Spectrum Sensing Research Articles

Distributed Sequential Detection for Cooperative Spectrum Sensing in Cognitive Internet of Things.

The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. Therefore, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources so that IoT devices can access them and, meanwhile, avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of sensing the PUs signal in IoT devices, the issue of sensing time and decision cost (the cost of determining whether the signal state of the PU is correct or incorrect) arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the sensing time and cost functions for IoT devices and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal sensing time problem as a finite horizon problem and solve the threshold of the optimal decision rule by person-by-person optimization (PBPO) methodology and dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various costs of each observation taken and thresholds.

Spectrum utilization improvement for multi‐channel EH‐CRN with spectrum sensing

AbstractDue to the ever‐growing applications and services of the Internet of Things (IoT), designing energy‐efficient and spectral‐efficient transmission schemes to support IoT devices for the 6G space–air–ground integrated networks becomes much more challenging. Fortunately, energy harvesting (EH) and cognitive radio (CR) technologies have been proposed to alleviate these challenges. Inspired by this fact, this paper studies the issue of spectrum reuse in terms of spectrum utilization efficiency (SUE) in the energy harvesting cognitive radio network (EH‐CRN), where multiple primary transceiver pairs, one multi‐antenna secondary transmitter (ST), and one secondary base station (SBS) coexist. To characterize the impact of small‐scale fading and improve the SUE of the EH‐CRN with perfect spectrum sensing (SS), an adaptive scheme concerning SS, channel selection, EH, and data transmission (SCED) scheme are proposed, where the ST selects the channels for SS based on the residual energy, and adjusts the duration of EH and data transmission with respect to the sensing results. Then the Markov decision process problem of SUE is formulated, which is challenging due to the infinite system space and action space. To tackle the Markov decision process problem, the system space and action space are discreted, and divide the ST into the energy‐limited case and energy‐sufficient case according to specific energy condition. Moreover, theoretical results are extended to the EH‐CRN with imperfect SS. Numerical results show that the SUE under the SCED scheme in perfect SS and imperfect SS scenarios is better than that under other schemes.

Performance of Detectors for Cooperative Spectrum Sensing Under Laplacian Noise

Electronic systems in general can be impaired by impulsive noise generated by a variety of sources. Spectrum sensors are of particular interest herein, since their probabilities of detection and false alarm can be severely degraded under this impairment. Several models for impulsive noise have been studied in the literature, all of them having the common characteristic of being well represented by heavy-tailed probability density functions, like Laplace and some Stable distributions. This article addresses the performances of state-of-the-art detectors for cooperative spectrum sensing when the received signal is impaired by Laplacian noise. This is made by means of estimating the probability of detection for a fixed false alarm rate, when important system parameters are varied. It is demonstrated that the robustness against impulsive noise varies significantly depending on the adopted detection strategy.

Deep Learning approach for Co-operative Spectrum Sensing under Congested Cognitive IoT networks

Deep Learning approach for Co-operative Spectrum Sensing under Congested Cognitive IoT networks

Spectrum sensors for detecting type of airport lamps in a light photometry system

Spectrum sensors for detecting type of airport lamps in a light photometry system

Analyzing the role of spectrum sensing for security enhancement in beyond 5G waveforms using energy detection

Spectrum sensing (SS) plays a pivotal role in bolstering the security of 5G waveforms. In this abstract, we explore how it contributes to safeguarding 5G networks. By continuously monitoring the radio environment, spectrum sensing allows for the early detection of unauthorized or malicious signals. Cognitive radio (CR) technology is a cutting-edge innovation that can alleviate the need for additional spectrum. It has the potential to influence the course of technology if applied effectively. A CR network allows secondary, unlicensed users to make opportunistic use of primary, licensed spectrum without negatively impacting primary, licensed transmission or customers. Spectrum sensing is the most crucial step in implementing opportunistic spectrum access. In the recent decade, many different spectrum sensing strategies have been proposed in the scientific literature. In this paper, the performance of energy detection-based cooperative spectrum sensing has been measured and visualized using MATLAB simulations.

CyberSpec: Behavioral Fingerprinting for Intelligent Attacks Detection on Crowdsensing Spectrum Sensors

Integrated sensing and communication (ISAC) is a novel paradigm using crowdsensing spectrum sensors to help with the management of spectrum scarcity. However, well-known vulnerabilities of resource-constrained spectrum sensors and the possibility of being manipulated by users with physical access complicate their protection against spectrum sensing data falsification (SSDF) attacks. Most recent literature suggests using behavioral fingerprinting and Machine/Deep Learning (ML/DL) for improving similar cybersecurity issues. Nevertheless, the applicability of these techniques in resource-constrained devices, the impact of attacks affecting spectrum data integrity, and the performance and scalability of models suitable for heterogeneous sensors types are still open challenges. To improve limitations, this work presents seven SSDF attacks affecting spectrum sensors and introduces CyberSpec, an ML/DL-oriented framework using device behavioral fingerprinting to detect anomalies produced by SSDF attacks affecting resource-constrained spectrum sensors. CyberSpec has been implemented and validated in ElectroSense, a real crowdsensing RF monitoring platform where several configurations of the proposed SSDF attacks have been executed in different sensors. A pool of experiments with different unsupervised ML/DL-based models has demonstrated the suitability of CyberSpec detecting the previous attacks within an acceptable timeframe.

Spectrum Sensing via Residual Dilated Network and Horizontal Shift Attention for Cognitive IoT

Spectrum Sensing via Residual Dilated Network and Horizontal Shift Attention for Cognitive IoT

Spectrum Sensing and SINR Estimation in IEEE 802.11s Cognitive Radio Ad Hoc Networks with Heterogeneous Interference

Spectrum Sensing and SINR Estimation in IEEE 802.11s Cognitive Radio Ad Hoc Networks with Heterogeneous Interference

Robust Spectrum Sensing and Signal Classification: Enhanced Out-of-Distribution Detection Incorporating a Voting Mechanism

Robust Spectrum Sensing and Signal Classification: Enhanced Out-of-Distribution Detection Incorporating a Voting Mechanism

OPTIMIZATION OF ENERGY EFFICIENCY IN MULTI-CHANNEL CO-OPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO NETWORKS

In this paper, the focus is on energy efficiency in cooperative spectrum sensing in cognitive radio networks by considering multiple channels in a slotted time scenario. The sensing time consists of mini slots. A secondary user uses a mini-slot to sense a channel. Considering multiple channels, throughput, energy consumption, and, finally, energy efficiency are calculated. Additionally, the optimal number of mini-slots are obtained regarding throughput and energy efficiency using the particle swarm optimization algorithm. Simulation results are presented for the algorithms considered in this paper.

AI-Empowered Multiple Access for 6G: A Survey of Spectrum Sensing, Protocol Designs, and Optimizations

AI-Empowered Multiple Access for 6G: A Survey of Spectrum Sensing, Protocol Designs, and Optimizations

Enhancing Network Longevity and Mitigating Emulation Attack Through Adaptive Metaheuristic Spectrum Sensing in Cognitive Radio Sensor Network

Enhancing Network Longevity and Mitigating Emulation Attack Through Adaptive Metaheuristic Spectrum Sensing in Cognitive Radio Sensor Network

SCM-GNN: A Graph Neural Network-Based Multi-Antenna Spectrum Sensing in Cognitive Radio

SCM-GNN: A Graph Neural Network-Based Multi-Antenna Spectrum Sensing in Cognitive Radio

Kendall's Tau Based Spectrum Sensing for Cognitive Radio in the Presence of Laplace Noise

Kendall's Tau Based Spectrum Sensing for Cognitive Radio in the Presence of Laplace Noise

Support Vector Machine Dynamic Selection of Voting Rule for Cooperative Spectrum Sensing in CUAVNs

Support Vector Machine Dynamic Selection of Voting Rule for Cooperative Spectrum Sensing in CUAVNs

Optimization of Cooperative Spectrum Sensing in Multi-hop Heterogeneous Cognitive Radio Networks

Optimization of Cooperative Spectrum Sensing in Multi-hop Heterogeneous Cognitive Radio Networks

Development of a Privacy-Preserved and Secure Cooperative Spectrum Sensing System in Cognitive Radio Networks Using ATSNRNN-Enabled FPPDES With Machine Learning

Development of a Privacy-Preserved and Secure Cooperative Spectrum Sensing System in Cognitive Radio Networks Using ATSNRNN-Enabled FPPDES With Machine Learning

GLRT-based Spectrum Sensing for Non-circular Signals in Cognitive Radios with Multiple Antennas

GLRT-based Spectrum Sensing for Non-circular Signals in Cognitive Radios with Multiple Antennas

Less Sample-Cooperative Spectrum Sensing in the Presence of Large-Scale Byzantine Attack

Less Sample-Cooperative Spectrum Sensing in the Presence of Large-Scale Byzantine Attack