Showing papers on "Cognitive network published in 2022"

Task-specific network interactions across key cognitive domains

Abstract: Abstract Human cognition is organized in distributed networks in the brain. Although distinct specialized networks have been identified for different cognitive functions, previous work also emphasizes the overlap of key cognitive domains in higher level association areas. The majority of previous studies focused on network overlap and dissociation during resting states whereas task-related network interactions across cognitive domains remain largely unexplored. A better understanding of network overlap and dissociation during different cognitive tasks may elucidate flexible (re-)distribution of resources during human cognition. The present study addresses this issue by providing a broad characterization of large-scale network dynamics in three key cognitive domains. Combining prototypical tasks of the larger domains of attention, language, and social cognition with whole-brain multivariate activity and connectivity approaches, we provide a spatiotemporal characterization of multiple large-scale, overlapping networks that differentially interact across cognitive domains. We show that network activity and interactions increase with increased cognitive complexity across domains. Interaction patterns reveal a common core structure across domains as well as dissociable domain-specific network activity. The observed patterns of activation and deactivation of overlapping and strongly coupled networks provide insight beyond region-specific activity within a particular cognitive domain toward a network perspective approach across diverse key cognitive functions.

Evaluating the reliability, validity, and utility of overlapping networks: Implications for network theories of cognition

Abstract: Brain network definitions typically assume nonoverlap or minimal overlap, ignoring regions' connections to multiple networks. However, new methods are emerging that emphasize network overlap. Here, we investigated the reliability and validity of one assignment method, the mixed membership algorithm, and explored its potential utility for identifying gaps in existing network models of cognition. We first assessed between-sample reliability of overlapping assignments with a split-half design; a bootstrapped Dice similarity analysis demonstrated good agreement between the networks from the two subgroups. Next, we assessed whether overlapping networks captured expected nonoverlapping topographies; overlapping networks captured portions of one to three nonoverlapping topographies, which aligned with canonical network definitions. Following this, a relative entropy analysis showed that a majority of regions participated in more than one network, as is seen biologically, and many regions did not show preferential connection to any one network. Finally, we explored overlapping network membership in regions of the dual-networks model of cognitive control, showing that almost every region was a member of multiple networks. Thus, the mixed membership algorithm produces consistent and biologically plausible networks, which presumably will allow for the development of more complete network models of cognition.

Toward the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

Abstract: This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. One concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability, and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. Machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study four main areas as a means to further develop cognitive networking capabilities: networking protocol development, analysis of wireless data for modeling and simulation, development of algorithms for a multi-agent system, and spectrum sensing technology.

Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Abstract: Cognitive radios are expected to play an important role in capturing the constantly growing traffic interest on remote networks. To improve the usage of the radio range, a cognitive radio hub detects the weather, evaluates the open-air qualities, and then makes certain decisions and distributes the executives’ space assets. The cognitive radio works in tandem with artificial intelligence and artificial intelligence methodologies to provide a flexible and intelligent allocation for continuous production cycles. The purpose is to provide a single source of information in the form of a survey research to enable academics better understand how artificial intelligence methodologies, such as fuzzy logics, genetic algorithms, and artificial neural networks, are used to various cognitive radio systems. The various artificial intelligence approaches used in cognitive radio engines to improve cognition capabilities in cognitive radio networks are examined in this study. Computerized reasoning approaches, such as fuzzy logic, evolutionary algorithms, and artificial neural networks, are used in the writing audit. This topic also covers cognitive radio network implementation and the typical learning challenges that arise in cognitive radio systems.

A robust core architecture of functional brain networks supports topological resilience and cognitive performance in aging

Abstract: Aging is associated with gradual changes in cognition, yet some individuals exhibit protection against aging-related cognitive decline. The topological characteristics of brain networks that support protection against cognitive decline in aging are unknown. Here, we investigated whether the robustness of brain networks, queried via the delineation of the brain’s core network structure, supports superior cognitive performance in healthy aging individuals (n=320, ages 60-90). First, we decomposed each subject’s functional brain networks using k-shell decomposition, finding that cognitive function is associated with more robust connectivity of core nodes, primarily within the frontoparietal control network (FPCN). Next, we find that the resilience of core brain network nodes, within the FPCN in particular, relates to cognition. Finally, we show that the degree of segregation in functional networks mediates relationships between network resilience and cognition. Together, these findings suggest that brain networks balance between robust core connectivity and segregation to facilitate high cognitive performance in aging.

Decentralized Bandits with Feedback for Cognitive Radar Networks

Abstract: Completely decentralized Multi-Player Bandit models have demonstrated high localization accuracy at the cost of long convergence times in cognitive radar networks. Rather than model each radar node as an independent learner, entirely unable to swap information with other nodes in a network, in this work we construct a “central coordinator” to facilitate the exchange of information between radar nodes. We show that in interference-limited spectrum, where the signal to interference plus noise (SINR) ratio for the available bands may vary by location, a cognitive radar network (CRN) is able to use information from a central coordinator to reduce the number of time steps required to attain a given localization error. Importantly, each node is still able to learn separately. We provide a description of a network which has hybrid cognition in both a central coordinator and in each of the cognitive radar nodes, and examine the online machine learning algorithms which can be implemented in this structure.

Graph convolutional reinforcement learning for resource allocation in hybrid overlay–underlay cognitive radio network with network slicing

Abstract: Nowadays, wireless communication system is facing the problems of spectrum resource shortage. Cognitive radio technology allows cognitive users to use the spectrums authorized to primary users to improve the spectrum utilization. In this paper, a cognitive network model based on hybrid overlay–underlay spectrum access mode is established. To solve the resource allocation problem, a multi-agent resource allocation algorithm based on graph convolution reinforcement learning which combines deep Q network (DQN) and graph attention network is proposed. DQN is used for action selection and graph attention network is used to obtain the information about neighbours, so as to achieve local cooperation. The proposed algorithm can adaptively optimize cognitive network throughput, spectrum efficiency, or power efficiency by controlling the transmission power and channel selection of cognitive users. To improve the information interaction efficiency, the agent's states are divided into two categories, whether it needs to interact with neighbours or not, which shortens training time and improves convergence speed. Simulation results show that the proposed algorithm can effectively improve the power efficiency of cognitive networks. Compared with Q-learning, DQN and exiting graph convolutional reinforcement learning algorithm, the proposed algorithm has faster convergence speed and higher stability, and obtains higher network power efficiency.

Optimal Power Allocation for Achieving Secure Green Cognitive Radio Networks

Abstract: In cognitive radio networks, wireless nodes adapt to the surrounding radio environment and utilize the spectrum of licensed users. The cognitive radio environment is dynamic, and wireless channels are accessible by both legitimate and illegitimate users. Therefore, maintaining the security of cognitive radio networks is a challenging task, which must be addressed thoroughly. Further, with the recent exponential surge in wireless nodes and associated high data rate requirements, energy consumption is also growing at an unprecedented rate. Hence, energy efficiency becomes an important metric that must be considered in the design of future wireless networks. Accordingly, by considering the great ecological and economic benefits of green wireless networks, this work focus on energy-efficient resource allocation in secure cognitive radio networks. Since physical-layer security is an emerging technique that improves the security of communication devices, in this paper, an ergodic secure energy efficiency problem for a cognitive radio network is formulated with a primary user, a secondary user, and an eavesdropper. As the formulated problem is non-convex, a concave lower bound is applied to transform the original non-convex problem into a convex one. Further, by adopting the fractional programming and dual decomposition techniques, optimal power allocation strategies are obtained with the aim of maximizing the ergodic secure energy efficiency of the secondary user with constraints on the average interference power and average transmit power. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm.

A Learning Approach Towards Power Control in Full-Duplex Underlay Cognitive Radio Networks

Abstract: Adopting full-duplex (FD) technique in the underlay cognitive radio network (CRN), the secondary user can sense the activity of the primary user (PU) and transmit data simultaneously to enhance spectrum reuse efficiency. However, the sensing accuracy degrades due to the self-interference compared with the discrete sensing and transmission half-duplex network. We employ deep reinforcement learning models to analyze and solve the power control problem with the objective to minimize the adjustment steps in FD underlay CRNs. The proposed power control algorithm can help the secondary transmitter search an optimal transmit power, which satisfies pre-defined quality of service (QoS) requirements while retaining interference to the PU under a threshold. Simulation results show that compared to the benchmark, our method can achieve lower average number of transactions, and reduce more than 53.3% and 98.1% of the computing time and the storage resources, which verifies the effectiveness of our scheme.

Cascade Forward Artificial Neural Network based Behavioral Predicting Approach for the Integrated Satellite-terrestrial Networks

Abstract: Abstract In order to reduce the risk of authorized users being interrupted in the cognitive satellite wireless network, a multi-step prediction approach based on a cascaded forward artificial neural network is proposed to predict user behavior in the designed scenario. This approach uses the powerful learning ability of the cascaded forward network to analyze the historical spectrum occupancy records of licensed users, and then predict the user behavior in the next few time slots. The prediction result can help the base station in the cognitive network to schedule the dynamic access process of the cognitive users, and reduce the interference caused by the cognitive user to the authorized users. Finally, compared with traditional prediction algorithms, it is verified that the proposed multi-step prediction algorithm can effectively reduce the probability of spectrum conflicts.

Power Allocation in CRNs based on QoS and QoE

Abstract: In Cognitive Radio Networks, secondary users have access to the channel Opportunistically. So, it must be ensured that the severe interference, that damages the main network, does not occur. Using the resource allocation methods provides the best access to the spectrum for the secondary users, increases their performance, and primary users are protected from collisions. The Underlay method is one of the methods of the Cognitive Radio Network. In this method the secondary and the primary users simultaneously present in the channel. The power allocation in an underlay Cognitive Radio Network, may ensure that the secondary users do not interfere with the primary users. Using optimization techniques is a way to improve the performance of these networks. The NSGA-III algorithm, a multi-Objective optimization method, is efficient and offers acceptable results. This paper presents a power allocation method in the Underlay Cognitive Radio Networks based on the improvement of QoS and QoE, using the NSGA-III algorithm.

Geospatial Cognitive Networking Protocols and Sensing Algorithms for 5G NR Beamforming

Abstract: Although 5G New Radio (NR) has created new opportunities for cognitive radio networks, its increased physical layer security and flexibility limit the usefulness of traditional cognitive detectors such as energy and blind control channel algorithms. This paper presents CASINO-NR, a novel framework for establishing a cognitive self-reliant secondary network with no additional physical infrastructure, collaboration from the primary network nodes, and software or hardware changes to the existing 5G network. CASINO-NR includes a novel beam detection algorithm that finds and ranks 5G NR synchronization signals to determine geospatially non-interfering beams for secondary communications. We compare the developed beam detector with multiple existing approaches for sensitivity to interference and phase distortions. We also apply power control to prevent interference on neighboring beams. CASINO-NR is analyzed against the estimated throughput capacity and capabilities of other cognitive detectors found in literature. Finally, we examine an experimental beamforming example to demonstrate our beam detection algorithm and present a case for geospatial resources for cognitive radio communications.

Blockchain-Enabled Online Traffic Congestion Duration Prediction in Cognitive Internet of Vehicles

Abstract: The real-time intelligent perception and prediction of traffic situation can assist connected automated vehicles (CAVs) in path planning and reduce traffic congestion in Cognitive Internet of Vehicles (CIoVs). The centralized traffic congestion prediction solutions generally fail to adapt to the dynamic traffic environment and lead to significant communication overheads. Blockchain technology has attracted great attention in the information sharing of vehicular networks for its advantages in decentralization, transparency, traceability, and tamper-proof capability. However, due to the bottlenecks, such as high computational cost, current blockchains are incapable actuate on efficient online traffic situational cognition and prediction for CIoVs. Motivated by this, we propose a blockchain-enabled cognitive segments sharing framework for online multistep congestion duration prediction. We design a cognitive model of traffic situation based on anomaly detection and filtering mechanism to guarantee the accuracy of the cognitive segments before being packaged into the block. Furthermore, to improve the consensus efficiency, we design a credit evaluation mechanism and propose a credit-based delegated Byzantine fault tolerance (CDBFT) algorithm. Finally, we propose an online multistep prediction algorithm based on long short-term memory (LSTM) to predict future traffic congestion duration. Experimental results demonstrate that the proposed algorithms achieve shorter consensus latency and higher predictive accuracy than the existing algorithms.

Sustainable & Scalable Bio-inspired Routing Protocol in Cognitive Radio Sensor Networks

Abstract: Abstract In this paper the future communication system, such as next generation communication as 5G using some bio-inspired intelligence associated with sensor has considered. The wireless sensor networks are the emerging area with self-organization capabilities is highly motivated. The solution to this is intelligence techniques applied to develop such capabilities. Cognitive radio sensor networks are the only way to solve the multiple issues in the next generation wireless communication. The MAC layer with cognitive capabilities utilizes the spectrum band for accessing multiple channels over communication. Same intelligence techniques exists in biological applications, such as flaws of fishes, Ant colonies, Honey bees. By utilizing to these bio-inspired methods, here a sustainable and scalable routing protocol is developed for next generation communication technologies in Cognitive radio sensor networks used for spectrum sensing and sharing. Wireless Sensor Network is used unlicensed band with 2.4 GHz of frequency used for communication. In this paper, a bio-inspired routing protocol with cognitive capabilities is considered. The Primary User (PUs) as licensed band user used the channels as per requirements. Secondary Users (SUs) used the available channels and sense and shared the band as hand-off from primary to secondary users similar to Ants and Bees find the alternative path for food search, this is state-of-the-art. Here the Probabilistic approach is used for selecting channels by available users in the networks. The Protocol is simulated in NS2 simulator and results shows the protocol with cognitive capabilities shows improvement in DSR(cogmac) protocol as compare to AODV(cogmac) and AntHocNet(cogmac) to solves the routing issues.

Cognitive network spectrum allocation based on multi-agent reinforcement learning

Abstract: A more important part of the field of deep reinforcement learning is the study of multi-agents, for the specific scenario of multi-cognitive networks, the choice of spectrum will be affected by two parts, a single cognitive network and the access device under the cognitive network. In view of this specific problem, this paper uses the improved multi-agent reinforcement learning to solve, through the use of multiple agents from the user link modeling, can solve the problem of different cognitive networks for action execution when the environmental state is constantly updated, compared with the original algorithm. In the scenario where spectrum allocation is required in multi-cognitive networks, the improved algorithm can better handle the relationship between master and slave users in multiple networks, so that the spectrum utilization and the overall communication performance of the system are further improved.

A Trust-Based Relay Selection Approach to the Multi-Hop Network Formation Problem in Cognitive Radio Networks

Abstract: One of the major challenges for today’s wireless communications is to meet the growing demand for supporting an increasing diversity of wireless applications with limited spectrum resource. In cooperative communications and networking, users share resources and collaborate in a distributed approach, similar to entities of active social groups in self organizational communities. Users’ information may be shared by the user and also by the cooperative users, in distributed transmission. Cooperative communications and networking is a fairly new communication paradigm that promises significant capacity and multiplexing gain increase in wireless networks. This research will provide a cooperative relay selection framework that exploits the similarity of cognitive radio networks to social networks. It offers a multi-hop, reputation-based power control game for routing. In this dissertation, a social network model provides a humanistic approach to predicting relay selection and network analysis in cognitive radio networks.

Energy Efficiency Metrics in Cognitive Radio Networks: A Hollistic Overview

Abstract: Due to the explosive progression in the number of users for new generation wireless communication networks which includes cognitive radio networks, energy efficiency has been a fundamental factor affecting its development and performance. In order to adeptly access and analyze the energy efficiency of a cognitive radio network, a standardized metric for this purpose is required. As a starting point, in this article we provided an analysis for energy efficiency metrics of a cognitive radio network in respect to its design and operation. The performance metrics and metrics developed at the different levels of a cognitive radio network are also studied. Establishing a comprehensive metric for evaluating, measuring and reporting the energy efficiency of cognitive radio networks is a crucial step in achieving an energy-efficient cognitive radio network.

Analysis of Hardware and Software Tools for Implementation of Cognitive Radio Networks

Abstract: This paper analyzes of the hardware and software tools for implementing a cognitive radio network. In particular, software-configurable radio modules, which are considered the main element of the cognitive network, were studied according to their main characteristics, among which the widely used ones were comparatively analyzed. As a result of the analysis, the HackRF One device was selected as the most preferred option for cognitive network research, and its working principles, structure, and characteristics were revealed in detail. It also provides information about the GNU Radio software environment and its capabilities, which are used to operate the HackRF One device.

Outage performance analysis of underlay cognitive cooperative NOMA network

Abstract: In this thesis, we study the cognitive cooperative non-orthogonal multiple access network with interference power constraint in underlay mode, where a primary receiver is situated at the communication range of the secondary network. In the cognitive secondary network, the secondary originating node transmits information with the cognitive close user directly and with the cognitive remote user by the aid of multiple relays under cognitive radio constraint. Secondary originating node sends the mixed message to the cognitive close user and to multiple relays via NOMA principle. Through the signal to interference plus noise ratio between secondary originating node and multiple relays, the best relay is opted to forward the decode signal to the cognitive remote user. In order to gauge the performance of the system accurately, the exact closed form formulas for the outage probabilities of the cognitive close user and the cognitive remote user are deduced respectively over Rayleigh fading channels. Experiment results indicate that power allocation has a great influence on the performance of system for NOMA network and it is an effective way for improving the performance of cognitive cooperative NOMA network that increasing the quantity of relays.

Enhanced Spectrum Handoff Security Mechanism in Cognitive Radio Network

Abstract: Abstract: In this work we introduce a novel cognitive user emulation attack (CUEA) in a cognitive radio network (CRN), which can be exploited by intruders during spectrum handoff. Then, we propose a secure handoff mechanism that can successfully counter such an attack by introducing a coordinating cognitive user that computes the level of trust of each cognitive user based on its behavioral characteristics. Malicious users can be effectively identified by the coordinating cognitive user by looking up the trust values. The performance of the proposed mechanism is validated using MATLAB simulations. The simulation results show that the utility of the proposed mechanism in terms of its probability in correctly identifying false authentication, detection rate, throughput rate, and transmission delay.

Performance Improvement of Next-Generation Network with Cognitive Radio Network and Coordinated Multipoint Joint Transmission

Abstract: Cognitive Radio Network (CRN) is a favourable technology for the future of wireless networks. It aims to seamlessly exploit spectrum resources by opportunistically using the unused or underused radio spectrum that is licensed. Cognitive Radio Network is a technology that is planned to be used in the 5th Generation of wireless communication. This technology is belied to be the answer to the unused and underused radio spectrum in the Radio environment. Whereas Coordinated Multipoint Joint Transmission (CoMP JT) is a technology that has already been used, first used in 3GPP LTE-Advanced. Coordinated Multipoint Joint Transmission aims to improve the performance of the cellular network. In this report, Cognitive Radio Network is being implemented along with Coordinated Multipoint Joint Transmission techniques. An inclusive and layered architecture of Cognitive Radio Network along with Coordinated multipoint Joint Transmission is being designed in this report, showing different technologies and different steps involved in the concept of this combination. Then the impact of CoMP JT on CRN is being studied. By implementing CoMP JT on CRN, it has been able to show how the performance of CRN is changing when implemented alone and when implemented with CoMP JT and some of the significant results are being depicted to better understand the changes in CRN with CoMP JT. Lastly, a study is performed on the combination of Cognitive Radio Network and Coordinated Multipoint Joint Transmission. As far as the investigation is being performed, two factors have been considered, which is: Signal to Interference plus Noise Ratio (SINR) and Total Average Throughput (TAT). The detailed study is being performed in this report and is being implemented in three different environments. Hence the results are being compared and a conclusion is being drawn based on these results.

Modeling of Cognitive Bias of Video Viewing Users Based on Quantum Decision Making

Abstract: Recently, the need for watching videos as comfortably as possible in streaming services is increasing with the growing amount of video traffic. User satisfaction depends not only on the network quality but also on various factors such as the video content. Estimating the user’s Quality of Experience(QoE) and using it for network control is effective to improve user satisfaction. Therefore, we need to estimate the QoE accurately. Various QoE models using network quality have been studied. However, the QoE can vary with cognitive bias, which is a bias that occurs in our cognitive processes. For the QoE control, we need a QoE model with cognitive bias and quantum decision-making has gained attention as a method for modeling cognitive biases. In this paper, we propose a QoE model that includes cognitive bias with quantum decision-making. Then we simulate the QoE to show that our proposed method with quantum decision-making can estimate the QoE and represent video viewers’ cognitive bias.

Allocation and sharing of spectrum resources based on cognitive mechanisms

Abstract: This paper provides an overview of existing research and applications in the field of magnetic spectrum channel allocation, and finds that the demand for spectrum resources is still growing rapidly, while the limited spectrum resources are not fully utilized under the traditional fixed allocation strategy. To address the problem of allocation and sharing of magnetic spectrum resources in cognitive wireless networks, we propose an exploration of the application of cognitive reinforcement learning in the allocation and sharing of magnetic spectrum cognitive wireless networks by combining the efficient decision-making capability of reinforcement learning, the representational capability of deep learning and the self-learning capability of cognitive learning. Simulation experimental results indicate that the method can make spectrum allocation more efficient and flexible, and achieve certain results in various scenarios, while verifying the possibility of applying cognitive reinforcement learning in the field of spectrum allocation, and laying the foundation for the final realization of efficient and high-performance spectrum allocation.

Network Optimization Using Learning and Distributed Intelligence through Cognition Based Networks

Abstract: Network optimisation can be achieved with the incorporation of sophisticated technology and advanced algorithms to achieve efficiency and scalability. Optimisation of the entire network with learning and distributed intelligence through cognitive-based networks is investigated in the paper. A cognition-based network enhances the capacity for network optimization and designing processes to offer better services to the customers appropriately. With the rapid growth of users, the network distribution channels are required to adopt cognitive-based architecture for optimised capacity, capable of providing secure data transmission to a wider user base. 5G network construction with SDN and NFV, along with AI and machine learning algorithms are considered to be the most efficient cognitive-based approach for network optimisation. The application of NFV helps to develop network functions in operating open hardware platforms, reducing Capex, and OpenX and improving network design efficiently. On the other hand, SDN is capable of separating the control plane and the data plan with a defined interface for programming, providing an entire view of the network with centralised control.

Systematic Fusion of Multi-Source Cognitive Networks With Graph Learning - A Study on Fronto-Parietal Network

Abstract: Cognitive tasks induce fluctuations in the functional connectivity between brain regions which constitute cognitive networks in the human brain. Although several cognitive networks have been identified, consensus still cannot be achieved on the precise borders and distribution of involved brain regions for each network, due to the multifarious use of diverse brain atlases in different studies. To address the problem, the current study proposed a novel approach to generate a fused cognitive network with the optimal performance in discriminating cognitive states by using graph learning, following the synthesization of one cognitive network defined by different brain atlases, and the construction of a hierarchical framework comprised of one main version and other supplementary versions of the specific cognitive network. As a result, the proposed method demonstrated better results compared with other machine learning methods for recognizing cognitive states, which was revealed by analyzing an fMRI dataset related to the mental arithmetic task. Our findings suggest that the fused cognitive network provides the potential to develop new mind decoding approaches.

Fuzzy logic based authentication in cognitive radio networks

Abstract: <p>Security is a critical issue in cognitive radio networks because the cognitive node enters and variably leaves the spectrum, so it is difficult to process communication secretly. We suggested a fuzzy logic-based implicit authentication mechanism to be a solution for the confusion if there were any cognitive node doubts it to be unauthentic, and to improve user privacy in cognitive radio networks. Using a fuzzy logic technique, the proposed scheme computed certification based on proposed feedback. When a cognitive node needs to join the network, it is verified by using fuzzy logic if the node was authenticated or not. Our proposed fuzzy logic's results implicit authentication proved that it was a very successful and applicable scheme on cognitive radio networks, and it will be able to make an effective final decision in the context of incompleteness, ambiguity, and heterogeneity</p>