Spectrum Sensing In Cognitive Radio Research Articles

Encapsulation of Energy Efficient, Clustering Algorithm and Spectrum Sensing for Cognitive Radio Based Internet of Things Networks

Since the two decade the Internet of Things (IoT) plays an important role in the field of communication technology. Out of maximum of IoT devices are battery operated. So there should be energy efficient devices that can operate more functions with less power consumption. The main power constraints in the IoT devices are in the communication like spectrum selection, data transmission, etc. To perform communication between the two nodes the spectrum must be available. But as spectrums are limited and a lot of data is to be transmitted there may the issue of spectrum scarcity. The dynamic spectrum access is done using Cognitive Radio (CR) technology that can overcome spectrum scarcity issue. This paper gives the research work on energy efficient, clustering algorithm and spectrum sensing for CR based IoT networks in terms of the methods, merits, demerits and implementation. For efficiency in the spectrum sensing and energy consumption in 5G wireless communication network and data transfer between the IoT devices this study is essential. The biblometric analysis is shown by using VOSviewer to visualize the bibliometric information and the result as an analysis of ROC curve for Rayleigh and Rician channel is plotted using Matlab.

Secure Federated Learning for Cognitive Radio Sensing

This paper considers reliable and secure Spectrum Sensing (SS) based on Federated Learning (FL) in the Cognitive Radio (CR) environment. Motivation, architectures, and algorithms of FL in SS are discussed. Security and privacy threats on these algorithms are overviewed, along with possible countermeasures to such attacks. Some illustrative examples are also provided, with design recommendations for FL-based SS in future CRs.

Multi-Antenna Assisted Spectrum Sensing for Cognitive Radio in Nakagami-M Fading Channel

CR (Cognitive Radio) is a key technology that enables the limited and inefficiently used frequency bands to be used more effectively with an opportunistic approach. Communication performance and continuity in cognitive radio networks are highly dependent on whether the spectrum sensing function is performed correctly or not. Spectrum sensing is a critical issue of cognitive radio technology because of the shadowing, fading, and time-varying natures of wireless channels. To sense the limited or unused frequency bands, different methods for spectrum sensing have been proposed. Here, improved energy detection is used for this work. Energy detection is a spectrum sensing technique based on measuring the received signal energy and deciding the presence or absence of the primary user by comparing the received energy level with a threshold. Fading channels shows that the speed of the SU increases, the energy detection performance decreases in deterioration in detection probability

A Cooperative Cognitive Radio Spectrum Sensing Based on Correlation Sum Method with Linear Equalization

For moving forward toward the next generations of information technology and wireless communication, it is becoming necessary to find new resources of spectrum to fulfill the requirements of next generations from higher data rates and more capacity. Increasing efficiency of the spectrum usage is an urgent need as an intrinsic result of the rapidly increasing number of wireless users and the conversion of voice-oriented applications to multimedia applications. Spectrum sensing techniques in cognitive radio technology work upon an optimal usage of the available spectrum determined by the Federal Communication Commission (FCC). In this paper, the performance of a cooperative cognitive radio spectrum sensing detection based on the correlation sum method by utilizing the multiuser multiple input multiple output (MU_MIMO) technique over fading and Additive White Gaussian Noise (AWGN) channel is analyzed. Equalization is used at the receiver to compensate the effect of fading channels and improve the reliability of spectrum sensing. The performance is compared with the performance of Energy detection technique. The simulation results show that the detection performance of cooperative correlation sum method is more efficient than that obtained for the cooperative Energy detection technique.

CNN-SVM Spectrum Sensing in Cognitive Radio Based on Signal Covariance Matrix

Among the basic components of cognitive radio technology is spectrum sensing technology. Cooperative spectrum sensing is also a crucial factor research directions of spectrum sensing. Due to the problems of channel fading and hidden nodes, the spectrum sensing based on a single node cannot meet the requirements of the cognitive radio system for the primary user (PU) detection performance. A spectrum sensing algorithm is based on CNN-SVM and covariance matrix. In this algorithm, the covariance matrix of the signal is input into the convolutional neural network for feature extraction. Secondly, the result of the fully connected layer is taken as the input of the support vector machine for training to obtain a classifier, and finally, spectrum sensing is performed. The simulation outcomes demonstrate that the algorithm suggested in this research works outperform other algorithms in this paper. When the signal-to-noise ratio is -14 dB, the false alarm probability is 0.1, the number of secondary users is 32, and the detection probability reaches 0.81.

Spectrum Sensing for Cognitive Radio Based on Feature Extraction and Deep Learning

In cognitive radio, spectrum sensing is used to determine whether the primary user is using the spectrum based on the signal received on a specific frequency band, thereby determining whether the secondary user can use the spectrum. The main problem faced by spectrum sensing is how to identify the existence of the primary signal under the condition of low signal-to-noise ratio (SNR). Compared with traditional technologies, deep learning methods can identify the features of input data more efficiently and accurately. Based on convolutional neural network (CNN), This paper regard spectrum sensing as a binary classification problem. In the method we proposed, different features of received are extracted, and a dataset of feature matrices obtained under different SNRs is constructed for the training of the CNN network. Experiment results show that under the condition of low signal-to-noise ratio, the performance of our method is improved compared with the traditional method, and the combination of different features can improve the sensing accuracy.

Adversarial Learning-Based Spectrum Sensing in Cognitive Radio

Adversarial Learning-Based Spectrum Sensing in Cognitive Radio

Ensemble Classifier with Heterogenous Fusion Center for Cooperative Spectrum Sensing in Cognitive Radio

Cooperative spectrum sensing (CSS) in a cognitive radio uses a fusion center, which receives local sensing decisions from multiple secondary users to predict whether primary user is present or absent. Therefore, an ensemble classifier with heterogenous fusion center (EC-HFC) is proposed in this work, where the ensemble classifier comprise three classification algorithms such as logistic regression (LR), support vector machine (SVM), and gaussian naive bayes (GNB). In addition, voting classifier with its variants also employed for finding the best suitable classifier. Further, the performance metrics such as accuracy, F1-score, area under the curve (AUC), probability of detection and probability of false alarm are computed for evaluating the performance of proposed ensemble classifier-based fusion center for cooperative spectrum sensing in cognitive radio. Finally, the obtained receiver operating characteristics (ROC) and extensive simulation results shows that proposed fusion center resulted in superior performance as compared to individual secondary users.

Federated Learning-Based Cooperative Spectrum Sensing in Cognitive Radio

Deep cooperative sensing is a cooperative spectrum sensing (CSS) algorithm based on a deep neural network (DNN). Since training DNN requires a large amount of sample data, what is worse existing algorithms directly send training data to the fusion center (FC), which greatly occupies the transmission channel. Motivated by this, in this letter, we introduce the federated learning framework to CSS and propose a federated learning-based spectrum sensing (FLSS) algorithm. In the federated learning framework, there is no need to gather data together. Each secondary user (SU) uses local data to train the neural network model and sends the gradient to FC to integrate the parameters. This framework can perform collaborative training while ensuring local data privacy and greatly reducing the traffic load between SU and FC. Besides, we adopt an efficient network model ShufflenetV2 to reduce the number of parameters and improve training efficiency. Simulation results demonstrate that the FLSS can achieve a detection probability of 98.78% with a false alarm probability of 1% at SNR = −15dB.

Spectrum Sensing in Cognitive Radio Using CNN-RNN and Transfer Learning

Cognitive radio has been proposed to improve spectrum utilization in wireless communication. Spectrum sensing is an essential component of cognitive radio. The traditional methods of spectrum sensing are based on feature extraction of a received signal at a given point. The development in artificial intelligence and deep learning have given an opportunity to improve the accuracy of spectrum sensing by using cooperative spectrum sensing and analyzing the radio scene. This research proposed a hybrid model of convolution and recurrent neural network for spectrum sensing. The research further enhances the accuracy of sensing for low SNR signals through transfer learning. The results of modelling show improvement in spectrum sensing using CNN-RNN compared to other models studied in this field. The complexity of an algorithm is analyzed to show an improvement in the performance of the algorithm.

Eigenvalue Fusion based Machine Learning Approach for Cooperative Spectrum Sensing in Cognitive Radio

Eigenvalue Fusion based Machine Learning Approach for Cooperative Spectrum Sensing in Cognitive Radio

Estimation of Distribution of Primary Channel Periods Based on Imperfect Spectrum Sensing in Cognitive Radio

Spectrum occupancy modeling plays an important role in improving the performance of Cognitive Radio (CR) systems, because most of the methods to improve Dynamic Spectrum Access (DSA) performance need to make assumptions about the channel periods’ distribution. However, Secondary Users (SUs) cannot directly observe the accurate channel periods’ distribution due to the sensing errors caused by noise, which will lead to inaccurate analysis of transmission efficiency or throughput. In this paper, we analyze the influence of different types of sensing errors on the observed channel periods, and establish the relationship between the Probability Mass Function (PMF) of idle periods under Imperfect Spectrum Sensing (ISS) and the results under Perfect Spectrum Sensing (PSS). In addition, we derive a closed-form expression for estimating the PMF of channel periods from the sensing results under ISS. Simulation results show that the proposed estimation method is more accurate than the existing methods, and is not affected by the sensing period, channel mean period and sensing error probability.

Improved Convolutional Neural Network Based Cooperative Spectrum Sensing For Cognitive Radio

Improved Convolutional Neural Network Based Cooperative Spectrum Sensing For Cognitive Radio

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge.

Spectrum Sensing (SS) plays an essential role in Cognitive Radio (CR) networks to diagnose the availability of frequency resources. In this paper, we aim to provide an in-depth survey on the most recent advances in SS for CR. We start by explaining the Half-Duplex and Full-Duplex paradigms, while focusing on the operating modes in the Full-Duplex. A thorough discussion of Full-Duplex operation modes from collision and throughput points of view is presented. Then, we discuss the use of learning techniques in enhancing the SS performance considering both local and cooperative sensing scenarios. In addition, recent SS applications for CR-based Internet of Things and Wireless Sensors Networks are presented. Furthermore, we survey the latest achievements in Spectrum Sensing as a Service, where the Internet of Things or the Wireless Sensor Networks may play an essential role in providing the CR network with the SS data. We also discuss the utilisation of CR for the 5th Generation and Beyond and its possible role in frequency allocation. With the advancement of telecommunication technologies, additional features should be ensured by SS such as the ability to explore different available channels and free space for transmission. As such, we highlight important future research axes and challenging points in SS for CR based on the current and emerging techniques in wireless communications.

Deep Learning for Spectrum Sensing in Cognitive Radio

The detection of primary user signals is essential for optimum utilization of a spectrum by secondary users in cognitive radio (CR). The conventional spectrum sensing schemes have the problem of missed detection/false alarm, which hampers the proper utilization of spectrum. Spectrum sensing through deep learning minimizes the margin of error in the detection of the free spectrum. This research provides an insight into using a deep neural network for spectrum sensing. A deep learning based model, “DLSenseNet”, is proposed, which exploits structural information of received modulated signals for spectrum sensing. The experiments were performed using RadioML2016.10b dataset and the outcome was studied. It was found that “DLSenseNet” provides better spectrum detection than other sensing models.

A Deep Neural Network Model for Hybrid Spectrum Sensing in Cognitive Radio

Spectrum sensing (SS) is an essential task of the secondary user (SU) in a cognitive radio system. SS monitors the primary user (PU) activity in order to avoid any collision with SU, as the latter should be silent when PU is active on a given channel. Hybrid SS (HSS) is one of the powerful methods used to monitor PU activity. It consists of using different detectors together to make a final decision on the PU status. In this manuscript, artificial neural networks (ANN) are used to perform HSS. Since our data is composed from the test statistics (TSs) of several detectors, thus it can be modeled as tabular. Fully connected neural networks become the most suitable ANN model. We applied cutting-edge techniques in the field of deep learning in order to get the best possible accurate neural network model in our application. These techniques boil down to: embedding, regularization, batch normalization and smart learning rate selection. With the help TSs related to several detectors, ANN is trained to distinguish between two hypotheses, $$H_0$$ : PU is absent and $$H_1$$ : PU is active. Numerical results show the effectiveness of our proposed ANN-based HSS, as it outperforms the classical ANN-based energy detector and proves its capability to detect PU signal at very low SNR.

Deep STFT-CNN for Spectrum Sensing in Cognitive Radio

Spectrum sensing is one of the crucial technologies used to solve the shortage of spectrum resources. In this letter, based on the short-time Fourier transform (STFT) and convolutional neural network (CNN), we firstly develop a STFT-CNN method for spectrum sensing. The proposed method exploits the time-frequency domain information of the signal samples and achieves the state of the art detection performance. In particular, the method is suitable for various primary users’ signals and does not need any priori information. Besides, we also analyze the signal-to-noise ratio robustness and the generalization ability of the proposed algorithm. Finally, simulation results demonstrate that the proposed method outperforms other popular spectrum sensing methods. Notably, the proposed method can achieve a detection probability of 90.2% with a false alarm probability of 10% at SNR = −15dB.

Deep Learning-Based Spectrum Sensing in Cognitive Radio: A CNN-LSTM Approach

For most existing spectrum sensing detectors, the design of their test statistics relies on certain signal-noise model assumptions and hence, their detection performance heavily depends on the accuracy of the assumed models. Therefore, recently, much attention in the research of spectrum sensing is focused on deep learning which is free from model assumptions. Note that, in deep learning, the convolutional neural networks (CNNs) and the long-short term memory (LSTM) networks have the powerful capabilities in extracting spatial and temporal features of the input, respectively. In this letter, we propose a CNN-LSTM detector which first uses the CNN to extract the energy-correlation features from the covariance matrices generated by the sensing data, then the series of energy-correlation features corresponding to multiple sensing periods are input into the LSTM so that the PU activity pattern can be learned. The purpose of learning PU activity pattern is to further promote the detection probability. With sufficient simulations, the superiority of the CNN-LSTM detector is proven in scenarios with and without noise uncertainty.

An Adaptive Covariance Matrix Based on Combined Fully Blind Self Adapted Method for Cognitive Radio Spectrum Sensing

The main aim of this paper is to analysis and simulate existing methods such as energy detection, Maximum–Minimum Eigen value detectors (MME), MME with blind two stage detector and compare with the proposed adaptive covariance threshold method. This comparison is being made keeping in mind the complexity and accurateness in the terms of the sensing receiver operating characteristics curve. The influence of signal bandwidth of signal in comparison to the bandwidth of observation is done for every detector. As of the MME detector, the ratio between the bandwidth of the signal and the bandwidth of the observation is observed to be 0.5 when the reasonable values are used. The performance of the adaptive covariance threshold with combined full blind self-adapted detector are simulated on Matlab. The proposed method of detector shows a superior performance values when compared to three individual detectors. The performance metrics of proposed method are performed better than other three individual detector.

Optimized Complexity Problem Based on Combined Fully Blind Self Adapted Method using PSO for Cognitive Radio Spectrum Sensing

The main purpose of this paper is to solve the complexity problem and improved existing methods such as energy detection, Maximum-Minimum Eigen value detectors (MME), MME with blind two stage detector and adaptive covariance threshold method. PSO algorithm stands for Particle Swarm Optimization is utilized to novelty the optimal sensing time at that there will be maximum detection of probability for the Primary user. In which, we compare the optimal-PSO algorithm with non-optimal method. The simulation of the paper is depend on the performance of probability of detection based on the false alarm rate on the different region of the signal to noise ratio. The proposed optimized method of detector shows a superior performance values when compared to three individual detectors. The performance metrics of proposed method are performed better than other three individual detector.