scholarly journals Design of Hybrid Blind Detection Based Spectrum Sensing Technique

2020 ◽  
Vol 12 (4) ◽  
pp. 575-583
Author(s):  
V. Sharma ◽  
S. Joshi
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Research Area ◽  
Detection Accuracy ◽  
Blind Detection ◽  
Spectrum Utilization ◽  
Detection Techniques ◽  
Generation Spectrum

Cognitive Radio is a boon to efficient utilization of spectrum to meet the demand of next generation. Spectrum Sensing (SS) is an active research area, essential to meet the requirement of efficient spectrum utilization as it detects the vacant bands. This paper develops a Hybrid Blind Detection (HBD) technique for cooperative spectrum sensing which combines the Energy Detector (ED) and the Anti-Eigen Value Detection (AVD) techniques together to enhance the detection accuracy of a cognitive radio. Collaboration among the cognitive users is achieved to reduce the error and hard fusion based detection is implemented to detect the existence of primary user. The detection accuracy of the design is evaluated with respect to detection probabilities and the results are examined for improvements with the traditional two stage detection techniques. Fusion rules for the cooperative environment are implemented and compared to detect majority rule suitable for the proposed design.

Optimal Linear Weighted Cooperative Spectrum Sensing for Clustered-based Cognitive Radio Networks

2021 ◽  
Author(s):  
Haiyan Ye ◽  
Jiabao Jiang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Detection Accuracy ◽  
Channel Fading ◽  
Spectrum Utilization ◽  
Optimal Linear ◽  
Static Division

Abstract The lack of spectrum resources restricts the development of the wireless communication-oriented applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, cognitive radio is regarded as an effective technology. Cooperative spectrum sensing with multi cognitive users can improve the low detection performance caused by channel fading or shadow effect. However, it also may lead to poor detection accuracy due to poor channel conditions of individual users. In order to solve the above problems, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

Efficient Recursive Least Square Technique for Spectrum Sensing in Cognitive Radio Networks

2019 ◽  
Vol 15 (2) ◽  
pp. 1-14
Author(s):  
Bommidi Sridhar ◽  
Srinivasulu Tadisetty
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Spectral Estimation ◽  
Primary User ◽  
Least Square ◽  
Electromagnetic Spectrum ◽  
Recursive Least Square ◽  
Spectrum Utilization ◽  
Overall Performance

Cognitive radio-based systems rely on spectrum sensing techniques to detect whitespaces to exploit. Cognitive radio (CR) is an attractive approach to face the shortage in the electromagnetic spectrum resources and improve the overall spectrum utilization. However, Energy detectors perform far from optimally by affecting the performance of the underlying system. In this article, two spectrum-sensing techniques are considered for CR networks; one based on energy detection and the other based on multi-taper spectral estimation (MSE). This article proposes a new method to optimize the overall performance in cooperative spectrum sensing in cognitive radio (CR) networks. An efficient recursive least square (ERLS)-based approach is proposed in order to optimize the overall performance to monitor the primary user active or inactive stage with use of secondary user while receiving data. An energy detector (ED) and multi-taper (MTM) spectrum sensing techniques are examined as local spectrum sensing techniques. Finally, a genetic algorithm is compared with the proposed system to show the system effectiveness.

scholarly journals Optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Haiyan Ye ◽  
Jiabao Jiang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Spectrum Utilization ◽  
Channel Characteristics ◽  
Optimal Linear ◽  
Static Division ◽  
Cooperative Nodes

AbstractThe lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

scholarly journals Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 129
Author(s):  
Mingdong Xu ◽  
Zhendong Yin ◽  
Yanlong Zhao ◽  
Zhilu Wu
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Primary User ◽  
Radio Spectrum ◽  
Local Information ◽  
Channel State ◽  
Single Node

cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.

Deep Cooperative Spectrum Sensing Utilizing Recurrent Convolutional Neural Networks

2020 ◽  
Author(s):  
Rahil Sarikhani ◽  
Farshid Keynia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Recurrent Neural Network ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Secondary Users ◽  
Spectrum Holes

Abstract Cognitive Radio (CR) network was introduced as a promising approach in utilizing spectrum holes. Spectrum sensing is the first stage of this utilization which could be improved using cooperation, namely Cooperative Spectrum Sensing (CSS), where some Secondary Users (SUs) collaborate to detect the existence of the Primary User (PU). In this paper, to improve the accuracy of detection Deep Learning (DL) is used. In order to make it more practical, Recurrent Neural Network (RNN) is used since there are some memory in the channel and the state of the PUs in the network. Hence, the proposed RNN is compared with the Convolutional Neural Network (CNN), and it represents useful advantages to the contrast one, which is demonstrated by simulation.

scholarly journals A Comparative Analysis of Different Outlier Detection Techniques in Cognitive Radio Networks with Malicious Users

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Arshed Ahmed ◽  
Muhammad Sajjad Khan ◽  
Noor Gul ◽  
Irfan Uddin ◽  
Su Min Kim ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Outlier Detection ◽  
Spectrum Sensing ◽  
Error Probability ◽  
Cooperative Spectrum Sensing ◽  
Minimum Error ◽  
Detection Techniques ◽  
Malicious User ◽  
Hidden Terminal ◽  
Median Test

In a cognitive radio (CR), opportunistic secondary users (SUs) periodically sense the primary user’s (PU’s) existence in the network. Spectrum sensing of a single SU is not precise due to wireless channels and hidden terminal issues. One promising solution is cooperative spectrum sensing (CSS) that allows multiple SUs’ cooperation to sense the PU’s activity. In CSS, the misdetection of the PU signal by the SU causes system inefficiency that increases the interference to the system. This paper introduces a new category of a malicious user (MU), i.e., a lazy malicious user (LMU) with two operating modes such as an awakened mode and sleeping mode. In the awakened mode, the LMU reports accurately the PU activity like other normal cooperative users, while in the sleeping mode, it randomly reports abnormal sensing data similar to an always yes malicious user (AYMU) or always no malicious user (ANMU). In this paper, statistical analysis is carried out to detect the behavior of different abnormal users and mitigate their harmful effects. Results are collected for the different hard combination schemes in the presence of the LMU and opposite categories of malicious users (OMUs). Simulation results collected for the error probability, detection probability, and false alarm at different levels of the signal-to-noise ratios (SNRs) and various contributions of the LMUs and OMUs confirmed that out of the many outlier detection tests, the median test performs better in MU detection by producing minimum error probability results in the CSS. The results are further compared by keeping minimum SNR values with the mean test, quartile test, Grubbs test, and generalized extreme studentized deviate (GESD) test. Similarly, performance gain of the median test is examined further separately in the AND, OR, and voting schemes that show minimum error probability results of the proposed test as compared with all other outlier detection tests in discarding abnormal sensing reports.

scholarly journals Performance Analysis Of Cognitive Radio Networks With Diversity And Faded Channels

2021 ◽  
Author(s):  
Sattar J. Hussain
Keyword(s):  
Cognitive Radio ◽  
Performance Analysis ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Multipath Fading ◽  
Radio Networks ◽  
Fusion Center ◽  
Detection Accuracy ◽  
Amplify And Forward ◽  
End To End

This dissertation presents new approaches for cognitive radio networks that combat fading effects and improve detection accuracy. We propose an advance framework for performance analysis of cooperative spectrum sensing over non-identical Nakagami- A detect-amplify-and-forward strategy is proposed to mitigate bandwidth requirements of relaying local observations to a fusion center. The end-to-end performance of a relay-based cooperative spectrum sensing over independent identically distributed Rayleigh fading channels is also investigated in this dissertation. Specifically, we aim to incorporate sensing time, end-to-end SNR, and end-to-end channel statistic into the performance analysis of cooperative CR networks. We also propose a cluster-based cooperative spectrum sensing approach to overcome the bandwidth limitations of the reporting links. The approach reduces the number of reporting terminals to a minimal reporting set and replaces the global fusion center by a local fusion center to mitigate the destructive channel conditions of global relaying channels. A new approach is proposed to select the location of the local fusion center using the general center scheme in graph theory. We aim to show that multipath fading on relaying channels yields similar performance degradations as multipath fading on sensing channels. With the detect-amplify-and forward strategy, refraining the heavily faded relays improves the detection accuracy. A gain of 3 dB is achieved by switching from amplify-and-forward strategy to detect-amplify-and-forward strategy with 3 cooperative users. Compared to the non-cooperative spectrum sensing, a gain of up to 8 dB is achieved with 4 cooperative users and equal gain combining receiver. Similar experimental set up but with selection combining receiver, a gain of 5 dB is achieved.

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