Cognitive network

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

Outage Analysis of Multi-Antenna Cognitive Hybrid Satellite-Terrestrial Relay Networks With Beamforming

Abstract: This letter investigates the outage performance of a multi-antenna cognitive decode-and-forward (DF) hybrid satellite-terrestrial relay network (HSTRN) with beamforming (BF), where the secondary source and relay can coexist with the primary user (PU) as long as their interference imposed to PU is below a predefined threshold. Specifically, the novel closed-form expression for the outage probability (OP) of the cognitive network are derived. Furthermore, the asymptotic OP expression at high SNR is developed to reveal the diversity order and code gain of the considered network. Finally, simulation results are provided to demonstrate the validity of the theoretical results, and show the effect of antenna number and interfering satellite link on the performance of the cognitive network.

Cognitive Dynamic Systems

Abstract: The first half of the paper addresses the rationale for why we need to study cognitive dynamic systems, with particular reference to two wireless applications: cogitative radio for communication, and cognitive radar for remote sensing. The second half of the paper discusses the issues involved in dynamic spectrum management and transmit-power control, which are of particular importance to cognitive radio. The iterative water-filling algorithm, in a noncooperative radio environment is discussed, and its virtues and limitations are highlighted.

Optimal Probabilistic Initial and Target Channel Selection for Spectrum Handoff in Cognitive Radio Networks

Abstract: Spectrum mobility in cognitive radio networks not only enables the secondary users to guarantee the desired QoS of the primary users but also grants an efficient exploitation of the available spectrum holes in the network. In this paper, we propose a probabilistic approach in determining the initial and target channels for the handoff procedure in a single secondary user network. To characterize the network, a queuing theoretical framework is introduced, and “stay” and “change” handoff policies are both addressed. The performance of the secondary user in terms of average sojourn and extended service times for secondary connections is analyzed, and convex optimization problems with the objective of minimizing those times as well as analytical solutions are presented. Simulation results confirm the validity of our analytical approach.

Development of dynamic cognitive networks as complex systems approximators: validation in financial time series

Abstract: Dynamic cognitive networks (DCNs) define a novel approach to functionalize cognitive mapping and complex systems analysis, which were recently supported by fuzzy cognitive maps (FCMs). The modeling and inference limitations met in FCMs, especially in situations with strong nonlinearity and temporal phenomena, pushed towards DCNs; their theoretical framework is scheduled to confront the preceding weaknesses and offer wider possibilities in causal structures management. Trying to contribute to the enhancement of DCNs, at first, systemic and environmental metaphors are introduced with practical mathematical formalisms and generalized nomenclature. Nonlinear and asymmetric cause-effect relationships, decaying mechanisms, inertial forces, diminishing effects and biases formulate a powerful set of adaptive characteristics that strengthen the operational behavior of DCNs. Second, the strategic reorientation of DCNs is attempted as generalized approximation tools. This new strategic option is verified not only in classical function approximation tests, but also in the challenging area of securities markets. The platform of evaluation of DCNs involves comparisons with a linear multiple regression model, a feed-forward neural network trained with both back-propagation and evolution strategies, a radial basis function network, and an adaptive network-based fuzzy inference system (ANFIS). Through the experiments for short-term stock price predictions, multiple issues are analyzed not only about the role of diverse DCN parameters, but also about the given problem of financial markets modeling and forecasting. Simulations distinguish DCNs as a strong methodology with noticeable adaptability in complicated patterns and broad generalization capabilities while, at the same time, the all-embracing outcomes support previous findings of partially random walk phenomena in short-term stock market forecasting attempts.

Optimal Defense against Jamming Attacks in Cognitive Radio Networks Using the Markov Decision Process Approach

Abstract: Cognitive radio technology has become a promising approach to increase the efficiency of spectrum utilization. Since cognitive radio users are vulnerable to malicious attacks, security countermeasures are crucial to the successful deployment of cognitive radio networks in the future. In this paper, we focus on defending against the jamming attack, one of the major threats to cognitive radio networks, where several malicious attackers intend to jam the secondary user's communication link by injecting interference. We model this scenario into a jamming game, and derive the optimal strategy through the Markov decision process approach. Furthermore, a learning scheme is proposed for the secondary user to observe the wireless environment and estimate parameters such as primary users' access pattern and the number of attackers. Finally, simulation results are presented to verify the performance.