Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks

Ma, Ji; Li, Ye

doi:10.1109/glocom.2007.594

Cited by 339 publications

(411 citation statements)

References 9 publications

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“…As mentioned in Section 1, an average consensus-based decentralized CSS scheme among all SUs has been proposed [16]. However, owing to the collected energies (i.e., x i (0)=e(i)) being directly exchanged among one-hop neighbors, that scheme can be regarded as a decentralized implementation of the EGC scheme [12]. Once consensus is reached, each SU locally obtains the final decision by comparing the consensus variable value x * with a predefined threshold :…”

Section: Phase 2: Consensus-based Decentralized Cssmentioning

confidence: 99%

“…In Figure 4, we compare the ROC curves of the proposed scheme, the OSC scheme [12], the EGC scheme [16], and the location-awareness clustering scheme. The figure shows that the proposed scheme significantly outperforms the EGC scheme and the location awareness scheme, and achieves detection performance comparable to that of the OSC scheme.…”

Section: Comparison Of the Receiver Operating Characteristic (Roc) Cumentioning

confidence: 99%

“…we then obtain a decentralized implementation of the optimal soft combination (OSC) scheme [12], which usually outperforms the EGC scheme. Note that the priori estimation of the local SNR…”

Section: Phase 2: Consensus-based Decentralized Cssmentioning

confidence: 99%

“…In terms of whether a fusion center exists, previous works can be mainly classified as those on centralized CSS and those on decentralized CSS. A number of centralized CSS techniques have been proposed, including methods of hard combination [8][9][10], soft combination [11,12] and quantized combination [13]. To further reduce sensing overheads (e.g., time and energy consumption) and improve the network scalability and robustness, there has been a recent focus on the design of decentralized CSS schemes.…”

Section: Citationmentioning

confidence: 99%

“…A fully decentralized CSS scheme has been proposed [16] on the basis of average consensus theory [17], where SUs iteratively exchange their sensing outcomes with one-hop neighbors before reaching an agreement (i.e., the average of their initial outcomes) on the presence of the PU. That scheme can be regarded as a decentralized implementation of the wellknown equal gain combination (EGC) scheme [12].…”

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confidence: 99%

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Consensus-based decentralized clustering for cooperative spectrum sensing in cognitive radio networks

Ding

Wang

et al. 2012

Chin. Sci. Bull.

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A large number of previous works have demonstrated that cooperative spectrum sensing (CSS) among multiple users can greatly improve detection performance. However, when the number of secondary users (SUs; i.e., spectrum sensors) is large, the sensing overheads (e.g., time and energy consumption) will likely be intolerable if all SUs participate in CSS. In this paper, we proposed a fully decentralized CSS scheme based on recent advances in consensus theory and unsupervised learning technology. Relying only on iteratively information exchanges among one-hop neighbors, the SUs with potentially best detection performance form a cluster in an ad hoc manner. These SUs take charge of CSS according to an average consensus protocol and other SUs outside the cluster simply overhear the sensing outcomes. For comparison, we also provide a decentralized implementation of the existing centralized optimal soft combination (OSC) scheme. Numerical results show that the proposed scheme has detection performance comparable to that of the OSC scheme and outperforms the equal gain combination scheme and location-awareness scheme. Meanwhile, compared with the OSC scheme, the proposed scheme significantly reduces the sensing overheads and does not require a priori knowledge of the local received signal-to-noise ratio at each SU.cognitive radio networks, spectrum sensing, decentralized clustering, unsupervised learning, consensus theory Citation:Wu Q H, Ding G R, Wang J L, et al. Consensus-based decentralized clustering for cooperative spectrum sensing in cognitive radio networks. Chin Sci Bull, 2012Bull, , 57: 36773683, doi: 10.1007 In cognitive radio networks (CRNs), secondary users (SUs) opportunistically exploit spectrum holes [1] left by primary users (PUs) to improve utilization of the wireless spectrum. To determine the temporal or/and spatial spectrum holes, a main challenge is reliable and efficient spectrum sensing. Compared with the single-SU spectrum sensing methods [2], cooperative spectrum sensing (CSS) among multiple SUs can be employed to tackle the problem of hidden primary receivers [3] and wireless channel fading (e.g., shadowing and multi-path fading) [4] by exploiting multi-user spatial diversity [5]. There have been recent comprehensive surveys on CSS in the literature [6,7]. In terms of whether a fusion center exists, previous works can be mainly classified as those on centralized CSS and those on decentralized CSS. A number of centralized CSS techniques have been proposed, including methods of hard combination [8][9][10], soft combination [11,12] and quantized combination [13]. To further reduce sensing overheads (e.g., time and energy consumption) and improve the network scalability and robustness, there has been a recent focus on the design of decentralized CSS schemes. Decentralized CSS has been modeled as an evolutionary game in which each SU learns its best strategy (to collaborate or not) from strategic interactions with other SUs [14]. The network [14] is a single-hop network; i.e., each SU directly exc...

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Section: Phase 2: Consensus-based Decentralized Cssmentioning

confidence: 99%

Section: Comparison Of the Receiver Operating Characteristic (Roc) Cumentioning

confidence: 99%

“…we then obtain a decentralized implementation of the optimal soft combination (OSC) scheme [12], which usually outperforms the EGC scheme. Note that the priori estimation of the local SNR…”

Section: Phase 2: Consensus-based Decentralized Cssmentioning

confidence: 99%

Section: Citationmentioning

confidence: 99%

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Consensus-based decentralized clustering for cooperative spectrum sensing in cognitive radio networks

Ding

Wang

et al. 2012

Chin. Sci. Bull.

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Weighted Average Consensus for Cooperative Spectrum Sensing

2017

Distributed Cooperative Control

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A soft cooperative spectrum sensing in the presence of most destructive smart PUEA using energy detector

Emami

Zarrabi

Jabbarpour

et al. 2018

Concurrency and Computation

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Summary Recently, the growth of Internet of Things (IoT) and its remarkable impacts on human well‐being life style are deniable. On the connectivity side, IoT is highly related to Wireless Sensor Network (WSN) concept. The key elements include the data, which is machine‐produced, specifically by sensors, and the data communication through connectivity technologies. On the security side, primary user emulation attack (PUE) is one of the well‐defined attacks in cognitive radio (CR)–based WSN. Here, we investigate a smart primary user emulation attacker that has the most destructive effect on the spectrum sensing unit of cognitive radio users. To deal with this attack, a soft cooperative spectrum sensing using an energy detector is proposed. In the proposed method, the values of sensing information of each secondary user are sent to a fusion center. Once the values are received, they will be combined with some appropriate coefficients in order to minimize spectrum sensing probability of error for a given probability of false alarm. The coefficients are the variables of a constrained optimization problem. Based on simulation results, our method has a lower error probability in spectrum sensing in comparison to hard combination schemes (eg, OR rule) and soft combination schemes (eg, CSINR method).

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Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks

Cited by 339 publications

References 9 publications

Consensus-based decentralized clustering for cooperative spectrum sensing in cognitive radio networks

Consensus-based decentralized clustering for cooperative spectrum sensing in cognitive radio networks

Weighted Average Consensus for Cooperative Spectrum Sensing

A soft cooperative spectrum sensing in the presence of most destructive smart PUEA using energy detector

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