Spectrum Monitoring During Reception in Dynamic Spectrum Access Cognitive Radio Networks

Boyd, Steven W.; Frye, Joan M.; Pursley, M.B.; Royster, Thomas C.

doi:10.1109/tcomm.2011.122111.100603

Cited by 57 publications

(30 citation statements)

References 24 publications

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“…By contrast, spectrum monitoring in CRNs is a relatively new technique, where the cognitive radios can continue their communications while simultaneously monitoring the band to detect any transmissions that are initiated by primary radios. More specifically, the spectrum is monitored by the cognitive radio receiver during reception and without quiet periods [290]- [292]. So far, spectrum sensing has not been widely accepted by regulatory bodies to ensure nonharming the primary/licensed users.…”

Section: B Relationship With Spectrum Sensingmentioning

confidence: 99%

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Ding

Jiao

Wang

et al. 2018

IEEE Commun. Surv. Tutorials

154

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Abstract-Spectrum inference, also known as spectrum prediction in the literature, is a promising technique of inferring the occupied/free state of radio spectrum from already known/measured spectrum occupancy statistics by effectively exploiting the inherent correlations among them. In the past few years, spectrum inference has gained increasing attention owing to its wide applications in cognitive radio networks (CRNs), ranging from adaptive spectrum sensing, and predictive spectrum mobility, to dynamic spectrum access and smart topology control, to name just a few. In this paper, we provide a comprehensive survey and tutorial on the recent advances in spectrum inference. Specifically, we first present the preliminaries of spectrum inference, including the sources of spectrum occupancy statistics, the models of spectrum usage, and characterize the predictability of spectrum state evolution. By introducing the taxonomy of spectrum inference from a time-frequency-space perspective, we offer an in-depth tutorial on the existing algorithms. Furthermore, we provide a comparative analysis of various spectrum inference algorithms and discuss the metrics of evaluating the efficiency of spectrum inference. We also portray the various potential applications of spectrum inference in CRNs and beyond, with an outlook to the 5th generation mobile communications (5G) and next generation high frequency (HF) communications systems. Last but not least, we highlight the critical research challenges and open issues ahead.

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Section: B Relationship With Spectrum Sensingmentioning

confidence: 99%

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Ding

Jiao

Wang

et al. 2018

IEEE Commun. Surv. Tutorials

154

View full text Add to dashboard Cite

show abstract

“…In receiver statistic approach the spectrum is monitored by the CR receiver during reception and without any quiet periods [2].Here for each received packet, compare the bit error count, which is produced by a strong channel code like a Low Density Parity Check (LDPC) code to a threshold value. The monitoring algorithm indicates that the primary user is active when the number of detected errors is above certain value.…”

Section: Spectrum Monitoring Approachesmentioning

confidence: 99%

“…Mathematically let Zi be the ith sample of the reserved tone sequence. Now define the decision making variable, Xk, as the ratio of the energy of the second window, Uk, and the energy of the first window, Vk as given by (2).Where N is the number of samples per window and k is an integer such that k = 1,2,3, . .…”

Section: Benergy Ratio Algorithmmentioning

confidence: 99%

Survey on Spectrum monitoring for OFDM based Cognitive Radio Networks

Lal¹,

N²

2016

IOSR

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“…First, handoff latency can be very short because everything can be planned in advance. Second, the possibility of multiple spectrum handoffs can be minimized by considering future target channel usage when selecting backup target channel [6]. However, the drawback of this strategy is that backup target channel can remain obsolete.…”

Section: Non-handojjstrategymentioning

confidence: 99%

User aware Mobility Management in Cognitive radio cellular network

Sumathi¹,

M²,

Suganthi³

2014

2014 International Conference on Electronics and Communication Systems (ICECS)

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Spectrum aware Mobility Management framework consists of Spectrum mobility management, User mobility management and Intercell resource allocation. Spectrum Mobility Management is used to determine the target cell and spectrum band for CR users. It adaptively dependent on time varying spectrum opportunities, leading to increase cell capacity. In the User Mobility Management scheme, a mobile user selects proper handoff mechanism to minimize the switching latency. Intercell resource allocation helps to improve the performance of QOS parameters. There exist three types of Handoff schemes namely Classical, Reactive and Proactive. All the spectrum handoff schemes have its own different shortcomings. So it is essential to find a better handoff scheme for different environments. The performance parameters have been identified such as Handoff latency, Packet delivery ratio, Throughput and Mean packet delay of Secondary user. The Cognitive radio networks are modeled and simulated using NS-2 under LINUX platform. An analysis has been done over the performance of the system using the three handoff schemes under different scenarios. The most appropriable handoff strategy for different environments and applications to meet the QOS requirements has also been suggested.

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Spectrum Monitoring During Reception in Dynamic Spectrum Access Cognitive Radio Networks

Cited by 57 publications

References 24 publications

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Survey on Spectrum monitoring for OFDM based Cognitive Radio Networks

User aware Mobility Management in Cognitive radio cellular network

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