Cognitive network

About: Cognitive network is a research topic. Over the lifetime, 4213 publications have been published within this topic receiving 107093 citations.

Game Theory for Security in Cognitive Radio Networks

Abstract: Cognitive Radio Networks (CRNs) appear as a probable solution for the shortage of spectrum. However, the Security in cognitive radio network becomes a challenging issue, since more chances are given to attackers by cognitive radio technology compared to general wireless network. These chances may cause degradation the network quality of service but currently there are no specific secure protocols for cognitive radio networks. By this motivation, we present the state of art in security of cognitive radio network. In addition, we present the existing game theory models and non-game theory model for security issues in CRN. The attacks in different protocols layers were also investigated.

Deep-learning based Cooperative Spectrum Prediction for Cognitive Networks

Abstract: The concept of dynamic spectrum access under the basic principles of Cognitive Radio (CR) networks is considered to alleviate the current inefficient use of radio spectrum. Relying on the presumed knowledge of the radio environment, CR opportunistically allows secondary users to access licensed spectrum bands when not in use by their respective owners (primary users), in a non-interference manner. As identifying and exploiting the available spectrums is time and energy consuming, different spectrum prediction methodologies are used in order to provide meaningful data about current and future spectrum usage in specific time and location. However, local spectrum prediction done from a single SU`s perspective can be unreliable and prone to error under harsh channel conditions. Where applicable, cooperative spectrum prediction in multi-user environment has the potential to overcome the limitations of local spectrum prediction accuracy. In this paper, cooperative spectrum prediction with neural network predictors is investigated. Considering heterogeneous CR network, soft cooperative fusion is implemented to exploit the spatial dependency of spectrum measurement data and provide occupancy predictions for an SU in near proximity range without it conducting spectrum sensing.

Reinforcement Learning-based Spectrum Sharing for Cognitive Radio

Abstract: This thesis investigates how distributed reinforcement learning-based resource assignment algorithms can be used to improve the performance of a cognitive radio system. Decision making in most wireless systems today, including most cognitive radio systems in development, depends purely on instantaneous measurement. The purpose of this work is to exploit the historical information the cognitive radio device has learned through the interactions with the unknown environment. Two system architectures have been investigated in this thesis. A point-to-point architecture is examined first in an open spectrum scenario. Then, for the first time distributed reinforcement learning-based algorithms are developed and examined in a novel two-hop architecture for Beyond Next Generation Mobile Network. The traditional reinforcement learning model is modified in order to be applied to a fully distributed cognitive radio scenario. The inherent exploration versus exploitation trade-off seen in reinforcement learning is examined in the context of cognitive radio. A two-stage algorithm is proposed to effectively control the exploration phase of the learning process. This is because cognitive radio users will cause a higher level of disturbance in the exploration phase. Efficient exploration algorithms like pre-partitioning and weight-driven exploration are proposed to enable more efficient learning process. The learning efficiency in a cognitive radio scenario is defined and the learning efficiency of the proposed schemes is investigated. Results show that the performance of the cognitive radio system can be significantly enhanced by utilizing distributed reinforcement learning since the cognitive devices are able to identify the appropriate resources more efficiently. The reinforcement learning-based ‘green’ cognitive radio approach is discussed. Techniques presented show how it is possible to largely eliminate the need for spectrum sensing, along with the associated energy consumption, by using reinforcement learning to develop a preferred channel set in each device.

Cognitive computing and World Wide Wisdom (WWW

Abstract: It is recognized that the key theoretical and technical problems toward the next generation internet are not only a speed issue, but also a more fundamental issue of the increasingly demands for the sharing of computational intelligent capabilities. According to cognitive informatics [1, 2, 3, 5, 9, 14, 16, 18], the cognitive information that humans acquire, process, retain, and share can be classified into four profound forms known as knowledge, experience, skill, and wisdom. Among them, wisdom is the most advanced cognitive objects, which is a form of natural intelligence of humans that transfers a query or instruction into an action or behavior based on a well developed reasoning and judgment. However, the current Internet is still remains as an information network. Towards the development of next generation Internet as a wisdom network, the World Wide Wisdom (WWW+) network infrastructures and technologies are yet to be sough t on the basis of cognitive informatics and cognitive computers. In WWW+, each node is a cognitive computer (CC) [3, 9, 10, 15], which is a form of autonomous and intelligent computers that think, perceive, and learn. CC enables the simulation of machinable thought such as computational inferences, reasoning, and causality analyses by autonomous inferences and perceptions mimicking the mechanisms of the brain [3, 15]. The cognitive learning engine of a CC is an autonomous learning system that enables machines learn in natural languages and symbolic notations. The cognitive search engine of a CC is a machine-learning-based search system that results in cognitive knowledge acquisitions and manipulations. On the basis of the development of CCs, the next generation internet, WWW+, will be developed as a world-wide intelligent network for knowledge processing, autonomous learning, and machine-supported problem solving. The theoretical foundations for WWW+ and cognitive computing are cognitive informatics [1, 2, 3, 5, 9, 14, 16, 18], with underpinning contemporary denotational mathematics [6, 12], such as concept algebra [7], system algebra [17], realtime process algebra [4, 8], granular algebra [12], and visual semantic algebra [11]. Denotational mathematics provides a coherent set of powerful mathematical means and explicit expressive power for the design, modeling, and implementation of cognitive computers and WWW+, as that of Boolean algebra for conventional computing technologies. WWW+ extends the current information-search-based Internet to wisdom providing and intelligence services that mimic and simulate the brain in the largest scope of the cyberspace in which each node plays a role as an autonomous super neural cell. As that the conventional Internet provides a solution to the “to be” category of problems for information sharing based on searching technologies, the WWW+-based internet solves the advanced “to do” category of problems for wisdom and intelligence capability sharing based on cognitive computing technologies. WWW+ will be the largest scope of computational intelligence and the closest embodiment of the brain as interconnected constituent intelligent components. A wide range of applications of WWW+ and cognitive computers have been identified such as, inter alia, theories, methodologies, and infrastructures of collective intelligence, networks of computational intelligence, services providing networks, distributed agent networks, distributed cognitive sensor networks, and distributed remote control systems.

Fundamentals of Cognitive Radio

Abstract: Wireless communication systems increasingly use cognition to enhance their networks. With this comprehensive resource, readers will discover how the use of cognitive techniques helps to improve wireless communication. Starting with chapters on resource allocation, cellular networks, and cognitive radio, the book progresses to game theory, variational inequalities, control theory, and reinforcement learning. The material then delves into spectrum sensing, dynamic spectrum management, and transmit power control and, ultimately, investigates the dynamics of cognitive radio networks.