Cooperative Spectrum Sensing as Image Segmentation: A New Data Fusion Scheme

Wu, Keyu; Tang, Min; Tellambura, Chintha; Ma, Dongtang

doi:10.1109/mcom.2018.1700743

Cited by 14 publications

(9 citation statements)

References 14 publications

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“…The distributed cooperation architecture is known to have the potential to increase spectral estimation reliability and decrease the probability of interference of cognitive radios to existing radio systems. The Network architecture consists of two basic elements: the sensing group and the Sensing Coordinator (SC) [44][45][46]. The sensing group is divided into G sensing subgroups which each subgroup performed by n secondary users.…”

Section: A Distributed Spectrum Sensingmentioning

confidence: 99%

Cooperative Fusion Architecture-based Distributed Spectrum Sensing Under Rayleigh Fading Channel

Mokhtar

Saeed

Alhumyani

2022

Wireless Pers Commun

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Cognitive radio (CR) is one of the most promising technology soon due to the scarcity of the spectrum, especially at microwave band. CR faces massive resistance from the industry because of the potential interference caused by the secondary users. Spectrum sensing forms an important functionality for CR systems. However, such detection performance is usually compromised by shadowing and fading channel conditions. Cooperative sensing is one of the crucial solutions to overcome degraded detection performance. To improve the sensing performance and reduce the reporting error, a distributed architecture for processing and fusion of sensing information is proposed in this work. In dense network scenarios, the decision fusion for cooperated users could be complex and reported sensing traffic may require large bandwidth. This paper proposes a new distributed detection and adapted threshold based on controlled false alarm probability to improve sensing reliability and efficiency in a highly Rayleigh faded environment. A distributed detection is developed by selecting fusion nodes (FN) that are dynamically selected from a group of nodes. The detection threshold is calculated adaptively using the link quality indicator (LQI) of the sensing channel. Moreover, the proposed method can significantly minimize the typically transmitted bits in the reporting channel. The paper also discussed in detail the design parameter of the CR number on the performance of fusion values. The simulation analysis shows that the performance of the distributed cooperative sensing (DCS) process is considerably improved by the adapted threshold.The numerical results demonstrated that the error was remarkably minimized. The ROC curve of the sensing process is notably improved for detection probability and false alarm probability, respectively. Finally, it was shown that the requirement of sensitivity can be greatly improved up to 0.95.

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Section: A Distributed Spectrum Sensingmentioning

confidence: 99%

Cooperative Fusion Architecture-based Distributed Spectrum Sensing Under Rayleigh Fading Channel

Mokhtar

Saeed

Alhumyani

2022

Wireless Pers Commun

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“…In [7], Wu et al proposed a non-cooperative game-theory approach is proposed to realise an effective Byzantine defence. Inspired by image segmentation in the field of computer vision, Wu et al proposed a graph-cut (GC) CSS algorithm in [8], which provides robust spectrum sensing results despite Heterogeneous spectrum occupancy (HetSOc) and SSDF attacks in CR ad-hoc networks (CRAHNs). Clement in [9] proposed two-stage test at the combining centre, including Shapiro-Wilk's normality test and loglikelihood ratio test.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the blind condition (10) can be rewritten as P fa = 1 − P ma . According to (8) and 9, we get the following equation:…”

Section: Blind Conditionmentioning

confidence: 99%

Reuse of Byzantine data in cooperative spectrum sensing using sequential detection

Song

et al. 2020

IET Communications

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“…Figure 3 and Figure 4 show the influence of probabilistic attacks on the performance of three sensing algorithms with a hard decision and with a soft decision [4], respectively. It was demonstrated that CSS with a soft decision is vulnerable to malicious attacks and the performance of CSS with a soft decision is worse than that of CSS with a hard decision.…”

Section: Performance Analysismentioning

confidence: 99%

“…However, neighboring SUs are spatially close and prefer to experience identical spectral status. This characteristic of ad hoc networks is called spectrum partial homogeneity [4]. Cooperative spectrum sensing (CSS) takes full advantage of this prior information to improve the performance of malfunctioning SUs with poor wireless propagation characteristics, such as those nodes experiencing deep shading.…”

Section: Introductionmentioning

confidence: 99%

Reputation-Based Spectrum Sensing Strategy Selection in Cognitive Radio Ad Hoc Networks

Sun

Chen

et al. 2018

Sensors

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Spectrum sensing plays an essential role in the detection of unused spectrum whole in cognitive radio networks, including cooperative spectrum sensing (CSS) and independent spectrum sensing. In cognitive radio ad hoc networks (CRAHNs), CSS enhances the sensing performance of cognitive nodes by exploring the spectrum partial homogeneity and fully utilizing the knowledge of neighboring nodes, e.g., sensing results and topological information. However, CSS may also open a door for malicious nodes, i.e., spectrum sensing data falsification (SSDF) attackers, which report fake sensing results to deteriorate the performance of CSS. Generally, the performance of CSS has an inverse relationship with the fraction of SSDF attackers. On the contrary, independent spectrum sensing is robust to SSDF attacks. Therefore, it is desirable to choose a proper sensing strategy between independent sensing and collaborative sensing for CRAHNs coexisting with various fractions of SSDF attackers. In this paper, a novel algorithm called Spectrum Sensing Strategy Selection (4S) is proposed to select better sensing strategies either in a collaborative or in an independent manner. To derive the maximum a posteriori estimation of nodes’ spectrum status, we investigated the graph cut-based CSS method, through which the topological information cost function and the sensing results cost function were constructed. Moreover, the reputation value was applied to evaluate the performance of CSS and independent sensing. The reputation threshold was theoretically analyzed to minimize the probability of choosing the sensing manner with worse performance. Simulations were carried out to verify the viability and the efficiency of the proposed algorithm.

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Cooperative Spectrum Sensing as Image Segmentation: A New Data Fusion Scheme

Cited by 14 publications

References 14 publications

Cooperative Fusion Architecture-based Distributed Spectrum Sensing Under Rayleigh Fading Channel

Cooperative Fusion Architecture-based Distributed Spectrum Sensing Under Rayleigh Fading Channel

Reuse of Byzantine data in cooperative spectrum sensing using sequential detection

Reputation-Based Spectrum Sensing Strategy Selection in Cognitive Radio Ad Hoc Networks

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