Collaborative Spectrum Sensing Based on Hidden Bivariate Markov Models

Sun, Yuandao; Mark, Brian L.; Ephraim, Yariv

doi:10.1109/glocomw.2015.7414019

Cited by 2 publications

(3 citation statements)

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“…If the linear combination weights and decision threshold are precomputed, the complexity of soft linear fusion becomes only O(Q). However, if these parameters are estimated online using HBMM parameter estimation, as proposed in [17], the complexity of the online soft linear fusion scheme per time slot becomes O(r 5 ). In practice, small values of r in the range 2 to 5 are sufficient to represent the PU active/idle sojourn time distributions accurately (see [2]).…”

Section: A Hbmm Soft Fusionmentioning

confidence: 99%

“…To apply the linear soft fusion scheme, the channel parameters are assumed to be known and the weights for linear combination and the decision threshold are computed offline according to [16]. As discussed in [17], the channel parameters can be estimated online by incorporating HBMM parameter estimation, which can then be used to compute the weights and threshold for linear soft fusion in an online manner. The performance of such a scheme was found to be nearly as good as that of linear soft fusion with precomputed weights and thresholds from known channel parameters.…”

Section: Soft Fusionmentioning

confidence: 99%

“…Thus, they are not able to adapt to changes in the channel conditions. In a preliminary version of our work [17], we showed how online HBMM parameter estimation could be applied to compute the weights and threshold for linear soft fusion in an online manner. However, the HBMM soft fusion scheme developed here obviates the need for linear soft fusion altogether.…”

Section: Introductionmentioning

confidence: 99%

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Collaborative Spectrum Sensing via Online Estimation of Hidden Bivariate Markov Models

Sun

Mark

Ephraim

2016

IEEE Trans. Wireless Commun.

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Collaborative spectrum sensing exploits multiuser diversity by combining spectrum sensing information from multiple secondary users to make joint decisions about spectrum occupancy. In hard fusion schemes, each secondary user makes a hard decision on spectrum occupancy and a fusion center makes a final decision by combining the individual hard decisions according to a fusion rule. In soft fusion schemes, each secondary user provides a signal power measurement to the fusion center, which performs further processing on the collection of all observations to make a final decision. In this paper, we propose hard and soft fusion collaborative spectrum sensing schemes based on online hidden bivariate Markov chain modeling of the signals received by secondary users. Compared to prior collaborative sensing schemes, the proposed model-based schemes do not rely on precomputed thresholds or weights, and achieve superior performance. Online estimation of hidden bivariate Markov models provides predictive information that can be used to improve the performance of dynamic spectrum access. Numerical results are presented to demonstrate the performance and communication overhead tradeoffs of the proposed collaborative spectrum sensing schemes.

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Section: A Hbmm Soft Fusionmentioning

confidence: 99%

Section: Soft Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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