Spectrum Sensing In Cognitive Radio Research Articles

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.

Simultaneous Power Harvesting and Cyclostationary Spectrum Sensing in Cognitive Radios

We expect that 5G will support a large volume of data traffic to provide various services with a low latency. This will inevitably require an increased amount of energy. Wireless power transfer needs a higher receiver sensitivity than data decoding. Increased electromagnetic fields may introduce harmful effects on living organisms. Fortunately, massive multiple-input and multiple-output (MIMO) can provide significant gains in radiated energy and spectral efficiencies. Cognitive radio and 5G are emerging technologies. In this paper, we show that cognitive spectrum sensing and power harvesting can be accomplished simultaneously. Energy detection (ED) with energy harvesting has been widely investigated. However, ED may not work at a low signal-to-noise ratio with a harsh signal fluctuation in millimeter-wave in 5G. Therefore, we focus on cyclostationary spectrum sensing in this paper. We show that the maximum likelihood cyclostationary detection that can be used for power harvesting is the power squared. The cyclic power can be conveniently harvested and used for spectrum sensing.

Enhanced Particle Swarm Optimization assisted Cooperative Spectrum Sensing in Cognitive Radio under Rayleigh Fading Scenario

When performing cooperative spectrum sensing by using Soft Decision Fusion (SDF), the weighting coefficients play a major role in the detection performance. In this work, by utilizing the Enhanced Particle Swarm Optimization (EPSO) is optimization of the weighting coefficient vector is carried out. The EPSO selects the best weighting coefficients from the weighting coefficient vector. The detection accuracy of the EPSO technique is evaluated and contrasted with traditional PSO, GA (Genetic Algorithm) and also with traditional Soft-Decision Fusion (SDF) methods by using MATLAB simulations. From simulation results, it is inferred that the proposed technique outperforms all other Soft-Decision methods over Rayleigh channel. An increased detection performance is obtained as inferred from the results.

Performance Analysis of Spectrum Sensing in Cognitive Radio under Low SNR and Noise Floor

Cognitive radio (CR) is a new technology that is proposed to improve spectrum efficiency by allowing unlicensed secondary users to access the licensed frequency bands without interfering with the licensed primary users. As there are several methods available for spectrum sensing, the energy detection (ED) is more popular due to its simple implementation. However, ED is more vulnerable to the noise uncertainty so for that reason, we present a robust detector using signal to noise ratio (SNR) with dynamic threshold energy detection technique is combined with the kernel principal component analysis (KPCA) in Cognitive Radio Networks (CRN). The primary purpose of kernel function is to ensure that its dependency relies on inner-product of data without the feature space data requirement. In this paper, with the aid of kernel function the spectrum sensing with the leading eigenvector approach is modified to a feature space of higher dimensionality.By introducing of efficient detection system with dynamic threshold facility helps the better detection levels even low SNR values with quite a lot of noise uncertainty levels. The simulation results of the proposed system reveal that KPCA outperforms with that of traditional PCA in terms of false alarm rate, detector performance when tested under various uncertainties for orthogonal frequency division multiplexing signal.

Effectiveness of Spectrum Sensing in Cognitive Radio toward 5G Technology

Effectiveness of Spectrum Sensing in Cognitive Radio toward 5G Technology

A New Method of Spectrum Sensing in Cognitive Radio for Dynamic and Randomly Modelled Primary Users

In this study, a new spectrum sensing method for cognitive radio (CR) is proposed for primary user (PU) signals which are modelled as random with unknown power. It is also assumed that, being dynamic, the PU might exhibit multiple status changes in the channel within the sensing period. As a novel approach for detecting dynamic and randomly modelled PUs, we propose a cumulative sum based weighted energy detector (CuS-WED) that only utilizes the samples between the last status change point (LSCP) of the PU and the end of the sensing period contrary to using all the observed data samples in the sensing period. It is shown that the proposed sensing strategy provides noticeable performance improvement in detecting dynamic and randomly modelled PUs. Probability of detection performance of the proposed sensing scheme is investigated for strictly one, at most two, and at most three status changes of the PU. Based on simulation results, it is concluded that the proposed method performs better than the other existing techniques, especially for low probability of false alarm values.

Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio.

This paper presents an approach to exploit the superimposed training (ST)-based primary users’ (PUs) transmissions in the context of spectrum sensing for cognitive radio. In the low signal-to-noise ratio (SNR), the proposed scheme splits the spectrum sensing phase into two sample processing periods, allowing a secondary user (SU) to carry out a training sequence synchronization (with a small probability of error) before the implementation of a robust spectrum sensing algorithm that enhances the detection, based on the deterministic signal components embedded in the ST PU’s signals along with the unknown data signal. The overall sensing performance is improved using a reasonable number of samples to achieve a high probability of detection, resulting in a reduced spectrum sensing duration. Furthermore, a low computational complexity version of the proposed ST combined approach for a reduced phase (SCAR-Phase) of spectrum sensing is presented, which attains the same detection performance with a smaller number of real operations in the low SNR. In the practical consideration of imperfect training sequence synchronizations, the results show the advantages of exploiting the ST sequence to perform spectrum sensing, thus quantifying the significant improvement in detection performance and the maximum SU’s achievable throughput.

An Investigation into a Novel Multi Slot Spectrum Sensing for Cognitive Radio

An Investigation into a Novel Multi Slot Spectrum Sensing for Cognitive Radio

Wavelet Transform and Artificial Neural Network Based Spectrum Sensing in Cognitive Radio

With the progress and upgrade of wireless telecommunication technologies, the lack of spectrum resources is pretty serious. As a method to improve spectrum utilization, cognitive radio has received widespread attention. The technology of spectrum sensing, one of the most significant parts of cognitive radio, has important research value. The existing spectrum sensing technology is difficult to detect the main user signal at a lower signal-to-noise-ratio (SNR).To solve this problem, wavelet transform and artificial neural network based spectrum sensing in cognitive radio is put forward. The spectrum detection perceptual classifier is designed through the wavelet transform, cyclostationary feature and the artificial neural network. Firstly, the wavelet threshold method is used to process the received signal. The cyclic characteristic value of the processed signal is calculated. Secondly, the eigenvalues are learned and stored through the artificial neural network. Finally, the test data is brought into the trained network for classification. Verifying by simulation experiments, the proposed algorithm has better accuracy and effectiveness than the original method in the low SNR environment.

Spectrum Sensing in Cognitive Radio Using Actor–Critic Neural Network with Krill Herd-Whale Optimization Algorithm

Spectrum sensing is the active research area in the Cognitive radio networks that initiates the effective data sharing between the licensed and the unlicensed users of Cognitive Network. Maximizing the detection probability for a provided false alarm rate is a hectic challenge of most of the spectral sensing methods. The paper proposes a spectral sensing method, termed as Krill-Herd Whale optimization-based actor critic neural network. The unoccupied spectrum is optimally determined using the proposed method that allocates the free spectrum bands to the primary users instantly such that the delay is minimized due to the effective functioning of the fusion center. For the effective sensing, the Eigen-value-based cooperative sensing is activated in the cognitive radio. The analysis of the proposed method is progressed based on the performance metrics, such as false alarm probability and detection probability. The proposed spectral sensing method outperforms the existing methods that yield a maximum probability of detection and minimum probability of false alarm at a rate of 0.9805 and of 0.009.

Fusion Rule Based on Dynamic Grouping for Cooperative Spectrum Sensing in Cognitive Radio

In this paper, we study the distributed cooperative spectrum sensing in heterogeneous cognitive radio networks, where each secondary user (SU) adopts a different spectrum sensing algorithm and experience a different channel environment. To solve the spectrum sensing problem, we propose a fusion rule based on dynamic grouping, which can be used in the mobility of the primary user and SUs. By introducing a grouping parameter, the proposed algorithm can realize the dynamic grouping of SUs. The SUs in group-1 will not be able to participate in the cooperation, and the SUs in groups-2 and groups-3 have different weighting factors. The proposed fusion rule is only required to process the independent decision result of each SU, which significantly reduces the data transmission overhead and processing delay. The simulation results show that the proposed fusion rule can effectively improve the dynamic grouping of SUs and fuse detection information, and outperform the OR rule, AND rule, equal gain combining, and maximum signal-to-noise ratio under the same conditions.

Multi Feature Based Classifier for Spectrum Sensing in Cognitive Radio

Cognitive Radio (CR) is an important technology which can enable the implementation of Dynamic Spectrum Access, which is a paradigm shift from the static spectrum access model. It is an intelligent wireless communication system which can sense the environment and can take decisions to effectively use the available radio resource without creating any interference to the Licensed Primary Users. Hence sensing of the spectrum plays a very important role in the effective implementation of this technology. We propose a new spectrum sensing algorithm in this paper which is based on machine learning and uses a Multi Feature based Classifier (MFC) model for classification of the spectrum.

Spectrum Sensing in Cognitive Radio by Use of Volume-Based Method

Spectrum sensing is the mission of finding the licensed user signal situation, i.e. to determine the existence and deficiency of primary (licensed) user signal, the recent publications random matrix theory algorithms performs better-quality in spectrum sensing. The RMT fundamental nature is to make use of the distributed extremal eigenvalues of the arrived signal sample covariance matrix (SMC), specifically, Tracy-Widom (TW) distribution which is useful to certain extent in spectrum sensing but demanding for numerical evaluations because there is absence of closed-form expression in it. The sample covariance matrix determinant is designed for two novel volume-based detectors or signal existence and deficiency cases are differentiated by using volume. Under the Gaussian noise postulation one of the detectors theoretical decision thresholds is perfectly calculated by using Random matrix theory. The volume-based detectors efficiency is shown in simulation results.

Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

A framework of spectrum sensing with a multi-class hypothesis is proposed to maximize the achievable throughput in cognitive radio networks. The energy range of a sensing signal under the hypothesis that the primary user is absent (in a conventional two-class hypothesis) is further divided into quantized regions, whereas the hypothesis that the primary user is present is conserved. The non-radio frequency energy harvesting-equiped secondary user transmits, when the primary user is absent, with transmission power based on the hypothesis result (the energy level of the sensed signal) and the residual energy in the battery: the lower the energy of the received signal, the higher the transmission power, and vice versa. Conversely, the lower is the residual energy in the node, the lower is the transmission power. This technique increases the throughput of a secondary link by providing a higher number of transmission events, compared to the conventional two-class hypothesis. Furthermore, transmission with low power for higher energy levels in the sensed signal reduces the probability of interference with primary users if, for instance, detection was missed. The familiar machine learning algorithm known as a support vector machine (SVM) is used in a one-versus-rest approach to classify the input signal into predefined classes. The input signal to the SVM is composed of three statistical features extracted from the sensed signal and a number ranging from 0 to 100 representing the percentage of residual energy in the node’s battery. To increase the generalization of the classifier, k-fold cross-validation is utilized in the training phase. The experimental results show that an SVM with the given features performs satisfactorily for all kernels, but an SVM with a polynomial kernel outperforms linear and radial-basis function kernels in terms of accuracy. Furthermore, the proposed multi-class hypothesis achieves higher throughput compared to the conventional two-class hypothesis for spectrum sensing in cognitive radio networks.

Improved MCAS Based Spectrum Sensing in Cognitive Radio

Improved MCAS Based Spectrum Sensing in Cognitive Radio

Implementation of Spectrum-Sensing for Cognitive Radio Using USRP with GNU Radio and a Cloud Server.

Implementation of Spectrum-Sensing for Cognitive Radio Using USRP with GNU Radio and a Cloud Server.

Cognitive Radio Spectrum Sensing Technique Using M-ary QAM for Different Fading Environments

Cognitive Radio Spectrum Sensing Technique Using M-ary QAM for Different Fading Environments