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Cognitive network

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


Papers
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Proceedings ArticleDOI
18 May 2009
TL;DR: A novel technique is presented that takes advantage of cooperative diversity to establish clustering among cooperating secondary users to establish a tasking mechanism and ensures enhanced agility that will result in a reduction of interference to primary users through their early detection.
Abstract: Cognitive networks can potentially solve the problems of spectrum scarcity by accommodating unlicensed (secondary) users in under-utilized segments of the spectrum. Spectrum sensing serves as the primary stimulus for a cognitive radio and is vitally important for ensuring that the unlicensed users do not offer intolerable levels of interference to licensed (primary) users. Cooperative spectrum sensing provides the capability to cognitive networks to overcome problems related to “hidden” primary users. In this paper, we present a novel technique that takes advantage of cooperative diversity to establish clustering among cooperating secondary users. This technique is then extended to group-based spectrum sensing to establish a tasking mechanism. The technique ensures enhanced agility that will result in a reduction of interference to primary users through their early detection.

14 citations

Proceedings ArticleDOI
05 Apr 2009
TL;DR: It is shown through simulations that the proposed MAC layer enhancement outperforms well-known multi-channel MAC protocols both in terms of aggregate end- to-end throughput and average frame end-to-end delay.
Abstract: In this paper, we investigate stochastic multi-channel load balancing in a distributed cognitive network coexisting with primary users. In particular, we propose a probabilistic technique for traffic distribution among a set of data channels by incorporating statistical information of primary users' activities in different channels into the selection process without centralized control. Moreover, the proposed scheme is enabled by a multi-channel binary exponential backoff mechanism to further facilitate contention resolution in a multi-channel environment. It is shown through simulations that the proposed MAC layer enhancement outperforms well-known multi-channel MAC protocols both in terms of aggregate end-to-end throughput and average frame end-to-end delay. Furthermore, its performance is also compared to two heuristic channel selection techniques in a multi-channel cognitive network, coexisting with incumbents.

14 citations

Journal ArticleDOI
TL;DR: An extended state-of-the-art of machine learning applied to cognitive systems as coming from the recent research and an overview of three different learning capabilities of both the network and the user device are presented.
Abstract: Cognitive systems were first introduced by Mitola and in the last decade they have proved to be beneficial in self-management functionalities of future generation networks. The advantages and the way that networks gain benefits from cognitive systems is analysed in this article. Moreover, since such systems are closely related to machine learning, the focus of this article is also placed on machine learning techniques applied both in the network and the user devices side. In particular, celebrating 10 years of cognitive systems, this survey-oriented article presents an extended state-of-the-art of machine learning applied to cognitive systems as coming from the recent research and an overview of three different learning capabilities of both the network and the user device.

14 citations

Journal ArticleDOI
TL;DR: A new Artificial Neural Network (ANN) model is proposed for detection of a spectrum hole and the channel capacity status could be identified in a quantized index form.
Abstract: The recent phenomena of tremendous growth in wireless communication application urge increasing need of radio spectrum, albeit it being a precious but natural resource. The recent technology under development to overview the situation is the concept of Cognitive Radio (CR). Recently the Artificial Intelligence (AI) tools are being considered for the topic. AI is the core of the cognitive engine that examines the external and internal environment parameters that leads to some postulations for QoS improvement. In this article, we propose a new Artificial Neural Network (ANN) model for detection of a spectrum hole. The model is trained with some pertinent features over a channel like SNR, channel capacity, bandwidth efficiency etc. The channel capacity status could be identified in a quantized index form . Some simulation results are presented.

14 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202317
202234
202175
2020104
2019121
2018134