On MMD-Based Secure Fusion Strategy for Robust Cooperative Spectrum Sensing

Yuan, Shuai; Li, Lei; Chigan, Chunxiao

doi:10.1109/tccn.2019.2906236

Cited by 17 publications

(9 citation statements)

References 24 publications

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“…In this paper, cooperative cognitive radio detection has been introduced to utilize spatial and temporal diversity for multiuser detection reliability [28][29][30][31][32][33]. Cooperative spectrum sensing is proposed to overcome the fading, shadowing, and noise uncertainty problems in a channel.…”

Section: Main Aim and Contributionmentioning

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: Main Aim and Contributionmentioning

confidence: 99%

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

Mokhtar

Saeed

Alhumyani

2022

Wireless Pers Commun

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“…Many different metrics are introduced to distinguish MUs and HUs [11], [17], [18]. In [11], based on double-sided neighbor distance and frequency check, a robust CSS was proposed to detect MUs.…”

Section: Related Workmentioning

confidence: 99%

“…In [11], based on double-sided neighbor distance and frequency check, a robust CSS was proposed to detect MUs. In [17], the maximum mean discrepancy (MMD) was used as a metric of distance for the sensing reports to distinguish MUs and HUs. The genuine reports from MUs may still be used for sensing data fusion.…”

Section: Related Workmentioning

confidence: 99%

Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

et al. 2020

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In a centralized cooperative spectrum sensing (CSS) system, it is vulnerable to malicious users (MUs) sending fraudulent sensing data, which can severely degrade the performance of CSS system. To solve this problem, we propose sensing data fusion schemes based on K-medoids and Mean-shift clustering algorithms to resist the MUs sending fraudulent sensing data in this paper. The cognitive users (CUs) send their local energy vector (EVs) to the fusion center which fuses these EVs as an EV with robustness by the proposed data fusion method. Specifically, this method takes a Medoids of all EVs as an initial value and searches for a high-density EV by iteratively as a representative statistical feature which is robust to malicious EVs from MUs. It does not need to distinguish MUs from CUs in the whole CSS process and considers constraints imposed by the CSS system such as the lack of information of PU and the number of MUs. Furthermore, we propose a global decision framework based on fast K-medoids or Mean-shift clustering algorithm, which is unaware of the distributions of primary user (PU) signal and environment noise. It is worth noting that this framework can avoid the derivation of threshold. The simulation results reflect the robustness of our proposed CSS scheme. INDEX TERMS Cognitive radio, robust cooperative spectrum sensing, sensing data fusion, K-medoids clustering algorithm, Mean-shift clustering algorithm.

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“…Khan et al in [26] proposed a double adaptive thresholding technique in order to differentiate legitimate users from doubtful and malicious users. Yuan et al in [27] proposed a maximum mean discrepancy (MMD)-based secure fusion strategy to defend against intelligent SSDF attack for collaborative spectrum sensing in CRNs. Gul et al in [28] outlined different techniques to mitigate the damaging effects of the false sensing in soft decision fusion (SDF) schemes using one-to-many sensing-distances and Z-score.…”

Section: Related Workmentioning

confidence: 99%

Reuse of Byzantine data in cooperative spectrum sensing using sequential detection

Song

et al. 2020

IET Communications

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On MMD-Based Secure Fusion Strategy for Robust Cooperative Spectrum Sensing

Cited by 17 publications

References 24 publications

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

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

Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

Reuse of Byzantine data in cooperative spectrum sensing using sequential detection

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