Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks

Demestichas, Panagiotis; Katidiotis, A.; Tsagkaris, Kostas; Adamopoulou, Evgenia; Demestichas, Konstantinos

doi:10.1007/s11277-008-9559-1

Cited by 29 publications

(15 citation statements)

References 23 publications

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“…Therefore, the monitoring of versatile channel characteristics and computing the estimated residual channel capacity in the CRNs become challenging tasks because of the heterogeneous available channels, temporally and spatially varying PU activities, and limited resource availability. Although the work presented in [150] enhances the channel estimation, the proposed work still does not consider the interference caused by multi-hop communicating nodes. The real-time measurements must be combined with analytical calculations by considering the major factors that may affect the residual channel capacity including the link data rate, packet sizes, hidden nodes and channel errors.…”

Section: B Channel Capacity Estimationmentioning

confidence: 99%

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

Ahmed

Gani

Abolfazli

et al. 2016

IEEE Commun. Surv. Tutorials

154

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Abstract-The cognitive radio is an emerging technology that enables dynamic spectrum access in wireless networks. The cognitive radio is capable of opportunistically using the available portions of a licensed spectrum to improve the application performance for unlicensed users. The opportunistic use of the available channels in the wireless environment requires dynamic channel assignment to efficiently utilize the available resources while minimizing the interference in the network. A challenging aspect of such algorithms is the incorporation of the channels' diverse characteristics, highly dynamic network conditions with respect to primary users' activity, and different fragmented sizes of the available channels. This paper presents a comprehensive survey on the state-of-the-art channel assignment algorithms in cognitive radio networks. We also classify the algorithms by presenting a thematic taxonomy of the current channel assignment algorithms in cognitive radio networks. Moreover, the critical aspects of the current channel assignment algorithms in cognitive radio networks are analyzed to determine the strengths and weaknesses of such algorithms. The similarities and differences of the algorithms based on the important parameters, such as routing dependencies, channel models, assignment methods, execution model, and optimization objectives, are also investigated. We also discuss open research issues and challenges of channel assignment in the cognitive radio networks.

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Section: B Channel Capacity Estimationmentioning

confidence: 99%

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

Ahmed

Gani

Abolfazli

et al. 2016

IEEE Commun. Surv. Tutorials

154

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“…In addition to the above applications, we introduced a new application of Bayesian networks for modeling user preferences for radio access selection [12,13] which was later extended [14,15]. Bayesian networks have been applied in CRNs in localization [16], channel estimation [17], spectrum sensing [18] and channel selection [19] to name a few.…”

Section: Bayesian Networkmentioning

confidence: 99%

A Bayesian Approach to Service Selection for Secondary Users in Cognitive Radio Networks

Homayounvala¹

2015

ijacsa

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Abstract-In cognitive radio networks where secondary users (SUs) use the time-frequency gaps of primary users' (PUs) licensed spectrum opportunistically, the experienced throughput of SUs depend not only on the traffic load of the PUs but also on the PUs' service type. Each service has its own pattern of channel usage, and if the SUs know the dominant pattern of primary channel usage, then they can make a better decision on choosing which service is better to be used at a specific time to get the best advantage of the primary channel, in terms of higher achievable throughput. However, it is difficult to inform directly SUs of PUs' dominant used services in each area, for practical reasons. This paper proposes a learning mechanism embedded in SUs to sense the primary channel for a specific length of time. This algorithm recommends the SUs upon sensing a free primary channel, to choose the best service in order to get the best performance, in terms of maximum achieved throughput and the minimum experienced delay. The proposed learning mechanism is based on a Bayesian approach that can predict the performance of a requested service for a given SU. Simulation results show that this service selection method outperforms the blind opportunistic SU service selection, significantly.

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“…Among the articles that report so are [14][15][16], respectively. The specific approaches are selected to be further analysed in the next sections.…”

Section: Related Studymentioning

confidence: 99%

“…Looking from the user preferences side, effort was put on developing context awareness techniques [17][18][19][20], recording of user preferences [21,22] and learning capabilities [11,14] and exploiting these to influence the configuration selection [23][24][25]. Additionally, relevant work also includes the use of Bayesian networks in support of user modelling, as a method for evaluating, in a qualitative and quantitative manner, elements of the user behaviour and consequently updating the user profile.…”

Section: Related Studymentioning

confidence: 99%

An overview of learning mechanisms for cognitive systems

Bantouna

Stavroulaki

Kritikou

et al. 2012

J Wireless Com Network

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Cognitive systems were first introduced by Mitola and in the last decade they have proved to be beneficial in selfmanagement 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.

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Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks

Cited by 29 publications

References 23 publications

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

A Bayesian Approach to Service Selection for Secondary Users in Cognitive Radio Networks

An overview of learning mechanisms for cognitive systems

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