Dynamic frequency broker and cognitive radio

Maseng, Torleiv; Ulversøy, Tore

doi:10.1049/ic:20080390

Cited by 7 publications

(3 citation statements)

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“…• New forms of businesses to facilitate spectrum trade and intermediation promise to dominate in the future. A potential example is the dynamic frequency broker (DFB) system proposed in [99]. DFB acts as a local computerized frequency coordination authority and keeps a complete database of frequency assignments within an area as well as an updated terrain propagation path loss model of its area.…”

Section: Future Developments In Crnsmentioning

confidence: 99%

“…• The need for accurate PU characterisation calls for knowledge based on-line databases. Unlike proposed spectrum brokerage business systems, such as in [99], PU activity databases may be controlled by primary network operators, and may only be accessible by CRN operators who are willing to operate as secondary networks. • Although CR promises efficient utilization of the RF spectrum, it is challenged with network switching delay.…”

Section: Future Developments In Crnsmentioning

confidence: 99%

See 1 more Smart Citation

Spectrum Decision in Cognitive Radio Networks: A Survey

Masonta¹,

Mzyece²,

Ntlatlapa

2013

IEEE Commun. Surv. Tutorials

286

149

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Spectrum decision is the ability of a cognitive radio (CR) to select the best available spectrum band to satisfy secondary users' (SUs') quality of service (QoS) requirements, without causing harmful interference to licensed or primary users (PUs). Each CR performs spectrum sensing to identify the available spectrum bands and the spectrum decision process selects from these available bands for opportunistic use. Spectrum decision constitutes an important topic which has not been adequately explored in CR research. Spectrum decision involves spectrum characterization, spectrum selection and CR reconfiguration functions. After the available spectrum has been identified, the first step is to characterize it based not only on the current radio environment conditions, but also on the PU activities. The second step involves spectrum selection, whereby the most appropriate spectrum band is selected to satisfy SUs' QoS requirements. Finally, the CR should be able to reconfigure its transmission parameters to allow communication on the selected band. Key to spectrum characterization is PU activity modelling, which is commonly based on historical data to provide the means for predicting future traffic patterns in a given spectrum band. This paper provides an up-to-date survey of spectrum decision in CR networks (CRNs) and addresses issues of spectrum characterization (including PU activity modelling), spectrum selection and CR reconfiguration. For each of these issues, we highlight key open research challenges. We also review practical implementations of spectrum decision in several CR platforms.

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Section: Future Developments In Crnsmentioning

confidence: 99%

Section: Future Developments In Crnsmentioning

confidence: 99%

Spectrum Decision in Cognitive Radio Networks: A Survey

Masonta¹,

Mzyece²,

Ntlatlapa

2013

IEEE Commun. Surv. Tutorials

286

149

View full text Add to dashboard Cite

show abstract

“…To coordinate a large number of CRs, some research papers have integrated brokers into the telecommunication business model [2], [3]. Such dynamic frequency brokers (DFBs) are responsible for allocating frequency bands to radio nodes within their geographic area.…”

Section: Introductionmentioning

confidence: 99%

Wireless network virtualization

Wang

Krishnamurthy

Tipper

2013

2013 International Conference on Computing, Networking and Communications (ICNC)

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Abstract-The virtualization of wired networks and end computing systems has become one of the leading trends in networked information and communication technology (ICT) systems. In contrast relatively little virtualization has occurred in infrastructure based wireless networks, but the idea of virtualizing wireless access is gaining attention as it has the potential to improve spectrum utilization and perhaps create new services. In this paper we survey the state of the current research in virtualizing wireless networks. We define and describe possible architectures, the issues, hurdles and trends towards implementation of wireless network virtualization.

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