The evolving 5G and beyond 5G (B5G) wireless technologies are envisioned to provide ubiquitous connectivity and great heterogeneity in communication infrastructure by connecting diverse devices and providing multifarious services. Recently, the Internet of Things (IoT) and unmanned aerial vehicles (UAVs) are realized as an essential component of the upcoming 5G/B5G networks, enabling enhanced communication capacity, high reliability, low latency, and massive connectivity. However, one limiting factor in the expansion of 5G/B5G technology is the finite radio spectrum, which necessitates managing the anticipated spectrum crunch for future wireless networks. One potential solution is to develop intelligent cognitive methods to dynamically optimize the use of spectrum in 5G/B5G networks to solve the imminent problem of spectrum congestion and improve radio efficiency. This paper addresses the opportunistic spectrum access (OSA) problem in the 5G/B5G cognitive radio (CR) network of IoTs and UAVs through the novel deep learning-based detector, dubbed as Deep-CRNet. The proposed detector employs residual connections with cascaded multi-kernel convolutions to identify the primary user (PU) spectrum usage by extracting the inherent multi-scale signal and noise features in the sensed transmission patterns. Thereby, Deep-CRNet intelligently learns and locates the spectrum holes so that secondary users (SUs) and PUs can dynamically share network spectrum resources. The efficacy of Deep-CRNet is validated through simulation results, where it achieved 99.74% accuracy with 99.65% precision and 99.83% recall in accurately classifying the PU status. In addition, the average correct detection probability of Deep-CRNet in the low signal-to-noise ratio ( − 20 dB to − 15 dB) range is 38.21% higher than the second best-performing detector.

Deep learning-driven opportunistic spectrum access (OSA) framework for cognitive 5G and beyond 5G (B5G) networks

Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme.

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Spectrum sensing based on adaptive sampling of received signal

 Vacant frequency bands are used in cognitive radio (CR) by incorporating the spectrum sensing (SS) technique. Spectrum sharing plays a central role in ensuring the effectiveness of CR applications. Therefore, a new multi-stage detector for robust signal and spectrum sensing applications is introduced here. Initially, the sampled signal is subjected to SNR estimation by using a convolutional neural network (CNN). Next, the detection strategy is selected in accordance with the predicted SNR levels of the received signal. Energy detector (ED) and singular value-based detector (SVD) are the solutions utilized in the event of high SNR, whilst refined non-negative matrix factorization (MNMF) is employed in the case of low SNR. CNN weights are chosen via the Levy updated sea lion optimization (LU-SLNO) algorithm inspired by the traditional sea lion optimization (SLNO) approach. Finally, the outcomes of the selected detectors are added, offering a precise decision on spectrum tenancy and existence of the signal.

Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks

The invention relates to a spectrum sensing method based on a modulation signal cyclic autocorrelation function symmetric peak value. The method includes: enabling a cognitive user to perform cyclic autocorrelation function calculation on the received signal to form a two-dimensional signal detection domain, finally searching a symmetrical peak point in the signal detection domain to judge whether a main user signal exists or not, judging that the main user signal exists if the symmetrical peak point exists in the two-dimensional signal detection domain, or else judging that the main user signal does not exist. In order to ensure the false alarm probability of spectrum sensing, a symmetric peak point significance level factor is introduced in the symmetric peak point searching process. According to the method, whether a main user signal exists or not is judged according to the symmetry of a peak value of a modulation signal cyclic autocorrelation function; no priori knowledge of any main user signal and channel is needed, the influence of channel noise fluctuation on the spectrum sensing performance is eliminated, and the problem of signal spectrum sensing in a small signal-to-noise ratio and channel noise fluctuation environment is solved.

Spectrum sensing method based on modulation signal cyclic autocorrelation function symmetric peak value

Wind Turbine Fault Diagnosis Based on Cyclostationary Feature Detection from Compressive Samples

Cyclic spectrum is a basic tool to study the cyclostationary of signal. It has the advantages of high resolution, anti-interference ability and low channel environment sensitivity. These properties are beneficial to process digital modulated signals. There are two time-smoothed algorithms: the FFT Accumulation Method (FAM) and the Strip Spectral Correlation Algorithm (SSCA). SSCA is suitable for fast estimation and engineering realizable, while FAM has shortcoming of complex computation. But both methods need high sampling rate that requires high performance analog-to-digital converter. To improve the efficiency of cyclic spectrum estimation, we propose CS-SSCA algorithm in the framework of Compressive Sensing (CS). The cyclic spectrum can be recovered via compressed samples by SSCA. The theoretical analysis and simulation results prove that the improved algorithm displays better performance than CS-FAM under the same conditions.

Cyclic Spectrum Estimation Under Compressive Sensing by the Strip Spectral Correlation Algorithm

Spectrum sensing is a key technique for future intelligent networks, such as AdHoc network, wireless sensor networks. However, the existing works suffer from high computational complexity, which is a huge challenge for those networks composed of many nodes with the limited computing capabilities. In this paper, we propose a simple and novel method based on the generalized likelihood ratio test(GLRT) criterion and offer a specific algorithm based on maximum value of power spectrum calculated by multitaper spectral estimation. We first derive the statistical characteristics and probability density function of test statistic, and the closed-form expression of detection probability and false-alarm probability are obtained. Then, we carry out some simulations to verify the proposed algorithm by taking BPSK signal as an example. In the simulations, we compare the sensing performance between the proposed algorithm and the existing methods. Both theoretical analysis and simulation results demonstrate the advantages of the proposed algorithm over the existing methods.

GLRT-based spectrum sensing by exploiting Multitaper Spectral Estimation for cognitive radio network

The invention discloses a method for estimating the number of signals in broadband spectrum sensing based on a DPMM, and belongs to the technical field of broadband spectrum sensing. According to theinvention, the problem of low accuracy of estimating the number of signals in the received signal by using the existing method is solved. By selecting the cyclic spectrum of the signal as a characteristic, the cyclic spectrum of the signal is recovered, so that the anti-noise performance of the broadband spectrum sensing method is improved; the extracted cyclic spectrum is modelded into a Gaussianmixture model for signal and noise classification, signal elements are reserved after de-noising, the reserved signals are clustered, and the number of the signals in the received signals is estimated according to the calculated maximum probability. Under the condition of -4dB of the signal to noise ratio, the accuracy of signal number estimation can be improved by about 30% by adopting the method disclosed by the invention, and the anti-noise performance of the method disclosed by the invention is better. The method can be applied to the technical field of broadband spectrum sensing.

Method for estimating number of signals in broadband spectrum sensing based on DPMM

Modulation recognition of sparse multiband signals is a key technology for intelligent signal processing. However, the existing method suffers from high sampling rate and poor anti-noise performance. Therefore, compressed sensing and cyclic spectral density are combined to cope with the shortcomings of existing methods followed by the multi-layer perceptron (MLP) to recognize modulation mode of signal. Some simulations are carried out, and the simulation results show that the proposed algorithm is correct and effective.

CS-Based Modulation Recognition of Sparse Multiband Signals Exploiting Cyclic Spectral Density and MLP

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Papers

Cyclic Spectrum Estimation Under Compressive Sensing by the Strip Spectral Correlation Algorithm

GLRT-based spectrum sensing by exploiting Multitaper Spectral Estimation for cognitive radio network

Method for estimating number of signals in broadband spectrum sensing based on DPMM

CS-Based Modulation Recognition of Sparse Multiband Signals Exploiting Cyclic Spectral Density and MLP