Spectrum Sensing Research Articles

Optimal Node Selection for Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks with Energy Harvesting

Optimal Node Selection for Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks with Energy Harvesting

Artificial Neural Network Based Spectrum Sensing in Wireless Regional Area Network

Artificial Neural Network Based Spectrum Sensing in Wireless Regional Area Network

Enhanced Support Vector Machine for Cooperative Spectrum Sensing Against Byzantine Attack in Cognitive Wireless Sensor Networks

Enhanced Support Vector Machine for Cooperative Spectrum Sensing Against Byzantine Attack in Cognitive Wireless Sensor Networks

Privacy Preserving Algorithm for Spectrum Sensing in Cognitive Vehicle Networks

Privacy Preserving Algorithm for Spectrum Sensing in Cognitive Vehicle Networks

A Cross-layer Performance Evaluation System for Spectrum Sensing and Allocation Strategies in CR-WSN

A Cross-layer Performance Evaluation System for Spectrum Sensing and Allocation Strategies in CR-WSN

Cost and Benefit Tradeoff of SSDF Attack in Cooperative Spectrum Sensing in Cognitive Wireless Sensor Networks

Cost and Benefit Tradeoff of SSDF Attack in Cooperative Spectrum Sensing in Cognitive Wireless Sensor Networks

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

Cooperative Spectrum Sensing with Ensemble Empirical Mode Decomposition and Dual-Channel Composite Neural Network for Complex Environments

Cooperative Spectrum Sensing with Ensemble Empirical Mode Decomposition and Dual-Channel Composite Neural Network for Complex Environments

Optimizing Spectrum Sensing in Cognitive Radio Networks Using Bayesian-Optimized Random Forest Classifier

Optimizing Spectrum Sensing in Cognitive Radio Networks Using Bayesian-Optimized Random Forest Classifier

1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing

Spectrum sensing is an essential function of cognitive radio technology that can enable the reuse of available radio resources by so-called secondary users without creating harmful interference with licensed users. The application of machine learning techniques to spectrum sensing has attracted considerable interest in the literature. In this contribution, we study cooperative spectrum sensing in a cognitive radio network where multiple secondary users cooperate to detect a primary user. We introduce multiple cooperative spectrum sensing schemes based on a deep neural network, which incorporate a one-dimensional convolutional neural network and a long short-term memory network. The primary objective of these schemes is to effectively learn the activity patterns of the primary user. The scenario of an imperfect transmission channel is considered for service messages to demonstrate the robustness of the proposed model. The performance of the proposed methods is evaluated with the receiver operating characteristic curve, the probability of detection for various SNR levels and the computational time. The simulation results confirm the effectiveness of the bidirectional long short-term memory-based method, surpassing the performance of the other proposed schemes and the current state-of-the-art methods in terms of detection probability, while ensuring a reasonable online detection time.

Enhance the Probability of Detection of Cooperative Spectrum Sensing in Cognitive Radio Networks Using Blockchain Technology

Cognitive radio (CR) is the best way to improve the efficiency of spectrum consumption for wireless multimedia communications. Spectrum sensing, which allows legitimate secondary users (SU) to find vacant bands in the spectrum, plays a vital role in CR networks. When cooperative sensing is used in CR networks, spectrum availability must be taken into account. In many ways, the shared cooperative spectrum sensing (CSS) data among SU. The presence of a malicious user (MU) in the system and sending false sensing data can degrade the performance of cooperative CR. The sharp rise in mobile data traffic causes congestion in the licensed band for the transmission of signals. Handling this security issue in real time, on top of spectrum sharing, is a challenge in such networks. In order to manage the spectrum and identify MU, blockchain-based CSS is developed in this article. To gauge the efficiency of the proposed topology, performance metrics like sensitivity, node selection, throughput measurement, and energy efficiency are used. This work suggests a unique, easier-to-use CSS method with MU suppression that outperforms the current one. According to simulation studies, the suggested topology can increase the likelihood of MU detection by roughly 15% when 40% of system users are malicious.

Joint Cooperative Spectrum Sensing and Resource Allocation in Dynamic Wireless Energy Harvesting Enabled Cognitive Sensor Networks

Joint Cooperative Spectrum Sensing and Resource Allocation in Dynamic Wireless Energy Harvesting Enabled Cognitive Sensor Networks

Multitask Learning-Based Deep Signal Identification for Advanced Spectrum Sensing.

The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device's signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use.

Defending Cooperative Spectrum Sensing From Byzantine Attacks: An Effective Entropy-Based Weighted Algorithm

Defending Cooperative Spectrum Sensing From Byzantine Attacks: An Effective Entropy-Based Weighted Algorithm

Blind Orthogonal Least Squares Based Compressive Spectrum Sensing

Blind Orthogonal Least Squares Based Compressive Spectrum Sensing

CNN-Based Multiantenna Spectrum Sensing for Hybrid Circular and Noncircular Source Signals in Cognitive Radio

CNN-Based Multiantenna Spectrum Sensing for Hybrid Circular and Noncircular Source Signals in Cognitive Radio

DETERMINATION OF THE THRESHOLD SOLUTION FOR A CHANNEL WITH RAY-LEIG FADING WHEN PROBING THE COGNITIVE RADIO ENERGY DETECTOR SPECTRUM

Spectrum sensor system performance and detection of spectrum band occupancy in cognitive radio are among of the main research aspects. When considering a system in which the primary user changes or there are multiple types of primary users, an energy detector is used. For energy detector operation, the main parameters that determine probabilistic characteristics of detection (probability of user detection, probability of a «false alarm» error and probability of a «missing target» error) will depend on the correct determination of the decision threshold. This article reviews an analytical approach to the normalized decision threshold determination. The purpose is to analyze and determine the optimal decision threshold value for an energy detector in a channel with Rayleigh fading. A spectrum sensing system in cognitive radio is considered. Normalized values of the threshold solution for a channel with Rayleigh fading are determined. Graphic illustrations of the analysis results and calculations are presented. Results obtained for determining threshold solutions may be considered a proper approximation for calculating the characteristics of sensing systems in cognitive radio in channels with Rayleigh fading, providing the opportunity to determine frequency bands free of primary users more likely, thereby increasing the efficiency of use of the radio frequency spectrum.

RNN-BIRNN-LSTM based spectrum sensing for proficient data transmission in cognitive radio

With the advancements that are taking place in the wireless communications field, the number of users who are utilizing resources is also increasing; as a result, the wireless spectrum is scarce. In this article, RNN-BIRNN-LSTM with Gaussian noise (RBRLG)-based spectrum sensing (SS) for QAM16, CPFSK, QPSK, and BPSK modulation schemes has been proposed. Recurrent Neural Networks for sequential data use recurrent connections to capture temporal dependencies; BIRNN extends RNN by processing input in both forward and backward directions, capturing past and future context; and finally, LSTM, using specialized memory cells, efficiently manages long-term dependencies in sequential data. In order to create a spectrum sensing model, RNN units, BIRNN units, and LSTM units were cascaded in this paper. Open-source dataset RadioML2016.10B has been used for the investigation. The experimental results show that the proposed RBRLG-based SS has higher accuracy on the dataset especially at -20 dB, a lower probability of miss detection percentage of 7.19 %, and a lower sensing error (SE) percentage of 10.80 % for QAM16. The evaluation of performance indicators for our suggested model, such as the F1 Score, Jaccard Index, and Matthew's correlation coefficient, demonstrates that the proposed model provides improved SS performance.

Spectrum Sensing based Trust Aware Routing for Cognitive Radio Ad hoc Networks (CRAHNs)

Spectrum Sensing based Trust Aware Routing for Cognitive Radio Ad hoc Networks (CRAHNs)

Design Problems in Cognitive Radio Networks for Spectrum Sensing

Cognitive radio is regarded as an innovative approach aimed at enhancing the efficient utilization of the valuable natural resource, namely the electromagnetic spectrum for radio communication. Based on principles of SDR, cognitive radio is wireless communication system with intelligence to interpret its environment. It uses "understanding-by-building" approach to learn about its surroundings and adjust to statistical variations in input signals.Providing rock-solid connectivity whenever and wherever it's needed.aiding in effective use of radio spectrum.Spectrum sensing methods are discussed in this abstract. Spectrum sensing requires energy, so keep that in mind. Therefore, our focus centers on energy-efficient spectrum sensing in order to conserve energy during transmissions, taking into account relevant factors. In this context, an efficient model will be proposed for cognitive radio, encompassing both analytical and simulation aspects.