Spectrum prediction in cognitive radio networks

Xing, Xiaoshuang; Jing, Tao; Cheng, Wei; Huo, Yan; Cheng, Xiuzhen

doi:10.1109/mwc.2013.6507399

Cited by 248 publications

(105 citation statements)

References 9 publications

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“…To overcome the time delay introduced while performing this complete cycle, solutions such as spectrum prediction for spectrum sensing was proposed [37]. Coexistence issues may arise between different services sharing adjacent portion of the spectrum, such as Digital Terrestrial Television (DTE) and cellular networks operating in the TVWS, as highlighted in [38].…”

Section: Coexistence Challenges For Cr Network In Tvwsmentioning

confidence: 99%

A Survey on Coexistence in Heterogeneous Wireless Networks in TV White Spaces

Maloku

Fazliu

Ibrani

2018

Wireless Communications and Mobile Computing

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With the advent of analog to digital transition in TV broadcasting, a substantial amount of spectrum has become available in TV bands. To take advantage of this, the idea for cognitive radios was introduced for two major reasons: better utilization of spectrum in urban areas and facilitation of wireless connectivity in rural areas. To achieve these two goals, however, many challenges have to be addressed first. Considering that these frequencies are commonly licensed, besides primary user detection, a serious challenge remains the detection and identification of other secondary devices and networks. The problems arising from this issue concern the coexistence problems happening from having several primary and secondary networks of different technologies cohabiting the same licensed spectrum simultaneously and from many secondary systems/users coexisting at the same place while using identical or different technologies. In this survey we provide a review of existing works and outline new challenges regarding coexistence and self-coexistence in heterogeneous wireless networks in TV White Spaces including a comparative analysis between selected coexistence mechanisms.

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Section: Coexistence Challenges For Cr Network In Tvwsmentioning

confidence: 99%

A Survey on Coexistence in Heterogeneous Wireless Networks in TV White Spaces

Maloku

Fazliu

Ibrani

2018

Wireless Communications and Mobile Computing

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“…There is a mathematical expression to detect the PU signal by using following hypothesis for received signal [1], x(n) shows signal received by each CR user. s(n) is the PU licensed signal, w(n) shows AWGN (additive white gaussian noise) with zero mean i.e.…”

Section: System Descriptionmentioning

confidence: 99%

“…It measures the energy of PU signal and compares it to a threshold (γ) in order to determine whether the PU signal is present or absent. To determine signal energy there is a mathematical formulation given as [1] Where, x(n) shows received PU signal, N shows number of samples, and X shows the energy of x(n). The value of threshold is set to meet the desired target P f as per the power of noise signal.…”

Section: Energy Detectormentioning

confidence: 99%

See 1 more Smart Citation

Novel Spectrum detector for IEEE 802.22 Wireless Regional Area Network

Bagwari¹,

Kanti²,

Tomar³

2015

IJSDA

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“…Authors in [7] summarize several state-of-the-art spectrum prediction techniques and illustrate their applications. In [8] [9] [10], Markov related prediction algorithm has been improved from Hidden Markov Model (HMM) to highorder HMM and Hidden Bivariate Markov Model (HBMM).…”

Section: Introductionmentioning

confidence: 99%

Advanced High-order Hidden Bivariate Markov Model Based Spectrum Prediction

Zhao¹,

Zhiming²,

Luo³

et al. 2017

EAI Endorsed Transactions on Wireless Spectrum

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The majority of existing spectrum prediction models in Cognitive Radio Networks (CRNs) don't fully explore the hidden correlation among adjacent observations. In this paper, we first develop a novel prediction approach termed high-order hidden bivariate Markov model (H 2 BMM) for a stationary CRN. The proposed H 2 BMM leverages the advantages of both HBMM and high-order, which applies two dimensional parameters, i.e., hidden process and underlying process, to more accurately describe the channel behavior. In addition, the current channel state is predicted by observing multiple previous states. Afterwards, the mobility of secondary users is fully considered and we propose an advanced approach based on H 2 BMM, termed Advanced H 2 BMM, to accommodate a mobile CRN. Extensive simulations are conducted and results verify that the prediction accuracy is significantly improved using the proposed (H 2 BMM. The Advanced H 2 BMM is also evaluated with comparison to H 2 BMM and results show considerable improvements of H 2 BMM in a mobile environment.

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Spectrum prediction in cognitive radio networks

Cited by 248 publications

References 9 publications

A Survey on Coexistence in Heterogeneous Wireless Networks in TV White Spaces

A Survey on Coexistence in Heterogeneous Wireless Networks in TV White Spaces

Novel Spectrum detector for IEEE 802.22 Wireless Regional Area Network

Advanced High-order Hidden Bivariate Markov Model Based Spectrum Prediction

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