Analysis of Hard-Decision and Soft-Data Fusion Schemes for Cooperative Spectrum Sensing in Rayleigh Fading Channel

Nallagonda, Srinivas; Kumar, Y.R.; Shilpa, Pasham

doi:10.1109/iacc.2017.0057

Cited by 27 publications

(20 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Althunibat et al [9] conducted the comparison between soft-data combination and harddecision spectrum sensing schemes and analytically proved that the soft scheme be effective solution especially with the limitation of the sensing time. Nallagonda [10] analyzed the performance of soft-data fusion and hard-decision schemes for various sensing channels, and derived the closed-form analytic expressions of detection probability under various soft schemes in Rayleigh fading channel. By optimizing the sensing period and the searching time, Liu [11] proposed a periodic cooperative spectrum sensing model with weighted data fusion, which optimized the perception interval and search time.…”

Section: Related Workmentioning

confidence: 99%

Soft Decision Cooperative Spectrum Sensing With Entropy Weight Method for Cognitive Radio Sensor Networks

Lin

Liu

2020

IEEE Access

View full text Add to dashboard Cite

By fully utilizing the spatial gain and exploiting the multiuser diversity, cooperative spectrum sensing can enhance the sensing accuracy. In the actual wireless environment, the effect of shadowing and fading will result in the different features of signals received by the sensing nodes with different distances from primary user. As a result, some cooperative nodes in deep fading will suffer from serious missed detection, which will affect the final results during the fusing operation. To solve the above problems, a soft decision cooperative spectrum sensing with entropy weight method for cognitive radio sensor networks is presented. Initially, the sensor nodes will be organized into logical groups to obtain energy efficiency and improvement of sensing performance. After receiving the soft sensing information from all member nodes, the cluster heads employs the equal gain soft combination for inter-cluster fusion and then forwards the local decision to the fusion center. During the final decision, the entropy weight method is applied to assign optimal weight value to corresponding cluster local decisions. The simulation results show that the proposed method can outperform some typical clustering scheme for cooperative spectrum sensing in terms of the detection probability and the total error probability.

show abstract

Section: Related Workmentioning

confidence: 99%

Soft Decision Cooperative Spectrum Sensing With Entropy Weight Method for Cognitive Radio Sensor Networks

Lin

Liu

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Selection of fusion rule depends on the required detection accuracy and network topology. There are few widely used cooperative decision rules [7]- [10] the OR rule, the AND rule, the K out of N rule and machine learning approaches. OR is 1 out of N rule, AND is N out of N rule and K out of N is the flexible rule where K value can be varied as per the requirements.…”

Section: Decision Fusionmentioning

confidence: 99%

“…Pertaining to cooperative decision making, many researchers [7]- [10] have proposed and implemented different kinds of fusion rules considering the parameters such as communication overhead, processing time, complexity and detection accuracy. The author in [11] jointly optimized the sensing time (τ) and number of nodes required (N) with the objective of throughput maximization but shown any insight into the effect of fusion rule on these parameters.…”

Section: Introductionmentioning

confidence: 99%

An Optimized and Trained Model of Cooperative Sensing for Cognitive Radio Networks

T¹,

Basarkod²

2019

IJRTE

View full text Add to dashboard Cite

Sensing based spectrum allocation is one of the solutions to bridge the gap between spectrum scarcity and underutilization of allocated spectrum. In this context, cognitive radio technology has become the prominent solution for future wireless communication problems. To accurately detect the spectrum availability, CRN uses cooperative spectrum sensing where N number of selected nodes will be involved in making a decision on spectrum occupation. Various sensing parameters such as sensing duration (τ), decision threshold (λ), number of nodes (N) and decision rule (K) have huge impact on the performance of cooperative spectrum sensing. In addition, there are constraints on energy consumption and protection of licensed user’s needs to be considered. Our work focuses on optimization of sensing parameters to maximize the throughput of the cognitive radio network maintaining the energy efficiency and protecting the licensed users from the interference caused by the secondary users. The proposed work uses convex optimization to optimize sensing duration and two-dimensional search algorithm to find the values N and K. Further optimization is done by comparing local decision with cooperative decision.

show abstract

“…In the soft fusion combination schemes proposed in [30][31][32] sensing energies from different SUs are combined to take accurate decision about the PU spectrum holes. Similarly, in the hard fusion schemes SUs provide a hard binary decision to the FC to predict the licensed user activity in the spectrum [33][34][35]. The optimal quantization scheme in [36,37] is able to produce improved detection with a control on the probability of false alarm.…”

Section: Introductionmentioning

confidence: 99%

One‐to‐Many Relationship Based Kullback Leibler Divergence against Malicious Users in Cooperative Spectrum Sensing

Gul

Qureshi

Akbar

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The centralized cooperative spectrum sensing (CSS) allows unlicensed users to share their local sensing observations with the fusion center (FC) for sensing the licensed user spectrum. Although collaboration leads to better sensing, malicious user (MU) participation in CSS results in performance degradation. The proposed technique is based on Kullback Leibler Divergence (KLD) algorithm for mitigating the MUs attack in CSS. The secondary users (SUs) inform FC about the primary user (PU) spectrum availability by sending received energy statistics. Unlike the previous KLD algorithm where the individual SU sensing information is utilized for measuring the KLD, in this work MUs are identified and separated based on the individual SU decision and the average sensing statistics received from all other users. The proposed KLD assigns lower weights to the sensing information of MUs, while the normal SUs information receives higher weights. The proposed method has been tested in the presence of always yes, always no, opposite, and random opposite MUs. Simulations confirm that the proposed KLD scheme has surpassed the existing soft combination schemes in estimating the PU activity.

show abstract

Analysis of Hard-Decision and Soft-Data Fusion Schemes for Cooperative Spectrum Sensing in Rayleigh Fading Channel

Cited by 27 publications

References 7 publications

Soft Decision Cooperative Spectrum Sensing With Entropy Weight Method for Cognitive Radio Sensor Networks

Soft Decision Cooperative Spectrum Sensing With Entropy Weight Method for Cognitive Radio Sensor Networks

An Optimized and Trained Model of Cooperative Sensing for Cognitive Radio Networks

One‐to‐Many Relationship Based Kullback Leibler Divergence against Malicious Users in Cooperative Spectrum Sensing

Contact Info

Product

Resources

About