Showing papers on "Cognitive network published in 2019"

Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics

Abstract: Network science provides a set of quantitative methods to investigate complex systems, including human cognition. Although cognitive theories in different domains are strongly based on a network perspective, the application of network science methodologies to quantitatively study cognition has so far been limited in scope. This review demonstrates how network science approaches have been applied to the study of human cognition and how network science can uniquely address and provide novel insight on important questions related to the complexity of cognitive systems and the processes that occur within those systems. Drawing on the literature in cognitive network science, with a focus on semantic and lexical networks, we argue three key points. (i) Network science provides a powerful quantitative approach to represent cognitive systems. (ii) The network science approach enables cognitive scientists to achieve a deeper understanding of human cognition by capturing how the structure, i.e., the underlying network, and processes operating on a network structure interact to produce behavioral phenomena. (iii) Network science provides a quantitative framework to model the dynamics of cognitive systems, operationalized as structural changes in cognitive systems on different timescales and resolutions. Finally, we highlight key milestones that the field of cognitive network science needs to achieve as it matures in order to provide continued insights into the nature of cognitive structures and processes.

DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning

Abstract: Network slicing is a new paradigm for future 5G networks where the network infrastructure is divided into slices devoted to different services and customized to their needs. With this paradigm, it is essential to allocate to each slice the needed resources, which requires the ability to forecast their respective demands. To this end, we present DeepCog, a novel data analytics tool for the cognitive management of resources in 5G systems. DeepCog forecasts the capacity needed to accommodate future traffic demands within individual network slices while accounting for the operator’s desired balance between resource overprovisioning (i.e., allocating resources exceeding the demand) and service request violations (i.e., allocating less resources than required). To achieve its objective, DeepCog hinges on a deep learning architecture that is explicitly designed for capacity forecasting. Comparative evaluations with real-world measurement data prove that DeepCog’s tight integration of machine learning into resource orchestration allows for substantial (50% or above) reduction of operating expenses with respect to resource allocation solutions based on state-of-the-art mobile traffic predictors. Moreover, we leverage DeepCog to carry out an extensive first analysis of the trade-off between capacity overdimensioning and unserviced demands in adaptive, sliced networks and in presence of real-world traffic.

Fog-Computing-Enabled Cognitive Network Function Virtualization for an Information-Centric Future Internet

Abstract: Information-centric networking (ICN) is an important trend that will impact the future of the Internet. ICN caters to large content consumption patterns while achieving high performance. New features in the information-centric future Internet, such as caching, name-based routing, and content-based security, bring novel challenges to a decentralized environment. On one hand, the processing capabilities on the edge in an information-centric future Internet need to implement smart analysis for large quantities of content. On the other hand, the computational and storage resources need to be configured and controlled on demand and based on cognition of the content from users. To address these challenges, this article proposes a fog-computing- enabled cognitive network functions virtualization approach for an information-centric future Internet. We first propose an on-demand caching function virtualization scheme and design a communication scheme between the fog nodes and the future Internet nodes for the forwarding process. Then, to attain smart control for related operations (i.e., routing, cache policy, and security), we propose a control function virtualization approach. Finally, a cognitive resource configuration mechanism is proposed. The simulation results show the advantages and efficiency of the proposed approach.

Security-Reliability Tradeoff Analysis for Cooperative NOMA in Cognitive Radio Networks

Abstract: This paper develops a tractable analysis framework to evaluate the reliability and security performance of cooperative non-orthogonal multiple access (co-NOMA) in cognitive networks, where both a primary base station (PBS) and a NOMA-strong primary user (PU) send confidential messages to multiple uniformly distributed PUs in the presence of randomly located external eavesdroppers. For constricting the interference to the PUs imposed by cognitive femto base stations (CFBSs), a mobile association scheme is introduced. Moreover, an eavesdropper-exclusion zone is introduced around the PBS for improving the secrecy performance of the primary networks. To characterize the security-reliability tradeoff of the considered network, we first derive the activation probability of CFBSs and the conditional probability density function associated with the distance between the relay user and other PUs. Then, the connection outage probability (COP) and the secrecy outage probability (SOP) of each PU with NOMA (co-NOMA) or non-cooperative NOMA (nco-NOMA) are separately derived to obtain the overall COP and SOP in the primary networks. Finally, the tradeoff between COP and SOP with co-NOMA (identified as transmission SOP) is investigated for simultaneously reflecting the security and reliability. Numerical results demonstrate the performance improvements of the proposed co-NOMA scheme in comparison to that of the nco-NOMA scheme in terms of different parameters. Furthermore, the security-reliability tradeoff performance of co-NOMA is shown.

A cognitive network management system to improve QoE in stereoscopic IPTV service

Abstract: Polytechnic University of Valencia, Grant/Award Number: PAID-15-11; Ministerio de Ciencia e Innovacion, Grant/Award Number: TEC2011-27516

Interdependent Strategic Security Risk Management With Bounded Rationality in the Internet of Things

Abstract: With the increasing connectivity enabled by the Internet of Things (IoT), security becomes a critical concern, and users should invest to secure their IoT applications. Due to the massive devices in the IoT network, users cannot be aware of the security policies taken by all its connected neighbors. Instead, a user makes security decisions based on the cyber risks that he perceives by observing a selected number of nodes. To this end, we propose a model which incorporates the limited attention or bounded rationality nature of players in the IoT. Specifically, each individual builds a sparse cognitive network of nodes to respond to. Based on this simplified cognitive network representation, each user then determines his security management policy by minimizing his own real-world security cost. The bounded rational decision-makings of players and their cognitive network formations are interdependent and thus should be addressed in a holistic manner. We establish a games-in-games framework and propose a Gestalt Nash equilibrium (GNE) solution concept to characterize the decisions of agents and quantify their risk of bounded perception due to the limited attention. In addition, we design a proximal-based iterative algorithm to compute the GNE. With case studies of smart communities, the designed algorithm can successfully identify the critical users whose decisions need to be taken into account by the other users during the security management.

Cooperative Spectrum Handovers in Cognitive Radio Networks

Abstract: Cognitive radio systems require the absorption of cooperative spectrum sensing among cognitive network users to increase the reliability of detection. We have found that cooperative spectrum sensing is not only advantageous but is also essential to avoid interference with any primary network users. Interference by licensed users becomes a chief concern and issue, which affects primary as well as secondary users leading to restrictions in spectrum sensing in cognitive radios. Cognitive radio spectrum sensing ability to identify and make use of vacant spaces in the spectrum without causing any interference to the primary user is elaborately studied. An overview about spectrum sensing is given, and an effective system model based on conventional and cooperative sensing model is proposed. It is reviewed along with various cooperative handover factors, and a dynamic technique called CUSUM algorithm is devised. The efficiency of the proposed cooperative CUSUM spectrum sensing algorithm performs better than existing optimal rules based on a single observation spectrum sensing techniques under cooperative networks.

Joint Spectrum Sensing and Packet Error Rate Optimization in Cognitive IoT

Abstract: Massive wireless connections are emerging in Internet of Things (IoT) and will lead to a severe spectrum scarcity issue. To deal with this issue, we introduce the cognitive radio technology into the IoT, namely, cognitive IoT. Different from a conventional cognitive network, the cognitive IoT is dominated by short-packet transmissions, which suffer from a significant packet error rate even when the transmission rate is smaller than the Shannon capacity. In this paper, we jointly optimize the spectrum sensing time and packet error rate to maximize the cognitive effective-throughput, which is defined as the effective transmission rate by considering the packet error rate. First, we formulate an instantaneous effective-throughput maximization problem with the instantaneous channel state information (CSI) between cognitive transceivers, and develop a successive optimization algorithm. Second, we formulate an average effective-throughput maximization problem with the statistical CSI between cognitive transceivers. Due to the complicated expression of the average effective-throughput, we analyze its closed-form expression and adopt an exhaustive search method to obtain the optimal solution. Numerical and simulation results reveal that, the packet length has a significant impact on the optimal design. Meanwhile, the proposed algorithms can almost maximize the instantaneous/average effective-throughput.

Between-network Functional Connectivity Is Modified by Age and Cognitive Task Domain.

Abstract: Research on the cognitive neuroscience of aging has identified myriad neurocognitive processes that are affected by the aging process, with a focus on identifying neural correlates of cognitive function in aging. This study aimed to test whether internetwork connectivity among six cognitive networks is sensitive to age-related changes in neural efficiency and cognitive functioning. A factor analytic connectivity approach was used to model network interactions during 11 cognitive tasks grouped into four primary cognitive domains: vocabulary, perceptual speed, fluid reasoning, and episodic memory. Results showed that both age and task domain were related to internetwork connectivity and that some of the connections among the networks were associated with performance on the in-scanner tasks. These findings demonstrate that internetwork connectivity among several cognitive networks is not only affected by aging and task demands but also shows a relationship with task performance. As such, future studies examining internetwork connectivity in aging should consider multiple networks and multiple task conditions to better measure dynamic patterns of network flexibility over the course of cognitive aging.

Secrecy Performance of Small-Cell Networks With Transmitter Selection and Unreliable Backhaul Under Spectrum Sharing Environment

Abstract: We investigate the secrecy performance of an underlay small-cell cognitive radio network under unreliable backhaul connections. The small-cell network shares the same spectrum with the primary network, ensuring that a desired outage probability constraint is always met in the primary network. To improve the security of the small-cell cognitive network, we propose three sub-optimal small-cell transmitter selection schemes, namely sub-optimal transmitter selection, minimal interference selection, and minimal eavesdropping selection. Closed-form expressions of the non-zero secrecy rate, secrecy outage probability, and ergodic secrecy capacity are provided for the schemes along with asymptotic expressions. We also propose an optimal selection scheme and compare performances with the sub-optimal selection schemes. Computable expressions for the non-zero secrecy rate and secrecy outage probability are presented for the optimal selection scheme. Our results show that by increasing the primary transmitter's power and the number of small-cell transmitters, the system performance improves. The selection scheme, the backhaul reliability, and the primary user quality-of-service constraint also have a significant impact on secrecy performance.

Dynamic Access Point and Service Selection in Backscatter-Assisted RF-Powered Cognitive Networks

Abstract: In this paper, we investigate the dynamic access point and service selection in a backscatter-assisted radio-frequency-powered cognitive network, where many secondary transmitters (STs) can choose different transmission services provided by multiple access points. To analyze the access point and service selection of the STs, we formulate the problem as an evolutionary game. The STs act as the players and adjust their selections of the access points and services based on their utilities. Specifically, we model the access point and service adaptation of the STs by the replicator dynamics, and analytically prove the existence and uniqueness, and the stability of the evolutionary equilibrium. We also consider the delay of information used by the STs to adapt their selection and perform the analysis by using delayed replicator dynamics. In particular, the stability region of the delayed replicator dynamics in a special case is derived. Furthermore, we develop a low-complexity algorithm for the access point and service selection in the network based on evolutionary game. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed access point and service selection strategy in the network.

Primary user characterization for cognitive radio wireless networks using a neural system based on Deep Learning

Abstract: Cognitive radio is a paradigm that proposes maximizing the utilization of the usable radio-electric spectrum, allowing licensed users (PUs) and non-licensed users (SUs) to simultaneously coexist through the dynamic management and assignment of spectrum resources, by integrating the stages of spectrum sensing, decision, sharing and mobility. Spectrum decision is one of the most important stages, but its optimal operation depends on the characterization sub-stage, which is in charge of efficiently estimating time gaps in which a PU won’t make use of the assigned spectrum, so that it can be used in an opportunistic fashion by SUs. The design and implementation of an algorithm based on the Long Short-Term Memory (LSTM) recurrent neural network is proposed in order to increase the success percentage in the forecasting (presence/absence) of PUs in spectrum channels. The accuracy level exhibited in the results indicates LSTM increases the prediction percentage as compared to the Multilayer Perceptron Neural Network (MLPNN) and the Adaptative Neuro-Fuzzy Inference System (ANFIS) learning models, which means it could be implemented in cognitive networks with centralized physical topologies.

Dynamic dual threshold cooperative spectrum sensing for cognitive radio under noise power uncertainty

Abstract: The spectrum sensing performance depends on the accuracy of the detection about whether primary users are busy or idle. Previous studies on cognitive radio spectrum sensing have shown that the cooperation between secondary users can improve their spectrum detection performance in real cognitive networks. Aiming at the problem of threshold mismatch of energy detectors under noise power uncertainty, a cooperative spectrum sensing method with dynamic dual threshold is proposed. Firstly, the utility function is defined with the objective of minimizing the error probability of spectrum sensing, and the optimum threshold of energy detector is derived. Secondly, in order to mitigate the influence derived from noise uncertainty, an effective dynamic dual threshold adjustment mechanism is presented, and the optimizing combinative fusion rule is discussed with the prerequisite of the minimum global error probability. In addition, in view of insufficient number of cognitive users whose sensing results lie in decision zones, the parameter of credibility is defined to choose the secondary users with reliable local detection for final fusion. Simulation results show that our proposed method can mitigate the influence of noise uncertainty and increase the spectrum sensing accuracy compared with other existing methods.

Spectrum Prediction in Cognitive Radio with Hybrid Optimized Neural Network

Abstract: Modern radio networks promote the usage and emergence of new age technologies through enabling lay users to utilize superior gadgets without external assistance. Cognitive Radio technology, an emergent new age product, established the possibility for unlicensed cognitive users to access radio frequencies across a spectrum hole and understand its implications via spectrum sensing mechanisms. Since unlicensed users are not one of the primary groups that utilize the above technology, it poses a challenge to the use of spectrum prediction as there are several subtopics under this category, namely, prediction of channel statuses, ‘activities of Primary Users’, environment of radio and rate of transmission. In this paper, a new class of optimization heuristics called hybrid optimization is used. This will implement two or more algorithms for the same optimization. A Genetic Algorithm along with Particle Swarm Optimization (GAPSO) method is proposed with a Back-Propagation Neural Network (BPNN) as a novel supervised learning algorithm for predicting spectrum patterns in cognitive radio networks.

Interdependent Strategic Security Risk Management with Bounded Rationality in the Internet of Things.

Abstract: With the increasing connectivity enabled by the Internet of Things (IoT), security becomes a critical concern, and the users should invest to secure their IoT applications. Due to the massive devices in the IoT network, users cannot be aware of the security policies taken by all its connected neighbors. Instead, a user makes security decisions based on the cyber risks he perceives by observing a selected number of nodes. To this end, we propose a model which incorporates the limited attention or bounded rationality nature of players in the IoT. Specifically, each individual builds a sparse cognitive network of nodes to respond to. Based on this simplified cognitive network representation, each user then determines his security management policy by minimizing his own real-world security cost. The bounded rational decision-makings of players and their cognitive network formations are interdependent and thus should be addressed in a holistic manner. We establish a games-in-games framework and propose a Gestalt Nash equilibrium (GNE) solution concept to characterize the decisions of agents, and quantify their risk of bounded perception due to the limited attention. In addition, we design a proximal-based iterative algorithm to compute the GNE. With case studies of smart communities, the designed algorithm can successfully identify the critical users whose decisions need to be taken into account by the other users during the security management.

Effect of Imperfect Channel Estimation on the Performance of Cognitive Satellite Terrestrial Networks

Abstract: The incorporation of cognitive radio (CR) techniques into satellite communication systems, has been considered as one of the most promising approaches to address the spectrum scarcity, which constitutes an advanced infrastructure known as cognitive satellite terrestrial network (CSTN). Due to the estimation error or feedback delay, perfect channel state information (CSI) of satellite and/or terrestrial links in CSTNs are normally unavailable. This paper investigates the effect of imperfect CSI on the performance of CSTN, where a secondary satellite network coexists with a primary terrestrial network by employing the underlay cognitive mechanism according to which the satellite user is allowed to access the licensed spectrum without deteriorating the operation of terrestrial user. Specifically, we derive the analytical expressions for the outage probability and ergodic capacity of the cognitive network, which provides an efficient approach to jointly evaluate the impacts of imperfect channel estimations for different links on the performance of considered network. Moreover, simple asymptotic outage probability formula in the high signal-to-noise ratio regime is presented to reveal the diversity order and coding gain of the CSTNs. Finally, numerical results are provided to confirm the validity of the theoretical analysis, as well as quantitatively analyze the effects of various system parameters on the performance of the CSTNs with CSI imperfection.

Motor-Cognitive Neural Network Communication Underlies Walking Speed in Community-Dwelling Older Adults.

Abstract: While walking was once thought to be a highly automated process, it requires higher-level cognition with older age. Like other cognitive tasks, it also becomes further challenged with increased cognitive load (e.g., the addition of an unrelated dual task) and often results in poorer performance (e.g., slower speed). It is not well known, however, how intrinsic neural network communication relates to walking speed, nor to this "cost" to gait performance; i.e., "dual-task cost (DTC)." The current study investigates the relationship between network connectivity, using resting-state functional MRI (rs-fMRI), and individual differences in older adult walking speed. Fifty participants (35 females; 84 ± 4.5 years) from the MOBILIZE Boston Study cohort underwent an MRI protocol and completed a gait assessment during two conditions: walking quietly at a preferred pace and while concurrently performing a serial subtraction task. Within and between neural network connectivity measures were calculated from rs-fMRI and were correlated with walking speeds and the DTC (i.e., the percent change in speed between conditions). Among the rs-fMRI correlates, faster walking was associated with increased connectivity between motor and cognitive networks and decreased connectivity between limbic and cognitive networks. Smaller DTC was associated with increased connectivity within the motor network and increased connectivity between the ventral attention and executive networks. These findings support the importance of both motor network integrity as well as inter-network connectivity amongst higher-level cognitive networks in older adults' ability to maintain mobility, particularly under dual-task (DT) conditions.

Power allocation policy and performance analysis of secure and reliable communication in cognitive radio networks

Abstract: This paper investigates the problem of secure and reliable communications for cognitive radio networks. More specifically, we consider a single input multiple output cognitive model where the secondary user (SU) faces an eavesdropping attack while being subject to the normal interference constraint imposed by the primary user (PU). Thus, the SU must have a suitable power allocation policy which does not only satisfy the constraints of the PU but also the security constraints such that it obtains a reasonable performance for the SU, without exposing information to the eavesdropper. We derive four power allocation policies for different scenarios corresponding to whether or not the channel state information of the PU and the eavesdropper are available at the SU. Further, we introduce the concept secure and reliable communication probability (SRCP) as a performance metric to evaluate the considered system, as well as the efficiency of the four power allocation policies. Finally, we present numerical examples to illustrate the power allocation polices, and the impact of these policies on the SRCP of the SU.

Performance evaluation of underlay cognitive hybrid satellite–terrestrial relay networks with relay selection scheme

Abstract: The applicability of cognitive radio (CR) techniques into satellite communications has received considerable attention in recent years. This study considers the integration of relay transmission into CR-based satellite terrestrial network, which offers the advantages of not only increasing the overall spectral efficiency by spectrum sharing but also extending the wireless coverage through the deployment of relays. Specifically, the authors derive the exact closed-form expression of the outage probability (OP) for the cognitive network, which provide an efficient means to evaluate the impact of key parameters on the system performance. Furthermore, the asymptotic OP expression at high signal-to-noise ratio is presented, which reveals the achievable diversity order and coding gain of the considered network. Finally, numerical results are carried out to validate the theoretical results, and shows that although a higher transmit power or weaker shadowing severity of the satellite interference link has a severe detrimental impact on the outage performance of the cognitive network by reducing the coding gain, the achievable diversity order only depends on the fading severities and the number of relays of the cognitive network.

Cognitive Software-Defined Networking Using Fuzzy Cognitive Maps

Abstract: Future networks are expected to provide improved support for several different kinds of applications and services. All these services will have diverse characteristics and requirements to be satisfied. A potential technology to upgrade efficiently and effectively current generation networks is virtualization via network “softwarization.” This approach requires the combination of software-defined networking (SDN) and network function virtualization. Nevertheless, such a new complex network structure will raise further issues and challenges to be solved both reactively and proactively, without human intervention. In order to achieve that, academia and industry have identified the solution in the implementation and deployment of machine learning. Hence, very likely, 5G (and especially beyond 5G) networks will be cognitive virtualized networks. In that context, this paper proposes a cognitive SDN architecture based on fuzzy cognitive maps (FCMs). First, specific design modifications of FCMs are proposed to overcome some well-known issues of this learning paradigm. Second, the efficient integration with an SDN architecture is presented and analyzed. Finally, the emulation of a sample network scenario via Mininet is provided to validate the effectiveness and the potential of the new cognitive system and its capability to act and to adapt independently of human intervention.

Distributed Stable Routing with Adaptive Power Control for Multi-Flow and Multi-Hop Mobile Cognitive Networks

Abstract: In most existing routing algorithms for mobile ad hoc cognitive networks (MACNets), nodes are configured with a fixed even maximal transmission power. Links set up by such the algorithms often suffer from excessive co-channel interference, which significantly downgrades performance of MACNets because the link stability highly depends on not only node mobility but also co-channel interference. In this paper, we investigate how to improve route stability through jointly considering routing with adaptive power adjustment and mobility control. We first propose a set of power adjustment policies which dynamically sets up transmission power of cognitive nodes to enable the duration of links potentially interfered as long as possible. We, then, design a routing metric called integrated link stability (ILS) to quantitatively measure the link stability. This novel metric ILS considers both node mobility and channel interference. Finally, we propose a Joint Stable Routing and Adaptive Power Adjustment (J-SRAPA) algorithm for multi-flow and multi-hop MACNets, with the objective of maximizing network throughput. J-SRAPA dynamically adjusts the transmission power in a distributed way to mitigate co-channel interference and to improve channel utilization ratio accordingly, during both route setup and data transmission. NS2-based simulation results demonstrate that our J-SRAPA significantly outperforms related routing algorithms in terms of network throughput, end-to-end transmission delay, and packet loss ratio; and the higher channel interference degree MACNets experience, the more improvement our J-SRAPA will bring to the networks.

QoE Oriented Cognitive Network Based on Machine Learning and SDN

Abstract: With the aim of improving application Quality of Experience (QoE), this paper presents a framework for a QoE oriented cognitive network that enables the implementation of a machine learning model in SDN architecture. Software Defined Networking (SDN) technology is applied to dynamically manage and orchestrate end-to-end network resources as per application needs and network condition and scenario. A structured approach is applied to implementing Machine Learning (ML) techniques within the network. A ML approach is intended to be used to autonomously learn the best management strategy for a given application and best fulfill its requirements. The framework is based on the combined SDN and ML approach, combining information obtained from both the SDN North Bound Interface (NBI) and South Bound Interface to assess both the network and application state and condition. A module built on the SDN controller uses this information to correlate network level metrics with application condition. This module will learn how the features of the network effect the application condition. This information is then used to make decisions with regards to network resources for the application. The framework is structured into three main modules: data collection and aggregation, network/application learning and network management with prediction. The proposed framework is intended to be used for investigation into link types and their effects on end-to-end path selection for application flows in SDN. This is essential for future networks as more diverse applications are expected to enter the mobile domain with each application flow traversing a range of link types.

Cognitive Heterogeneous Networks with Multiple Primary Users and Unreliable Backhaul Connections

Abstract: Wireless backhaul has emerged as a suitable and flexible alternative to wired backhaul; however, it is not as reliable as its wired counterpart. This paper presents, for the first time, a comprehensive model including a heterogeneous underlay cognitive network with small cells also acting as multiple secondary users, multiple primary users, and unreliable wireless backhaul. In this system, a macro-base station connects to multiple secondary transmitters via wireless backhaul links. In addition, multiple secondary transmitters send information to a secondary receiver by sharing the same spectrum with multiple primary users. A Bernoulli process is adopted to model the backhaul reliability. A selection combining protocol is used at the secondary receiver side to maximize the received signal-to-noise ratio. We investigate the impact of the number of secondary transmitters, the number of primary users, as well as the backhaul reliability on the system performance in Rayleigh fading channels. Two key constraints are considered on the system performance: 1) maximum transmit power at the secondary transmitters and 2) peak interference power at the primary users caused by secondary transmitters. Closed-form expressions for outage probability, ergodic capacity, and symbol error rate and the asymptotic expressions for outage probability and symbol error rate are derived. Moreover, closed-form expressions are also applicable to non-cooperative scenarios.

Optimal Mode Selection for Full-Duplex Enabled D2D Cognitive Networks

Abstract: Full-Duplex (FD) and Device-to-Device (D2D) communications have been recognized as one of the successful solutions of spectrum scarcity in 5G networks. Significant advancements in self-interference-to-power-ratio (SIPR) reduction have paved the way for FD use to double the data rates and reduce the latency. This advantage can now be exploited to optimize dynamic spectrum sharing among different radio access technologies in cognitive networks. However, protecting the primary user communication has been a challenging problem in such coexistence. In this paper, we provide an abstract level analysis of protecting primary users reception based on secondary users FD enabled communication. We also propose optimal mode selection (Half-duplex, Full-duplex, or silent) for secondary D2D users depending on its impact on primary users. Our analysis presents the significant advantage of D2D mode selection in terms of efficient spectrum utilization while protecting the primary user transmission, thus, leading the way for FD enabled D2D setup. Depending on the location and transmit power of D2D users, the induced aggregate interference should not violate the interference threshold of primary users. For this, we characterize the interference from D2D links and derive the probability for successful D2D users for half-duplex and full-duplex modes. The analyses are further supported by theoretical and extensive simulation results.

Tracking the Dynamic Functional Network Interactions During Goal-Directed Auditory Tasks by Brain State Clustering.

Abstract: Both perceiving and processing external sound stimuli as well as actively maintaining and updating relevant information (i.e., working memory) are critical for communication and problem solving in everyday acoustic environments. The translation of sensory information into perceptual decisions for goal-directed tasks hinges on dynamic changes in neural activity. However, the underlying brain network dynamics involved in this process are not well specified. In this study, we collected functional MRI data of participants engaging in auditory discrimination and auditory working memory tasks. Independent component analysis (ICA) was performed to extract the brain networks involved and the sliding-window functional connectivity (FC) among networks was calculated. Next, a temporal clustering technique was used to identify the brain states underlying auditory processing. Our results identified seven networks configured into four brain states. The number of brain state transitions was negatively correlated with auditory discrimination performance, and the fractional dwell time of State 2-which included connectivity among the triple high-order cognitive networks and the auditory network (AN)-was positively correlated with working memory performance. A comparison of the two tasks showed significant differences in the connectivity of the frontoparietal, default mode, and sensorimotor networks (SMNs), which is consistent with previous studies of the modulation of task load on brain network interaction. In summary, the dynamic network analysis employed in this study allowed us to isolate moment-to-moment fluctuations in inter-network synchrony, find network configuration in each state, and identify the specific state that enables fast, effective performance during auditory processing. This information is important for understanding the key neural mechanisms underlying goal-directed auditory tasks.

Optimal energy-efficient transmission for hybrid spectrum sharing in cooperative cognitive radio networks

Abstract: In order to improve the energy efficiency (EE) in cognitive radio (CR), this paper investigates the joint design of cooperative spectrum sensing time and the power control optimization problem for the secondary user systems to achieve the maximum energy efficiency in a cognitive network based on hybrid spectrum sharing, meanwhile considering the maximum transmit power, user quality of service (QoS) requirements, interference limitations, and primary user protection. The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem. The Dinkelbach's method is adopted to solve this problem and to transform the non-convex optimization problem in fractional form into an equivalent optimization problem in the form of subtraction. Then, an iterative power allocation algorithm is proposed to solve the optimization problem. The simulation results show the effectiveness of the proposed algorithms for energy-efficient resource allocation in the cognitive network.

Dynamic Spectrum Handovers in Cognitive Radio Networks

Abstract: Wireless systems, spectrum handover, and radio networks have experienced a drastic transition due to modern-day technological initiatives. This advancement is evident in the static spectrum, which is a feasible resolution to the dynamic status of wireless networks and necessitates the reassurance of networking alternatives related to spectrum handovers. Cognitive networks are efficient and most effective initiative to ensuring dynamic spectrum handovers that will exploit the usage of spectrum handover are distributed to other neighboring potential devices. The application of the Cognitive Radio (CR) and its capabilities signals the nodes, which are unrestrained to the usage of static spectrum, other than selecting it based on its ultimatum. Conversely, the utility of dynamic spectrum leads to some problems that have to be discussed in further details: effective apportionment of the spectrum between CR users and licensed users aimed at maximizing the usage of the spectrum. The second problem is the avoidance of interferences with devices’ levels. Hence, this paper critically analyzes the dynamic spectrum handovers in cognitive networks by analyzing previous literature first. By evaluating diverse dynamic spectrum schemes and models, this research includes a discussion of problems and proposition of novel resolutions for the allocation of spectrums applying the multi-agent systems. The results of simulation indicate that this mitigating factor signifies approximately 80% of spectrum usages in a few message spans hence providing a fundamental mechanism for the handover of the dynamic spectrum.