Showing papers on "Cognitive network published in 2017"

Resource Allocation for Underlay Cognitive Radio Networks: A Survey

Abstract: For conventional wireless networks, the main target of resource allocation (RA) is to efficiently utilize the available resources. Generally, there are no changes in the available spectrum, thus static spectrum allocation policies were adopted. However, these allocation policies lead to spectrum under-utilization. In this regard, cognitive radio networks (CRNs) have received great attention due to their potential to improve the spectrum utilization. In general, efficient spectrum management and resource allocation are essential and very crucial for CRNs. This is due to the fact that unlicensed users should attain the most benefit from accessing the licensed spectrum without causing adverse interference to the licensed ones. The cognitive users or called secondary users have to effectively capture the arising spectrum opportunities in time, frequency, and space to transmit their data. Mainly, two aspects characterize the resource allocation for CRNs: 1) primary (licensed) network protection and 2) secondary (unlicensed) network performance enhancement in terms of quality-of-service, throughput, fairness, energy efficiency, etc. CRNs can operate in one of three known operation modes: 1) interweave; 2) overlay; and 3) underlay. Among which the underlay cognitive radio mode is known to be highly efficient in terms of spectrum utilization. This is because the unlicensed users are allowed to share the same channels with the active licensed users under some conditions. In this paper, we provide a survey for resource allocation in underlay CRNs. In particular, we first define the RA process and its components for underlay CRNs. Second, we provide a taxonomy that categorizes the RA algorithms proposed in literature based on the approaches, criteria, common techniques, and network architecture. Then, the state-of-the-art resource allocation algorithms are reviewed according to the provided taxonomy. Additionally, comparisons among different proposals are provided. Finally, directions for future research are outlined.

Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks

Abstract: Frequency-specific oscillations and phase-coupling of neuronal populations have been proposed as an essential mechanism for the coordination of activity between brain areas during cognitive tasks. To provide an effective substrate for cognitive function, we reasoned that ongoing functional brain networks should also be able to reorganize and coordinate in a similar manner. To test this hypothesis, we here use a novel method for identifying repeating patterns of network dynamics, and show that resting networks in magnetoencephalography are well characterised by visits to short-lived transient brain states (~50-100ms), with spatially distinct power and phase-coupling in specific frequency bands. Brain states were identified for sensory, motor networks and higher-order cognitive networks; these include a posterior cognitive network in the alpha range (8-12Hz) and an anterior cognitive network in the delta/theta range (1-7Hz). Both cognitive networks exhibit particularly high power and coherence, and contain brain areas corresponding to posterior and anterior subdivisions of the default mode network. Our results show that large-scale cortical phase-coupling networks operate in very specific frequency bands, possibly reflecting functional specialisation at different intrinsic timescales.

5G: Adaptable Networks Enabled by Versatile Radio Access Technologies

Abstract: The requirements and key areas for 5G are gradually becoming more apparent, and it is becoming clear that 5G will need to be able to deal with increased levels of diversity in both the requirements it must fulfil and the technologies that it uses to fulfil them. The diverse and demanding requirements for 5G necessitate a shift away from the rigid networks of previous generations, toward a more versatile and adaptable network. Essential to enabling this level of adaptability in 5G networks will be the new radio access technologies that are employed. In previous generations, the radio access network (RAN) was composed of technologies and techniques that were tailored to satisfy the killer application of that era. In contrast, 5G will require versatile solutions that can be adapted to satisfy many different services and applications. The core network will also undergo fundamental changes, with increased levels of abstraction allowing for further reconfiguration of the network. The relationship between the RAN and core network will have a key role to play in managing and enabling adaptable networks. In this paper, we survey the choices and adaptability afforded by some of the radio access technologies being considered for 5G and explore how several system-level techniques, such as software-defined networking and cloud-RAN, can be utilized to enable and manage versatile 5G networks. Specifically, we focus on the relationship between new radio access technologies and emerging system-level techniques, examining how they may assist and complement each other. In this regard, we examine some tools such as virtualization and cognitive networks that can bridge this relationship. This paper is not intended to be a general survey on 5G, but rather a survey on how the requirements of flexibility and adaptability may be achieved in 5G through the coupling of versatile radio access technologies and emerging system-level techniques.

Network Coding in Cognitive Radio Networks: A Comprehensive Survey

Abstract: Network coding (NC) is a technique used for effective and secure communication by improving the network capacity, throughput, efficiency, and robustness. In NC, data packets are encoded by intermediate nodes and are then decoded at the destination nodes. NC has been successfully applied in a variety of networks including relay networks, peer-to-peer networks, wireless networks, cognitive radio networks, and wireless sensor networks. Cognitive radio network (CRN) is an emerging field which exploits the utilization of unused spectrum or white spaces, effectively and efficiently. In CRNs, NC schemes are also applied to maximize the spectrum utilization, as well as to maintain the effective and secure transmission of data packets over the network. In this paper, we provide a comprehensive survey of NC schemes in cognitive radio networks, highlighting the motivations for and the applications of NC in CRNs. We provide typical case studies of NC schemes in CRNs, as well as the taxonomy of NC schemes in CRNs. Finally, we present open issues, challenges, and future research directions related with NC in cognitive radio networks.

Cognitive Radio Network and Network Service Chaining toward 5G: Challenges and Requirements

Abstract: Cognitive radio is a promising technology that answers the spectrum scarcity problem arising from the growth of usage of wireless networks and mobile services. Cognitive radio network edge computing will enhance the CRN capabilities and, along with some adjustments in its operation, will be a key technology for 5G heterogeneous network deployment. This article presents current requirements and challenges in CRN, and a review of the limited research work on the CRN cloud, which will take off CRN capabilities and 5G network requirements and challenges. The article proposes a cognitive radio edge computing access server deployment for network service chaining at the access layer level.

Cognitive Capacity Harvesting Networks: Architectural Evolution Toward Future Cognitive Radio Networks

Abstract: Cognitive radio technologies enable users to opportunistically access unused licensed spectrum and are viewed as a promising way to deal with the current spectrum crisis. Over the last 15 years, cognitive radio technologies have been extensively studied from algorithmic design to practical implementation. One pressing and fundamental problem is how to integrate cognitive radios into current wireless networks to enhance network capacity and improve users’ experience. Unfortunately, existing solutions to cognitive radio networks (CRNs) suffer from many practical design issues. To foster further research activities in this direction, we attempt to provide a tutorial for CRN architecture design. Noticing that an effective architecture for CRNs is still lacking, in this tutorial, we systematically summarize the principles for CRN architecture design and present a novel flexible network architecture, termed cognitive capacity harvesting network (CCHN), to elaborate on how a CRN architecture can be designed. Unlike existing architectures, we introduce a new network entity, called secondary service provider, and deploy cognitive radio capability enabled routers, called cognitive radio routers, in order to effectively and efficiently manage resource harvesting and mobile traffic while enabling users without cognitive radios to access and enjoy CCHN services. Our analysis shows that our CCHN aligns well to industrial standardization activities and hence provides a viable approach to implementing future CRNs. We hope that our proposed design approach opens a new venue to future CRN research.

QoS Driven Channel Selection Algorithm for Cognitive Radio Network: Multi-User Multi-Armed Bandit Approach

Abstract: In this paper, we deal with the problem of opportunistic spectrum access in infrastructure-less cognitive networks. Each secondary user (SU) Tx is allowed to select one frequency channel at each transmission trial. We assume that there is no information exchange between SUs, and they have no knowledge of channel quality, availability, and other SUs actions, hence, each SU selfishly tries to select the best band to transmit. This particular problem is designed as a multi-user restless Markov multi-armed bandit problem, in which multiple SUs collect a priori unknown reward by selecting a channel. The main contribution of the paper is to propose an online learning policy for distributed SUs, that takes into account not only the availability criterion of a band but also a quality metric linked to the interference power from the neighboring cells experienced on the sensed band. We also prove that the policy, named distributed restless QoS-UCB, achieves at most logarithmic order regret, for a single-user in a first time and then for multi-user in a second time. Moreover, studies on the achievable throughput, average bit error rate obtained with the proposed policy are conducted and compared to well-known reinforcement learning algorithms.

Wireless Energy Harvesting Assisted Two-Way Cognitive Relay Networks: Protocol Design and Performance Analysis

Abstract: This paper analyzes the effects of realistic relay transceiver on the outage probability and throughput of a two-way relay cognitive network that is equipped with an energy-harvesting relay. In this paper, we configure the network with two wireless power transfer policies and two bidirectional relaying protocols. Furthermore, the differences in receiver structure of relay node that can be time switching or power splitting structure are also considered to develop closed-form expressions of outage and throughput of the network providing that the delay of transmission is limited. Numerical results are presented to corroborate our analysis for all considered network configurations. This paper facilitates us not only to quantify the degradation of outage probability and throughput due to the impairments of realistic transceiver but also to provide an insight into practical effects of specified configuration of power transfer policy, relaying protocol, and receiver structure on outage and throughput. For instance, the system with multiple access broadcast protocol and the power splitting-based receiver architecture achieves ceiling throughout higher than that of the transmission rate of source nodes. On the contrary, a combination of dual-source energy transfer policy and the time division broadcast protocol is contributed the highest level of limiting factor in terms of transceiver hardware impairments on the network throughput.

Wireless big data: transforming heterogeneous networks to smart networks

Abstract: In HetNets (Heterogeneous Networks), each network is allocated with fixed spectrum resource and provides service to its assigned users using specific RAT (Radio Access Technology). Due to the high dynamics of load distribution among different networks, simply optimizing the performance of individual network can hardly meet the demands from the dramatically increasing access devices, the consequent upsurge of data traffic, and dynamic user QoE (Quality-of-Experience). The deployment of smart networks, which are supported by SRA (Smart Resource Allocation) among different networks and CUA (Cognitive User Access) among different users, is deemed a promising solution to these challenges. In this paper, we propose a frame-work to transform HetNets to smart networks by leveraging WBD (Wireless Big Data), CR (Cognitive Radio) and NFV (Network Function Virtualization) techniques. CR and NFV support resource slicing in spectrum, physical layers, and network layers, while WBD is used to design intelligent mechanisms for resource mapping and traffic prediction through powerful AI (Artificial Intelligence) methods. We analyze the characteristics of WBD and review possible AI methods to be utilized in smart networks. In particular, the potential of WBD is revealed through high level view on SRA, which intelligently maps radio and network resources to each network for meeting the dynamic traffic demand, as well as CUA, which allows mobile users to access the best available network with manageable cost, yet achieving target QoS (Quality-of-Service) or QoE.

Outage and diversity analysis of underlay cognitive mixed RF-FSO cooperative systems

Abstract: We investigate the performance of asymmetric radio frequency (RF) and free-space optical (FSO) dual-hop cognitive amplify-and-forward relay networks where RF links are subject to independent and nonidentically distributed Nakagami-m fading We consider that the RF link transmitter and receiver are secondary users of an underlay cognitive network Specifically, the transmit power conditions of the proposed spectrum-sharing network are governed by either the combined power constraint of the interference on the primary network and the maximum transmission power at the secondary network, or the single power constraint of the interference on the primary network Also, we consider a double generalized gamma fading channel with pointing error and both heterodyne and intensity modulation/direct detection methods in the FSO link The closed-form and asymptotic expressions of outage probability for this system are calculated for fixed gain and channel-state-informationassisted relaying techniques It is demonstrated that the diversity order is a function of the fading severity of the RF link, turbulence parameters of the FSO link, and pointing error, regardless of the interference channel parameter of the primary user However, the coding gain is impressed by the interference link parameter and RF-FSO links parameters The diversity-multiplexing trade-off analysis is done for this network, where we show that this trade-off is independent of the primary network

Delay-Minimized Routing in Mobile Cognitive Networks for Time-Critical Applications

Abstract: Cognitive radio significantly mitigates the spectrum scarcity for various applications built on wireless communication. Current techniques on mobile cognitive ad hoc networks (MCADNs), however, cannot be directly applied to time-critical applications due to channel interference, node mobility as well as unexpected primary user activities. In multichannel multiflow MCADNs, it becomes even worse because multiple links potentially interfere with each other. In this paper, we propose a delay-minimized routing (DMR) protocol for multichannel multiflow MCADNs. First, we formulate the DMR problem with the objective of delay minimization. Next, we propose a delay prediction model based on a conflict probability. Finally, we design the minimized path delay as a routing metric, and propose a heuristic joint routing and channel assignment algorithm to solve the DMR problem. Our DMR can find out the path with a minimal end-to-end (e2e) delay for time-critical data transmission. NS2-based simulation results demonstrate that our DMR protocol significantly outperforms related proposals in terms of average e2e delay, throughput, and packet loss rate.

Energy-Assisted Decode-and-Forward for Energy Harvesting Cooperative Cognitive Networks

Abstract: In this paper, we consider a simultaneous wireless information and power transfer (SWIPT)-enabled cooperative cognitive network that addresses energy scarcity and spectral scarcity, two important issues in 5G wireless communications In the considered network, the self-sustainable, SWIPT-enabled relay assists primary user’s transmission, while the relay itself is also a secondary user with its own information superimposed on the regenerated primary information for transmission The SWIPT relay employs the proposed energy-assisted decode-and-forward (EDF) protocol, which enhances the conventional decode-and-forward (DF) protocol with energy-dimension-augmented information decoding We conduct a comparative analysis of the proposed EDF and the conventional DF and amplify-and-forward (AF) protocols in this SWIPT cooperative cognitive framework in terms of capacity, outage probability, and throughput for both primary and secondary networks Simulation corroborates the analysis and demonstrates performance advantages of EDF over DF/AF from various perspectives

Performance of Adaptive Link Selection with Buffer-Aided Relays in Underlay Cognitive Networks

Abstract: In this paper, we investigate the performance of a three-node dual-hop cognitive radio network with a half-duplex decode-and-forward buffer-aided relay. We derive expressions for the average rate and symbol error rate (SER) performance of an adaptive link selection based channel-aware buffer-aided relay scheme that imposes peak-power and peak-interference constraints on the secondary nodes, and compare them with those of conventional non-buffer-aided relay and conventional buffer-aided relay schemes for a delay-tolerant system. For a finite-sized buffer, we analyze the performance of a modified threshold-based scheme for fixed-rate transmission. We analyze the tradeoffs between the delay, throughput, and SER. Computer simulation results are presented to demonstrate accuracy of the derived expressions.

Planning of Ultra-Dense Wireless Networks

Abstract: Fifth generation (5G) wireless networks adopt the deployment of ultra-dense small cells for efficient slicing of radio resources. This conceptual change in network structure aims to meet the rapid increase in mobile data traffic and connected devices. However, limited free spectrum and dynamic assignment of resources are main concerns when considering the cognitive small cells solution. Therefore, there is a need to map traffic patterns with the number of cognitive small cells to provide an optimized network architecture operating with adequate spectrum resources. This article investigates the case when network densification exceeds the radio resource capacity, causing a large scale overlapping in cell coverage area and used channels. Taking into consideration cognitive network performance characteristics, we identify two spectrum coexistence frameworks, Space Filling and Time Filling, to improve spectrum utilization and scalability for moderately large networks. Simulations show that there is a turning point when network performance starts to decline as the number of cognitive small cells exceeds the shared resources in a site area, subject to a certain load profile. This optimization of network structure, based on spectrum transmission opportunities, brings about a new topic for operators and research communities considering small cells operating in the unlicensed band.

On the ergodic capacity of NOMA-based cognitive hybrid satellite terrestrial networks

Abstract: The cognitive hybrid satellite terrestrial networks (CHSTN) is regarded as an effective way to enhance the spectrum efficiency as well as mitigate the spectrum scarcity problem. In this paper, we introduce a non-orthogonal multiple access (NOMA) scheme, which has the ability to simultaneously serve multiple users, in the cognitive network to further improve the utilization of radio spectrum. With the help of Meijer-G functions, the analytical expression for ergodic capacity of the considered system is derived. Simulations are provided to show the advantage of introducing the NOMA scheme in the cognitive system and the effects of some parameters on the performance of the considered network.

Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance.

Abstract: In cognitive network neuroscience, the connectivity and community structure of the brain network is related to measures of cognitive performance, like attention and memory. Research in this emerging discipline has largely focused on two measures of connectivity – modularity and flexibility – which, for the most part, have been examined in isolation. The current project investigates the relationship between these two measures of connectivity and how they make separable contribution to predicting individual differences in performance on cognitive tasks. Using resting state fMRI data from 52 young adults, we show that flexibility and modularity are highly negatively correlated. We use a Brodmann parcellation of the fMRI data and a sliding window approach for calculation of the flexibility. We also demonstrate that flexibility and modularity make unique contributions to explain task performance, with a clear result showing that modularity, not flexibility, predicts performance for simple tasks and that flexibility plays a greater role predicting performance on complex tasks that require cognitive control and executive functioning. The theory and results presented here allow for stronger links between measures of brain network connectivity and cognitive processes.

Distributed Energy-Spectrum Trading in Green Cognitive Radio Cellular Networks

Abstract: Reducing the power consumption of base stations is crucial to enhancing the energy efficiency of cellular networks. As the number of mobile users increases exponentially, enhancing the spectrum efficiency is also critical in order to accommodate more users. In this paper, by exploiting the cooperation between secondary base stations (SBSs) and primary base stations (PBSs), we propose a new energy spectrum trading model to enhance the energy as well as spectrum efficiency of cellular networks. In our scheme, by leveraging cognitive radio, PBSs share some portion of their licensed spectrum with SBSs, and SBSs, in exchange, provide data service to the primary users under their coverage. We first prove that the power consumption minimization problem is NP-hard. Then, to decrease the computational complexity, we design an efficient distributed auction model including green energy aware bidding (GEAB) and adaptive bid selection (ABS) algorithms, to achieve a good approximation of the optimal solution in less time. Our simulation results show that the cooperation between PBS and SBSs via ABS and GEAB algorithms can significantly improve the energy and spectral efficiency of cellular networks by nearly doubling the number of offloaded users and reducing the PBS power consumption by up to 40% as compared to existing approaches. Furthermore, green energy utilization among SBSs is increased by nearly 25%.

Mobile Demand Profiling for Cellular Cognitive Networking

Abstract: In the next few years, mobile networks will undergo significant evolutions in order to accommodate the ever-growing load generated by increasingly pervasive smartphones and connected objects. Among those evolutions, cognitive networking upholds a more dynamic management of network resources that adapts to the significant spatiotemporal fluctuations of the mobile demand. Cognitive networking techniques root in the capability of mining large amounts of mobile traffic data collected in the network, so as to understand the current resource utilization in an automated manner. In this paper, we take a first step towards cellular cognitive networks by proposing a framework that analyzes mobile operator data, builds profiles of the typical demand, and identifies unusual situations in network-wide usages. We evaluate our framework on two real-world mobile traffic datasets, and show how it extracts from these a limited number of meaningful mobile demand profiles. In addition, the proposed framework singles out a large number of outlying behaviors in both case studies, which are mapped to social events or technical issues in the network.

Distributed opportunistic spectrum sharing in cognitive radio networks

Abstract: Summary In cases where the licensed radio spectrum is underutilized, cognitive radio technology enables cognitive devices to sense and then dynamically access this scarce resource making the most out of it. In this work, we introduce a simple and intuitive, yet powerful and efficient, technique that allows opportunistic channel access in cognitive radio systems in a completely distributed fashion. Our proposed method achieves very high values of spectrum utilization and throughput. It also minimizes interference between cognitive base stations and the primary users licensed to use the spectrum. The algorithm responds quickly and efficiently to variations in the network parameters and also achieves a high degree of fairness between cognitive base stations. Copyright © 2016 John Wiley & Sons, Ltd.

Location Privacy Preservation in Database-Driven Wireless Cognitive Networks Through Encrypted Probabilistic Data Structures

Abstract: In this paper, we propose new location privacy preserving schemes for database-driven cognitive radio networks that protect secondary users’ (SUs) location privacy while allowing them to learn spectrum availability in their vicinity. Our schemes harness probabilistic set membership data structures to exploit the structured nature of spectrum databases (DBs) and SUs’ queries. This enables us to create a compact representation of DB that could be queried by SUs without having to share their location with DB, thus guaranteeing their location privacy. Our proposed schemes offer different cost-performance characteristics. Our first scheme relies on a simple yet powerful two-party protocol that achieves unconditional security with a plausible communication overhead by making DB send a compacted version of its content to SU which needs only to query this data structure to learn spectrum availability. Our second scheme achieves significantly lower communication and computation overhead for SUs, but requires an additional architectural entity which receives the compacted version of the database and fetches the spectrum availability information in lieu of SUs to alleviate the overhead on the latter. We show that our schemes are secure, and also demonstrate that they offer significant advantages over existing alternatives for various performance and/or security metrics.

Cognitive radio network security status and challenges

Abstract: One of the important trends which is supposed to have more attention in the system of cognitive radio is wireless security models. Security requirements for CRNs are the same as the general wireless networks, taking into consideration that the frequency band changes dynamically adding a specific feature when we deal with security of CRNs. A new category of security issues and challenges have been introduced in the cognitive radio systems, and providing security models to realize good and reasonable protection must be one of the main researchers interest. This paper addresses the literature review, the security models and requirements for CRNs, layered and cross-layers attacks against CRNs.

Packet Size Optimization for Cognitive Radio Sensor Networks Aided Internet of Things

Abstract: Cognitive radio sensor networks (CRSNs) is the state-of-the-art communication paradigm for power constrained short range data communication. It is one of the potential technologies adopted for Internet of Things (IoT) and other futuristic machine-to-machine-based applications. Many of these applications are power constrained and delay sensitive. Therefore, CRSN architecture must be coupled with different adaptive and robust communication schemes to take care of the delay and energy efficiency at the same time. Considering the tradeoff that exists in terms of energy efficiency and overhead delay for a given data packet length, it is proposed to transmit the physical layer payload with an optimal packet size (OPS) depending on the network condition. Furthermore, due to the cognitive feature of CRSN architecture overhead energy consumption due to channel sensing and channel handoff plays a critical role. Based on the above premises, in this paper, we propose a heuristic exhaustive search-based Algorithm-1 and a computationally efficient suboptimal low complexity Karuh–Kuhn–Tucker (KKT) condition-based Algorithm-2 to determine the OPS in CRSN architecture using variable rate m-QAM modulation. The proposed algorithms are implemented along with two main cognitive radio assisted channel access strategies based on distributed time slotted-cognitive medium access control (DTS-CMAC) and centralized common control channel-based cognitive medium access control (CC-CMAC) and their performances are compared. The simulation results reveal that proposed Algorithm-2 outperforms Algorithm-1 by a significant margin in terms of its implementation time. For the exhaustive search-based Algorithm-1 the average time consumed to determine OPS for a given number of cognitive users is 1.2 s, while for KKT-based Algorithm-2, it is of the order of 5–10 ms. CC-CMAC with OPS is most efficient in terms of overall energy consumption but incurs more delay as compared to DTS-CMAC with OPS scheme.