Novel Policy Reasoning Architecture for Cognitive Radio Environments

Denkovski, Daniel; Pavlovska, Valentina; Atanasovski, Vladimir; Gavrilovska, Liljana

doi:10.1109/glocom.2010.5683283

Cited by 16 publications

(12 citation statements)

References 5 publications

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“…The elaborated policy architecture incorporates many functionalities [67]. This subsection briefly describes some of them that are crucial for the subsequent understanding and elaboration of the policy-based reasoning applications.…”

Section: ) Policy Architecture Functionalitiesmentioning

confidence: 99%

“…For more extensive details on the elaborated policy architecture and its functionalities, the reader is referred to [67].…”

Section: ) Policy Architecture Functionalitiesmentioning

confidence: 99%

“…Fig. 4 depicts a fully functional architectural instantiation of the policy based reasoning concept [67]. The architecture embraces policies, expressed in CoRaL [64], coming from various stakeholders (e.g.…”

Section: Some Specific Reasoning Realizationsmentioning

confidence: 99%

“…The policy architecture also includes the specification of a custom policy protocol [67], which defines the communication between the policy components via the defined policy and control channel interfaces. …”

Section: ) Architectural Components and Interfacesmentioning

confidence: 99%

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Learning and Reasoning in Cognitive Radio Networks

Gavrilovska

Atanasovski

Macaluso

et al. 2013

IEEE Commun. Surv. Tutorials

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Abstract-Cognitive radio networks challenge the traditional wireless networking paradigm by introducing concepts firmly stemmed into the Artificial Intelligence (AI) field, i.e., learning and reasoning. This fosters optimal resource usage and management allowing a plethora of potential applications such as secondary spectrum access, cognitive wireless backbones, cognitive machine-to-machine etc. The majority of overview works in the field of cognitive radio networks deal with the notions of observation and adaptations, which are not a distinguished cognitive radio networking aspect. Therefore, this paper provides insight into the mechanisms for obtaining and inferring knowledge that clearly set apart the cognitive radio networks from other wireless solutions.

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Section: ) Policy Architecture Functionalitiesmentioning

confidence: 99%

“…For more extensive details on the elaborated policy architecture and its functionalities, the reader is referred to [67].…”

Section: ) Policy Architecture Functionalitiesmentioning

confidence: 99%

Section: Some Specific Reasoning Realizationsmentioning

confidence: 99%

Section: ) Architectural Components and Interfacesmentioning

confidence: 99%

See 2 more Smart Citations

Learning and Reasoning in Cognitive Radio Networks

Gavrilovska

Atanasovski

Macaluso

et al. 2013

IEEE Commun. Surv. Tutorials

Self Cite

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“…To that end, policy based management is discussed in [6], [7] and especially for cognitive networks in [8]. The terminology as well as exact roles and implementations of policy engine varies, but generally there is a need for "reasoning architecture" which sets the goals for the reasoning intelligence.…”

Section: Related Workmentioning

confidence: 99%

Network management issues in military Cognitive Radio Networks

Bräysy

Tuukkanen

Couturier

et al. 2017

2017 International Conference on Military Communications and Information Systems (ICMCIS)

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Abstract-Cognitive Radio (CR) was designed to support flexible spectrum usage by adding spectrum sensing facilities and decision making logic to the radio devices. Cognitive Radio Network (CRN) is an extension of the CR concept to enable holistic end-to-end optimization of the network operation and services. We discuss CRN management issues in the context of military and tactical operation environments, where the key feature is the temporal nature of the network installations. Our special interest is in the wireless ad hoc network solutions. The network lifetime may extend from just hours to several days. The limited lifetime of the networks makes it both possible and necessary to define the management functionalities with respect to different mission phases. Traditional FCAPS (Fault, Configuration, Administration, Performance, and Security) functions and their required actions are therefore detailed to some level at each operational phase (before, during and after mission). We will emerge the idea that, as the cognitive and autonomous technologies will be developed to operate communication networks and become trustworthy enough to be applied also in tactical context, they will most likely first be applied in the during mission phase. Of course, this phase is also the most critical in the sense that it is here that lives are at stake. To answer this critical issue, the policy management must be seen as an equally critical functionality. It is necessary to develop the interconnection between mission goals and defined policies so that the Cognitive Network Engine (CNE) determining the operational parameters of the network, in all situations provides a reliable and failsafe communication solution to be utilized. 1

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