scholarly journals A Novel Semi-Soft Decision Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2522 ◽  
Author(s):  
Yin Mi ◽  
Guangyue Lu ◽  
Yuxin Li ◽  
Zhiqiang Bao
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Transmission Band ◽  
Primary User ◽  
Detection Performance ◽  
Final Decision ◽  
Soft Decision ◽  
Hidden Terminal ◽  
Sensing Performance

Spectrum sensing (SS) is an essential part of cognitive radio (CR) technology, and cooperative spectrum sensing (CSS) could efficiently improve the detection performance in environments with fading and shadowing effects, solving hidden terminal problems. Hard and Soft decision detection are usually employed at the fusion center (FC) to detect the presence or absence of the primary user (PU). However, soft decision detection achieves better sensing performance than hard decision detection at the expense of the local transmission band. In this paper, we propose a tradeoff scheme between the sensing performance and band cost. The sensing strategy is designed based on three modules. Firstly, a local detection module is used to detect the PU signal by energy detection (ED) and send decision results in terms of 1-bit or 2-bit information. Secondly, and most importantly, the FC estimates the received decision data through a data reconstruction module based on the statistical distribution such that the extra thresholds are not needed. Finally, a global decision module is in charge of fusing the estimated data and making a final decision. The results from a simulation show that the detection performance of the proposed scheme outperforms that of other algorithms. Moreover, savings on the transmission band cost can be made compared with soft decision detection.

scholarly journals Trust Aware Scheme based Malicious Nodes Detection under Cooperative Spectrum Sensing for Cognitive Radio Networks

2020 ◽  
Author(s):  
Abhishek Kumar ◽  
Nitin Gupta ◽  
Riya Tapwal
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Base Station ◽  
Final Decision ◽  
Spectrum Utilization ◽  
Malicious Nodes ◽  
Hidden Terminal

<div>Emerging of Cognitive Radio (CR) technology has</div><div>provided optimistic solution for the dearth of spectrum by</div><div>improving the spectrum utilization. The opportunistic use of the spectrum is enabled by spectrum sensing which is one of the key functionality of CR systems. To perform the interference free transmission in a cognitive radio networks, an important part for unlicensed user is to identify a licensed user with the help of spectrum sensing. Recently, the Cooperative Spectrum Sensing has been widely used in the literature where various scattered unlicensed users collaborate with each other to make the final sensing decision. This overcome the hidden terminal problem</div><div>and also improve the overall reliability of the decisions made</div><div>about the presence or absence of a licensed user. Each unlicensed user send the sensing results to the base station for final decision. However there exist some nodes which do not provide the correct sensing results to maximize their own profit which can highly degrade the CR network functionality. In this paper, a trust aware model is proposed for detection of misbehaving nodes such that their sensing reports can be filter out from the final result. The performance evaluation of the proposed scheme is done by checking its robustness and efficiency against various possible attacks. </div>

scholarly journals Trust Aware Scheme based Malicious Nodes Detection under Cooperative Spectrum Sensing for Cognitive Radio Networks

2020 ◽  
Author(s):  
Abhishek Kumar ◽  
Nitin Gupta ◽  
Riya Tapwal
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Base Station ◽  
Final Decision ◽  
Spectrum Utilization ◽  
Malicious Nodes ◽  
Hidden Terminal

<div>Emerging of Cognitive Radio (CR) technology has</div><div>provided optimistic solution for the dearth of spectrum by</div><div>improving the spectrum utilization. The opportunistic use of the spectrum is enabled by spectrum sensing which is one of the key functionality of CR systems. To perform the interference free transmission in a cognitive radio networks, an important part for unlicensed user is to identify a licensed user with the help of spectrum sensing. Recently, the Cooperative Spectrum Sensing has been widely used in the literature where various scattered unlicensed users collaborate with each other to make the final sensing decision. This overcome the hidden terminal problem</div><div>and also improve the overall reliability of the decisions made</div><div>about the presence or absence of a licensed user. Each unlicensed user send the sensing results to the base station for final decision. However there exist some nodes which do not provide the correct sensing results to maximize their own profit which can highly degrade the CR network functionality. In this paper, a trust aware model is proposed for detection of misbehaving nodes such that their sensing reports can be filter out from the final result. The performance evaluation of the proposed scheme is done by checking its robustness and efficiency against various possible attacks. </div>

scholarly journals Cooperative Spectrum Sensing Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4440
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Neural Networks ◽  
Spectrum Sensing ◽  
Convolutional Neural Networks ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Majority Voting ◽  
Signal To Noise ◽  
Hidden Terminal ◽  
Simulation Results ◽  
Sensing Performance

Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.

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.

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