Article
Mathematics
Arun Kumar, J. Venkatesh, Nishant Gaur, Mohammed H. Alsharif, Peerapong Uthansakul, Monthippa Uthansakul
Summary: In this article, a novel cyclostationary spectrum (CS) algorithm for 5G waveforms is proposed. By restricting the computation of cyclostationary characteristics and the signal autocorrelation, the complexity of CS is reduced. The results of the study show that the suggested CS algorithm did a good job of detection and gained 2 dB compared to the conventional standards.
ELECTRONIC RESEARCH ARCHIVE
(2023)
Article
Computer Science, Information Systems
C. A. L. I. N. VLADEANU, A. L. E. X. A. N. D. R. U. MARTIAN, D. I. M. I. T. R. I. E. C. POPESCU
Summary: This paper presents a new algorithm that uses energy detection to sense the spectrum occupancy in cognitive radio systems. The algorithm performs detection in K consecutive sensing time slots around the current slot. Analytical expressions for false alarm probability and correct detection probability are derived based on a specified primary user traffic model. The results are confirmed through numerical simulations, showing good receiver operating characteristic performance for small K values.
Article
Chemistry, Analytical
Dayan Adionel Guimaraes
Summary: Recently, the modified Gini index detector (mGID) has been proposed as a more efficient alternative to the Gini index detector (GID) for data-fusion cooperative spectrum sensing in certain types of channels. The mGID inherits the robustness and simplicity of the GID while significantly reducing the computational cost and latency. The mGID achieves approximately 23.4 times smaller constant factor in time complexity, resulting in a 4% computation time compared to the GID test statistic calculation with no loss in performance.
Article
Engineering, Electrical & Electronic
Dawei Nie, Wenjuan Yu, Qiang Ni, Haris Pervaiz, Geyong Min
Summary: This paper proposes a cluster-based cooperative sensing-after-prediction scheme to simplify the complex physical sensing process and reduce energy consumption and the number of users, through cooperative prediction and optimization.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Murat Torlak, Won Namgoong
Summary: Compressed sensing is considered an effective method for detecting sparse spectrum in cognitive radio systems, offering reduced sensing time and hardware overhead compared to traditional spectrum scanners. While multi-channel spectrum scanners generally outperform CS scanners, they are comparable at high signal-to-noise ratios. The advantage of spectrum scanners lies in their more reliable sensing for each frequency bin.
Article
Engineering, Electrical & Electronic
Felipe G. M. Elias, Evelio M. G. Fernandez
Summary: Closed-form expressions for detection probability, false alarm probability, and energy detector constant threshold are derived using approximations of central and non-central chi-square distributions, showing closer proximity to original functions than expressions used in literature. These novel expressions provide gains up to 6% and 16% in false alarm and miss-detection probabilities, respectively, compared to the Central Limit Theorem approach, and enhance the throughput of cognitive networks by up to 9%. New equations are presented to minimize total error rate for detection threshold and optimal sample size, with analytical results matching simulation results across a wide range of SNR values.
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
(2021)
Article
Engineering, Electrical & Electronic
Srinivas Nallagonda, Ranjeeth Mamidi, Abhijit Bhowmick
Summary: This study introduces a cooperative sensing network with improved energy detection cognitive radio nodes. Mathematical expressions and simulation tests verify the network's performance characteristics in terms of throughput and energy efficiency, comparing different network parameters and decision fusion rules, as well as investigating the impact of diversity techniques and fading severity parameters on performance.
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
(2021)
Article
Telecommunications
Feten Slimeni, Tijeni Delleji, Ala Agrebi, Amine Trabilsi, Noureddine Boulejfen
Summary: This paper discusses a multistep RF detection technique implemented in a software defined radio platform for source localization. The system combines fast energy detection and cyclostationarity feature detection to handle low level signals, providing potential for monitoring a wider area.
TELECOMMUNICATION SYSTEMS
(2023)
Article
Multidisciplinary Sciences
Alaa Rabie Mohamed, Ahmad A. Aziz El-Banna, Hala A. Mansour
Summary: This paper presents a hybrid sensing model for spectrum detection in CR to enhance the sensing efficiency of traditional techniques, showing improved performance compared to conventional methods.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Telecommunications
Anil Kumar Budati, George Ghinea, S. N. V. Ganesh
Summary: Cognitive Radio Network serves as the backbone for 5G cellular networks and UAV user identification at low power levels is a challenging task. Existing methods rely on static or predefined threshold detection, while this paper proposes a novel approach for dynamic threshold estimation.
WIRELESS PERSONAL COMMUNICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Malgorzata Wasilewska, Hanna Bogucka, H. Vincent Poor
Summary: This article explores reliable and secure spectrum sensing in cognitive radio using federated learning. It discusses the motivation, architectures, and algorithms of federated learning in spectrum sensing. It provides an overview of security and privacy threats on these algorithms and presents possible countermeasures. The article also includes illustrative examples and offers design recommendations for future cognitive radios.
IEEE COMMUNICATIONS MAGAZINE
(2023)
Article
Engineering, Electrical & Electronic
Wenjing Zhao, Syed Sajjad Ali, Minglu Jin, Guolong Cui, Nan Zhao, Sang-Jo Yoo
Summary: This paper focuses on the design of optimal or near-optimal detectors using extreme eigenvalues. A general framework involving model-driven and data-driven approaches is introduced. The extreme eigenvalues based likelihood ratio test (LRT) and Naive Bayesian detector are derived via the model-driven and merged approaches, respectively. Two near-optimal detectors called alpha-SMME and alpha-PMME are further designed for practicality. Theoretical performance analysis is provided and optimal weight selection is obtained for the alpha-SMME and alpha-PMME algorithms. Simulation experiments demonstrate the improved performance of the proposed detectors using extreme eigenvalues.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2022)
Article
Telecommunications
Abbass Nasser, Hussein Al Haj Hassan, Ali Mansour, Koffi-Clement Yao, Loutfi Nuaymi
Summary: This paper investigates the impact of deploying Intelligent Reflecting Surface (IRS) on spectrum sensing in cognitive radio networks, considering two different scenarios. The results show that deploying IRS can significantly enhance spectrum sensing performance.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2022)
Article
Computer Science, Information Systems
Liuwen Li, Wei Xie, Xin Zhou
Summary: In this paper, a cooperative spectrum sensing model based on the parallel connection of convolutional neural network (CNN) and long-short-term memory (LSTM) is designed, making full use of the complementary feature extraction capabilities of CNN and LSTM networks. Experimental result shows that the detection performance of the proposed algorithm outperforms the conventional cooperative detection algorithm under low SNR condition.
Article
Computer Science, Artificial Intelligence
Sivagurunathan Paramasivam Thuraipandi, Sathish Kumar Nagarajan
Summary: The spectrum scarcity problem in wireless communication is addressed using cognitive radio network (CRN) and cooperative spectrum sensing (CSS). This study focuses on CSS in the presence of Rayleigh fading and proposes a model with improved detection performance and reduced false positives using hybrid Support Vector Machine (SVM). The proposed model outperforms standard SVM and Artificial Neural Network (ANN) models in terms of false alarm probability, error rate, spectrum utilization, throughput, and accuracy.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Jia Ke, Ying Wang, Mingyue Fan, Xiaojun Chen, Wenlong Zhang, Jianping Gou
Summary: This study integrates the emotional correlation analysis model and Self-organizing Map (SOM) to construct fine-grained user emotion vector based on review text and perform visual cluster analysis, which helps platform merchants quickly mine user clustering and characteristics.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Shi Qiu, Huping Ye, Xiaohan Liao, Benyue Zhang, Miao Zhang, Zimu Zeng
Summary: This paper proposes a multilevel-based algorithm for hyperspectral image interpretation, which achieves semantic segmentation through multidimensional information fusion, and introduces a context interpretation module to improve detection performance.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Jianteng Xu, Qingguo Bai, Zhiwen Li, Lili Zhao
Summary: This study constructs two optimization models for the omnichannel closed-loop supply chain by leveraging the combined power of leader-follower game and mean-variance theories. The focus is on analyzing the performance of manufacturers who distribute products through physical stores. The results show that the risk-averse attitude of the physical store has a positive impact on the overall system profitability, but if the introduced physical store belongs to another firm, total profit experiences a decline.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Jiahao Xiong, Weihua Ou, Zhonghua Liu, Jianping Gou, Wenjun Xiao, Haitao Liu
Summary: This paper proposes a novel remote photoplethysmography framework, named GraphPhys, which utilizes graph neural network to extract physiological signals and introduces Average Relative GraphConv for the task of remote physiological signal measurement. Experimental results show that the methods based on GraphPhys significantly outperform the original methods.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Zhiyao Tong, Yiyi Hu, Chi Jiang, Yin Zhang
Summary: The rise of illicit activities involving blockchain digital currencies has become a growing concern. In order to prevent illegal activities, this study combines financial risk control with machine learning to identify and predict the risks of users with poor credit. Experimental results demonstrate high performance in user financial credit analysis.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)