Cognitive network

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

SliceNet: End-to-End Cognitive Network Slicing and Slice Management Framework in Virtualised Multi-Domain, Multi-Tenant 5G Networks

Abstract: Network slicing has emerged as a major new networking paradigm for meeting the diverse requirements of various vertical businesses in virtualised and softwarised 5G networks. SliceNet is a project of the EU 5G Infrastructure Public Private Partnership (5G PPP) and focuses on network slicing as a cornerstone technology in 5G networks, and addresses the associated challenges in managing, controlling and orchestrating the new services for users especially vertical sectors, thereby maximising the potential of 5G infrastructures and their services by leveraging advanced software networking and cognitive network management. This paper presents the vision of the SliceNet project, highlighting the gaps in existing work and challenges, the proposed overall architecture, proposed technical approaches, and use cases.

System Design for Cognitive Radio Conmunications

Abstract: The rising number and capacity requirements of radio systems bring about an increasing demand for frequency spectrum. Cognitive radio offers a tempting solution to this problem by proposing opportunistic usage of frequency bands that are not occupied by their licensed users. Since it is a rather new concept, there is no consensus on the practical implementation of cognitive radio communications. In this paper, a comprehensive system design for cognitive radio is presented. The details of spectrum sensing and dynamic spectrum shaping, which constitute the basics of opportunistic spectrum usage, are given. It is proposed to transmit the spectrum sensing related information via on-off keying (OOK) modulated ultrawideband (UWB). The limits on the range of one-to-one cognitive communications are discussed. A processing gain based approach that leads to an increased range for cognitive networks is proposed. Different signaling options for the real data communications are given, and among these options an impulse radio that employs raised cosine filters is investigated in detail.

Modeling and Validation of Channel Idleness and Spectrum Availability for Cognitive Networks

Abstract: The potential of successful cognitive radio networks operating in TV White Spaces (and other future bands re-allocated for unlicensed operation) has led to significant upsurge of interest in their design optimization - particularly those that are cross-layer in nature, involving both MAC protocols as well as physical layer aspects such as channel sensing Typically, these seek to optimize a network-level metric (notably, aggregate throughput) of secondary (cognitive) network subject to interference constraints on the primary In turn, this requires suitable sensing by cognitive users to detect availability of primary channels (currently unused by the protected incumbents) for opportunistic usage To date, most studies have used largely hypothetical assumptions regarding channel idleness and resulting spectrum availability due to primary user dynamics For example, idleness of channels over any spectrum are typically assumed to be an independent and identically distributed Bernoulli sequence In contrast, nearly all real-time measurements suggest that channel idleness is frequency dependent, ie, the probability that a channel is idle depends on the channel location Cognitive radio research thus increasingly needs more realistic and validated models for channel idleness as the foundation of credible cross-layer analysis; this is the primary contribution of our work We use two sets of real-time measurements conducted in disparate geographic locations over four distinct time intervals to show that channel idleness is appropriately modeled as independent but non-identical (inid) Bernoulli variables characterized by p_i, the probability of idleness for the i-th channel We validate that Beta distribution can be used for modeling the variations in channel idleness probabilities; the Beta distribution parameters are estimated from the data to produce the best model fit Based on the validated inid model, we build a predictive model by computing the availability probability of k channels, ie, P{N_{idle} = k}, where N_{idle} denotes the number of idle channels over the spectrum of N channels However, the combinatorial complexity inherent in the computation of P{N_{idle} = k} suggests the need for efficient approximations We accomplish this by classifying idleness of channels based on the magnitude of p_i, and propose a novel Poisson-normal approximation for computing P{N_{idle} = k} For validation, the distribution obtained from our technique is compared with the exact distribution and normal approximation using the approximation error criterion

The security in cognitive radio networks: a survey

Abstract: Recent developments of wireless communication lead to the problem of growing spectrum shortage. Cognitive radio, as a novel technology, tends to solve this problem by dynamically utilizing the spectrum. 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 weaknesses are introduced by the nature of cognitive radio, and they may cause serious impact to the network quality of service. However, at present there are no specific secure protocols for cognitive radio networks. Motivated by this, the current state of art in security of cognitive radio network is reviewed in this paper. We focus on analyzing the security system at the macroscopic level, where both protection and detection are significant parts for ensuring security of the whole network. Special characteristics of cognitive radio network in different protocol layers are also investigated, such as physical layer, link layer, network layer, transport layer and application layer.