solution to the spectrum scarcity issue. To improve spectrum utilization, cognitive (unlicensed) wireless users are assigned an opportunistic access to vacant channels on the condition they avoid interference with primary (licensed) users. In this paper we present an impressive design of a low complexity and high efficiency dynamic spectrum access technique for cognitive radio networks. This spectrum assignment algorithm does not require central controllers nor the pre-establishment and maintenance of common control channels. Yet, it can provide throughput and fairness levels that approach the performance of centralized systems. In addition, the proposed technique reacts extremely well to disturbances in the cognitive radio network configuration, including when primary users are activated, or when newcomer cognitive users join the network. Furthermore, we present in this work an analytical model that can be used to provide quick predictions of the performance of our proposed algorithm.
TL;DR: A virtualized and cognitive network architecture is proposed, wherein network functionalities are implemented via software instances in the cloud, and the underlying architecture can adapt to the application of interest as well as to changes in channels and traffic conditions.
Abstract: Cellular networks have preserved an application agnostic and base station (BS) centric architecture1 for decades. Network functionalities (e.g. user association) are decided and performed regardless of the underlying application (e.g. automation, tactile Internet, online gaming, multimedia). Such an ossified architecture imposes several hurdles against achieving the ambitious metrics of next generation cellular systems. This article first highlights the features and drawbacks of such architectural ossification. Then the article proposes a virtualized and cognitive network architecture, wherein network functionalities are implemented via software instances in the cloud, and the underlying architecture can adapt to the application of interest as well as to changes in channels and traffic conditions. The adaptation is done in terms of the network topology by manipulating connectivities and steering traffic via different paths, so as to attain the applications’ requirements and network design objectives. The article presents cognitive strategies to implement some of the classical network functionalities, along with their related implementation challenges. The article further presents a case study illustrating the performance improvement of the proposed architecture as compared to conventional cellular networks, both in terms of outage probability and handover rate.
TL;DR: This paper first provides basics and features of neighbor discovery, as well as, the challenges when moving from traditional wireless networks towards cognitive radio networks, in order to pave the way for a better understanding of the neighbor discovery in cognitiveRadio networks.
TL;DR: The ongoing cellular system evolution is shown to form a solid base for the introduction of new shared spectrum bands for cognitive cellular systems.
Abstract: This article reviews the application of the recent European Licensed Shared Access (LSA) concept for spectrum sharing between a mobile network operator (MNO) and an incumbent user. LSA, as a new area of application of cognitive technology, provides the MNO an opportunity to access new frequency resources on a shared basis. The article presents critical design criteria of LSA from the MNO point of view in order to allow future cognitive cellular networks to efficiently exploit shared spectrum bands. We describe the role of LSA bands in the context of heterogeneous networking, and identify the Long Term Evolution (LTE) and LTE-Advanced enabling technologies that support the introduction of LSA. Such technologies include traffic steering, carrier aggregation, and self-organizing networking. Additionally, we introduce an LSA management unit controlled by the MNO, to be implemented on top of the existing LTE/LTEAdvanced architecture, and we discuss the functionalities required for the optimization and automation of LSA resource management. We also depict the interrelations of the tasks between the LSA management unit and the supporting LTE/LTE-Advanced technologies. Based on the findings in this article, the ongoing cellular system evolution is shown to form a solid base for the introduction of new shared spectrum bands for cognitive cellular systems.
TL;DR: A framework is proposed, based on the moment-generating function of the interference due to a random SU, to analytically compute the outage probability in the primary network, as well as the average number of active SUs in the secondary network.
Abstract: This paper analyzes the performance of the primary users (PUs) and secondary users (SUs) in an arbitrarily-shaped underlay cognitive network. In order to meet the interference threshold requirement for a primary receiver at an arbitrary location, we consider different SU activity protocols that limit the number of active SUs. We propose a framework, based on the moment-generating function of the interference due to a random SU, to analytically compute the outage probability in the primary network, as well as the average number of active SUs in the secondary network. We also propose a cooperation-based SU activity protocol in the underlay cognitive network that includes the existing threshold-based protocol as a special case. We study the average number of active SUs for the different SU activity protocols, subject to a given outage probability constraint at the PU, and we employ it as an analytical approach to compare the effect of different SU activity protocols on the performance of the primary and secondary networks.
TL;DR: MAC protocols for CRBANs are surveyed to address various application-specific requirements and compare the different MAC protocols with one another and discuss challenging open issues in the relevant research.
Abstract: The advancement in electronics, wireless communications and integrated circuits has enabled the development of small low-power sensors and actuators that can be placed on, in or around the human body. A wireless body area network (WBAN) can be effectively used to deliver the sensory data to a central server, where it can be monitored, stored and analyzed. For more than a decade, cognitive radio (CR) technology has been widely adopted in wireless networks, as it utilizes the available spectra of licensed, as well as unlicensed bands. A cognitive radio body area network (CRBAN) is a CR-enabled WBAN. Unlike other wireless networks, CRBANs have specific requirements, such as being able to automatically sense their environments and to utilize unused, licensed spectra without interfering with licensed users, but existing protocols cannot fulfill them. In particular, the medium access control (MAC) layer plays a key role in cognitive radio functions, such as channel sensing, resource allocation, spectrum mobility and spectrum sharing. To address various application-specific requirements in CRBANs, several MAC protocols have been proposed in the literature. In this paper, we survey MAC protocols for CRBANs. We then compare the different MAC protocols with one another and discuss challenging open issues in the relevant research.
09 Mar 2015
TL;DR: A cloud-assisted global positioning system (GPS)-driven dynamic spectrum access framework for transportation CPS is presented and performance evaluation of the proposed approach is presented with the help numerical results obtained from simulations.
Abstract: Transportation Cyber Physical Systems (CPS) are expected to rely on robust wireless communication networks for real-time feedback for controlling these systems. The IEEE 802.11p based Dedicated Short Range Communication (DSRC) standard has been proposed for vehicular communications that has 7 channels. However, these channels could be easily congested resulting in delay and unreliable communications when vehicle density is high. In this paper, we present a cloud-assisted global positioning system (GPS)-driven dynamic spectrum access framework for transportation CPS. To provide reliable communications, we assume that each vehicle is equipped with two transceivers: one transceiver (always connected to the internet using e.g., 4G link) queries spectrum database and/or can serve as a GPS through an application (app), and the other transceiver/radio switches channels and adapts to suitable transmit parameters for vehicular communications to avoid any harmful interference to primary users (PUs). Each vehicle calculates the best route to its destination using GPS and finds the set of idle channels along the route. Furthermore, each vehicle periodically checks the spectrum database throughout the route to get most updated spectrum opportunities. We present performance evaluation of the proposed approach with the help numerical results obtained from simulations.
TL;DR: This paper demonstrates how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies.