Cognitive network

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

Determining website content for small businesses: assisting the planning of owner/managers

Abstract: Drawing from research on knowledge-creation processes within networks, we propose an enrichment of the notion of network capabilities based on empirical results given by the design of an interfirm web service of competencies. Our study, through the development of this ICT solution, analyse the impact of a codification process on the development and the strengthening of network capabilities. The findings show that beyond the aim of fostering information exchanges, the codification process is not neutral and impacts massively both relational and cognitive network's dimensions in order to increase its knowledge-creation potential.

Enhancing the reliability of cognitive radio networks via channel assignment: risk analysis and redundancy allocation

Abstract: A key challenge in cognitive radio networks is the unreliability of cognitive radio links due to the interruptions from primary users. To enable reliable data transmission over cognitive radio networks, the large available bandwidth obtained from cognitive radio can be used to improve the system redundancy in order to guarantee the system reliability. Using the terminology in the theory of optimal reliability design in industrial engineering, a single path data flow can be considered as a parallel-series system, in which each cut set represents a secondary user and contains several assigned channels. A heuristic algorithm is proposed to assign the channels in the licensed spectrum band to different cut sets in the parallel-series system to minimize the overall risk. By adopting the concept of cut set hazard, the algorithm is applied in both single path and multiple path situations. It is also extended to the decentralized scenario. Channel reuse, which incurs the correlation of failures in the system, is also considered for the channel assignment. Simulation shows that the proposed algorithms can significantly improve the reliability of cognitive radio networks.

Adaptive Dynamic Radio Open-source Intelligent Team (ADROIT): Cognitively-controlled Collaboration among SDR Nodes

Abstract: The ADROIT project is building an open-source software-defined data radio, intended to be controlled by cognitive applications. The goal is to create a system that enables teams of radios, where each radio both has its own cognitive controls and the ability to collaborate with other radios, to create cognitive radio teams. The desire to create cognitive radio teams, and the goal of having an open-source system, requires a rich and carefully architected system that provides great flexibility (enabling cognitive applications to change the radio's behavior) and also has a clear structure (both so that others may add or enhance the software, and also so that the system can be clearly modeled for cognitive applications). What follows is a summary of the ADROIT system and the key architectural features intended to enable cognitive radio teams.

Breaking the Area Spectral Efficiency Wall in Cognitive Underlay Networks

Abstract: In this article, we develop a comprehensive analytical framework to characterize the area spectral efficiency of a large scale Poisson cognitive underlay network The developed framework explicitly accommodates channel, topological and medium access uncertainties The main objective of this study is to launch a preliminary investigation into the design considerations of underlay cognitive networks To this end, we highlight two available degrees of freedom, ie, shaping medium access or transmit power While from the primary user's perspective tuning either to control the interference is equivalent, the picture is different for the secondary network We show the existence of an area spectral efficiency wall under both adaptation schemes We also demonstrate that the adaptation of just one of these degrees of freedom does not lead to the optimal performance But significant performance gains can be harnessed by jointly tuning both the medium access probability and the transmission power of the secondary networks We explore several design parameters for both adaptation schemes Finally, we extend our quest to more complex point-to-point and broadcast networks to demonstrate the superior performance of joint tuning policies

QoE-based vertical handover decision management for cognitive networks using ANN

Abstract: The main goal of 5G cognitive radio in heterogeneous networks is to maintain seamless connectivity and provide satisfying Quality of Service (QoS) by switching from one network to another using Vertical Handovers (VHO) The accuracy and the quickness of VHO decision is the key feature to improve and maintain high QoS levels Recently, many works have been interested to Artificial Neural Networks (ANN) as decisional algorithm for VHO It was considered efficient since it can handle a decision based on prior knowledge acquired during a learning process and takes into account many criteria of QoS This efficiency can be more improved while considering the Quality of Experience (QoE) in the ANN inputs set This paper proposes a modified multi-criteria vertical handoff decision algorithm based on ANN with QoE prediction scheme The proposed mechanism concept serves to improve the accuracy of the vertical handoff decision for radio heterogeneous networks Developed algorithm is compared to classical Fuzzy Logic (FL) and Multi Attribute Decision-Making (MADM) ones Obtained results show that QoE based ANN improve final QoS/QoE satisfaction metrics while reducing delays and the number of executed handoffs