Online Parameter Estimation for Temporal Spectrum Sensing

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

doi:10.1109/twc.2015.2416720

Cited by 15 publications

(18 citation statements)

References 30 publications

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“…In [10], HBMM was adopted to model the temporal spectrum sensing for a single SU. Here, Y is used to represent the received signal power at an SU and X represents the state of the PU.…”

Section: A Definition and Parametrizationmentioning

confidence: 99%

“…The online HBMM parameter estimation algorithm developed in [10] is based on an algorithm for the HMM developed by Rydén [9], and a recursion for computing the score function for an HMM from Lystig and Hughes [22] (see also Willy et al [23]). The parameter estimation algorithm operates on a block of m observations at a time.…”

Section: B Online Parameter Estimationmentioning

confidence: 99%

“…The higher degrees of freedom afforded by the HBMM can provide higher modeling fidelity than the HMM, which results in better performance. Recently, a recursive parameter estimation algorithm for the HBMM, based on Rydén's [9] recursive algorithm for the HMM, was developed in [10]. Together with the state estimation and prediction recursions presented in [2], the recursive HBMM parameter estimation algorithm forms the basis for a fully online temporal spectrum sensing scheme.…”

Section: Introductionmentioning

confidence: 99%

“…Our approach relies on the online parameter estimation algorithm for the single channel temporal spectrum sensing approach presented in [10]. As in [2], [10], the detector frontend for each SU is assumed to provide averaged power estimates obtained from received signal power measurements, and the channel follows a path loss model with lognormal shadowing. In our proposed soft fusion scheme, the averaged power estimates from the SUs are quantized and transmitted to the fusion center.…”

Section: Introductionmentioning

confidence: 99%

“…Using the vector of quantized power estimates from the SUs, an estimate of the parameter of a hidden bivariate Markov model is computed recursively and used to estimate the state of the PU. In our HBMM hard fusion scheme, each SU computes a hard decision on the active/idle state of the PU based on the observed signal power measurements and an estimate of the associated HBMM parameter as in [10]. The hard decisions from the SUs are transmitted to the fusion center, which then applies linear combining to obtain the final decision.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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“…In [10], HBMM was adopted to model the temporal spectrum sensing for a single SU. Here, Y is used to represent the received signal power at an SU and X represents the state of the PU.…”

Section: A Definition and Parametrizationmentioning

confidence: 99%