Cognitive network

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

Research on the Routing Algorithm Based on QoS Requirement for Cognitive Radio Networks

Abstract: Cognitive radio network has received increasing attention in recent few years. Cognitive radio network is viewed as a novel network for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. This paper first gives a general overview of the background and concept of cognitive radio network. Then we focus on the routing algorithm based on the QoS requirement. In this paper, the characteristic of QoS requirement and the mathematical model of cognitive radio network are investigated. Finally, a novel term s which considers the effect of the bandwidth and the delay is introduced. We can take into account the term s for the QoS requirement of each session in future work, which means just a fixed value of s, rather than the bandwidth and the delay, will be requested when we provide our QoS requirement for a session.

Efficient data capturing for network forensics in cognitive radio networks

Abstract: Network forensics is widely used in tracking down criminals and detecting network anomalies, and data capture is the basis of network forensics. Compared to traditional networks, data capture faces significant challenges in cognitive radio networks. In traditional wireless networks, one monitor is usually assigned to one channel to capture traffic, which incurs very high cost in a cognitive radio network because the latter typically has a large number of channels. Furthermore, due to the uncertainty of the primary user's activity, cognitive radio devices change their operating channels randomly, which makes data capturing more difficult. In this paper, we propose a systematic method to capture data in cognitive radio networks with a small number of monitors. We utilize incremental support vector regression to predict packet arrival time and intelligently switch monitors between channels. In addition, a protocol is proposed to schedule multiple monitors to perform channel scan and packet capturing in an efficient manner. The real-world experiments and simulations show that our method is able to achieve the packet capture rate above 70% using a small number of monitors, which outperforms the random scheme by 200%–300%.

Robust sequential working memory recall in heterogeneous cognitive networks

Abstract: Psychiatric disorders are often caused by partial heterogeneous disinhibition in cognitive networks, controlling sequential and spatial working memory (SWM). Such dynamic connectivity changes suggest that the normal relationship between the neuronal components within the network deteriorates. As a result, competitive network dynamics is qualitatively altered. This dynamics defines the robust recall of the sequential information from memory and, thus, the SWM capacity. To understand pathological and non-pathological bifurcations of the sequential memory dynamics, here we investigate the model of recurrent inhibitory-excitatory brain networks with heterogeneous inhibition. We consider the ensemble of units with all-to-all inhibitory connections, in which the connection strengths are monotonically distributed at some interval. Based on computer experiments and studying the Lyapunov exponents, we observed and analyzed the new phenomenon - clustered sequential dynamics. The results are interpreted in the context of the winnerless competition principle. Accordingly, clustered sequential dynamics is represented in the phase space of the model by two weakly interacting quasi-attractors. One of them is similar to the sequential heteroclinic chain - the regular image of SWM, while the other is a quasi-chaotic attractor. Coexistence of these quasi-attractors means that the recall of the normal information sequence is intermittently interrupted by episodes with chaotic dynamics. We indicate potential dynamic ways for augmenting damaged working memory and other cognitive functions.

Modeling driver car-following based on the queuing network cognitive architecture

Abstract: Driver car-following control is a quite common activity in driving. Modeling driver car-following in a cognitive architecture can contribute to driving-related human factors research. Queuing Network-Model Human Processor (QN-MHP) is a computational cognitive architecture developed to represent human information processing as a queuing network on the basis of neuroscience and psychological findings. In this paper, using the QN-MHP cognitive architecture, we propose a driver car-following model to represent the concurrent perceptual, cognitive, and motor activities involved in the task of driver car-following. The simulation results show that this model can perform the control process of car-following well, and the results are consistent with those of driver control.

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.