Spectrum Sensing Using a Hidden Bivariate Markov Model

Nguyen, Thao T.; Mark, Brian L.; Ephraim, Yariv

doi:10.1109/twc.2013.072513.121864

Cited by 80 publications

(81 citation statements)

References 24 publications

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“…For example, spectrum utilization rates, which can be represented by the DC and the transition rate in the Markov chain, are utilized in spectrum sensing, and it has been shown that this information can enhance the spectrum sensing performance [30], [31]. As mentioned in [19], [30], [31], some prior information is useful for efficient spectrum sharing, such as both the spectrum sensing and channel access [32], [33]. However, it is still not clear what type of prior information or statistical information is very useful for managing the smart spectrum and this issue has not been investigated in-depth.…”

Section: Spectrum Measurement For Smart Spectrum and Issuesmentioning

confidence: 99%

“…However, it is still not clear what type of prior information or statistical information is very useful for managing the smart spectrum and this issue has not been investigated in-depth. Although the spectrum utilization rates, such as the DC, may be useful for spectrum sensing, as confirmed in [30], [31], a situation exists where the spectrum utilization rates will provide no benefits. Specifically, in the case described in [30], if the rate is time-invariant, there is no gain in the spectrum sensing.…”

Section: Spectrum Measurement For Smart Spectrum and Issuesmentioning

confidence: 99%

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Smart Spectrum for Future Wireless World

Fujii

Umebayashi

2017

IEICE Trans. Commun.

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SUMMARY As the role of wireless communication is becoming more important for realizing a future connected society for not only humans but also things, spectrum scarcity is becoming severe, because of the huge numbers of mobile terminals and many types of applications in use. In order to realize sustainable wireless connection under limited spectrum resources in a future wireless world, a new dynamic spectrum management scheme should be developed that considers the surrounding radio environment and user preferences. In this paper, we discuss a new spectrum utilization framework for a future wireless world called the "smart spectrum." There are four main issues related to realizing the smart spectrum. First, in order to recognize the spectrum environment accurately, spectrum measurement is an important technology. Second, spectrum modeling for estimating the spectrum usage and the spectrum environment by using measurement results is required for designing wireless parameters for dynamic spectrum use in a shared spectrum environment. Third, in order to effectively gather the measurement results and provide the spectrum information to users, a measurement-based spectrum database can be used. Finally, smart spectrum management that operates in combination with a spectrum database is required for realizing efficient and organized dynamic spectrum utilization. In this paper, we discuss the concept of the smart spectrum, fundamental research studies of the smart spectrum, and the direction of development of the smart spectrum for targeting the future wireless world. key words: smart spectrum, spectrum measurement, spectrum usage modeling, spectrum database, spectrum management

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Section: Spectrum Measurement For Smart Spectrum and Issuesmentioning

confidence: 99%

Section: Spectrum Measurement For Smart Spectrum and Issuesmentioning

confidence: 99%

Smart Spectrum for Future Wireless World

Fujii

Umebayashi

2017

IEICE Trans. Commun.

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“…The main advantage of HBMM [10] [14] is that the dwell time in a given state has a discrete phasetype distribution which is more suitable for modeling a cognitive radio channel than the geometric dwell time distribution of HMM. In HBMM, each channel state contains r sub-states denoted by {S n }.…”

Section: Hidden Bivariate Markov Modelmentioning

confidence: 99%

“…13, the miss detection rate is as low as 0.65 when the number of SUs is less than 3. With the increase of the SU number, the CBS can obtain more 10 EAI Endorsed Transactions on Wireless Spectrum 01 2017 -12 2017 | Volume 3 | Issue 10 | e4 sensing data and hence to decrease prediction tendency of status busy. Similarly, the slight effect of the increase of SU numbers on H 2 BMM has been explained in Fig.…”

Section: H 2 Bmm In Stationary Crnsmentioning

confidence: 99%

“…In addition, studies discover that the geometric distribution characteristic of HMM is not suitable for describing channel behaviors, especially when bursty transmissions occur [11]. To better model the cognitive radio channel, hidden bivariate Markov model (HBMM) Z. Hong, Y. Zhao, Y. Luo, G. Wang, L. Pu is proposed and applied to spectrum prediction in [10]. The main idea of HBMM is to further divide a channel state into multiple sub-states, forming an underlying process, so that the channel sub-state dwell time follows a Phase distribution.…”

Section: Introductionmentioning

confidence: 99%

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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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Optimal sequential decision rule for cooperative spectrum sensing of CUAVNs

Bao

2022

Trans Emerging Tel Tech

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Cognitive radio (CR) technology is beneficial for unmanned aerial vehicles (UAVs) to access the spectrum resources needed for communication. Then, in order to integrate CR technology into UAVs without causing excessive interference to the primary users (PUs), cooperative spectrum sensing (CSS) is used as a key function of CR technology to detect the PUs for the UAV's available spectrum. However, the flexible locations of UAVs may significantly degrade the performance and efficiency of CSS within them. Hence, on the basis of a cognitive UAV networks (CUAVN) framework, we propose an internal cooperation paradigm to achieve the cooperative gain of CSS within an UAV. Furthermore, we convert the decision rule as an optimal stopping problem to optimize the decision rule for CSS. The optimization problem is carried out by the error probability regarding the PU presence of the phenomenon and the stopping time required to reach a global decision about the PU status. By optimizing the error probability respectively under the condition of a fixed sensing time, the optimization problem is transformed into an optimal stopping problem and solved by Markov stopping theory. Following this, we further determine a pair of thresholds in the optimal sequential decision rule from the minimal cost function. At last, numerical simulation results verify the correctness and effectiveness of the optimal sequential detection rule, and corroborate that in contrast to the external cooperative paradigm, the superiority of the internal cooperative paradigm under various network environments and parameters, in terms of the CSS performance and the stopping time.

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Spectrum Sensing Using a Hidden Bivariate Markov Model

Cited by 80 publications

References 24 publications

Smart Spectrum for Future Wireless World

Smart Spectrum for Future Wireless World

Advanced High-order Hidden Bivariate Markov Model Based Spectrum Prediction

Optimal sequential decision rule for cooperative spectrum sensing of CUAVNs

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