Article
Computer Science, Information Systems
Santosh Kumar, Awadhesh Kumar Singh
Summary: This article proposes a localized clustering scheme for CRN to improve stability, efficiency, and reduce communication overhead. The scheme achieves this through node weight calculation and cluster head selection.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Abd Ullah Khan, Muhammad Tanveer, Wali Ullah Khan
Summary: Satisfying the QoS requirements of users is a major problem in cognitive radio networks. Existing research studies have limitations in their conceptual and mathematical modeling, leading to unreliable performance evaluation. This paper proposes a more realistic and reliable modeling approach by using connection availability instead of channel availability and considering the accessibility of the intended receiver.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Telecommunications
Linghui Zeng, Jianzhao Zhang
Summary: This article analyzes the spectrum sharing among high-density users in future mobile communication systems and proposes a channel selection strategy based on distributed reinforcement learning, as well as a spectrum sensing skipping scheme based on Bayesian estimation, which can effectively optimize the spectrum utilization efficiency.
TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES
(2021)
Article
Computer Science, Information Systems
Manisha A. Dudhedia, Yerram Ravinder
Summary: A study of existing MAC protocols for Cognitive Radio based Wireless Network (CRWN) reveals that these protocols are designed for optimizing individual node performance rather than the network performance. To address this gap, the framework of existing MAC protocol is modified in this paper to achieve global optimized performance at the network level. A realistic approach based on incomplete information is presented, and a game theoretical framework is applied to improve network performance in terms of channel capacity, throughput, and delay. Simulation results show significant improvements in collision avoidance, delay reduction, energy consumption, and overall network throughput.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Samir Gourdache, Azeddine Bilami, Kamel Barka
Summary: This paper proposes a cognitive-radio-inspired framework for integrating idle spectrum resources of different wireless networks into a single mobile heterogeneous wireless network. The framework combines a generic and cooperative spectrum-harvesting scheme to keep the network supplied with vital spectrum resources. By using cross-correlated sequences (CCSs) for context-aware event signaling, a reporting and detection scheme is presented in the context of OFDMA systems.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Mahmoud Khasawneh, Ahmad Azab, Saed Alrabaee, Heba Sakkal, Hossameldein Hussein Bakhit
Summary: Cognitive Radio Networks (CRNs) are proposed as a solution to spectrum scarcity in wireless communication systems. The integration of CRNs with the IoT has the potential to bring significant benefits to wireless communication systems and IoT applications. Spectrum sensing and routing are key aspects of CRNs integrated with the IoT, and this survey paper explores the recent research and developments in this field, discussing the challenges and future research directions.
Article
Computer Science, Information Systems
Pooja Ahuja, Preeti Sethi, Naresh Chauhan
Summary: Cognitive radio technology provides a solution to spectrum scarcity while introducing new security challenges.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Telecommunications
Ahmed F. Tayel, Ahmed Y. Zakariya, Sherif I. Rabia, Ahmed H. Abd El-Malek
Summary: This research derives discrete energy harvesting models based on practical continuous RF-to-DC power conversion models to address the inconsistency between existing discrete models and actual circuit properties. General closed-form expressions for the probability density functions of harvested energy are derived and applied to specific fading models, with simulation results and statistical analysis conducted to compare the derived models with other discrete models in the literature.
TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES
(2023)
Article
Computer Science, Information Systems
Suoping Li, Qianyu Xu, Jaafar Gaber, Nana Yang
Summary: This paper proposes a polling scheduling strategy with reserved channel for predefined priority services in cognitive radio networks. By dynamically adjusting the assembled channels of secondary users, the strategy improves the performance of the secondary network and enhances the service quality of high-priority services on the basis of providing fair scheduling.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2022)
Article
Computer Science, Information Systems
Babatunde S. Awoyemi, Bodhaswar T. Maharaj
Summary: This paper explores how to achieve target quality of service demands for heterogeneous CRSN under resource constraints, solving the complex resource allocation problem using integer linear programming and other approaches, and analyzing the impact of different solution methods on model performance.
COMPUTER COMMUNICATIONS
(2021)
Article
Physics, Multidisciplinary
Yaoxuan Wang, Xianhua Niu, Chao Qi, Zhihang He, Bosen Zeng
Summary: This paper emphasizes the importance of channel-hopping-based rendezvous in cognitive radio networks and proposes a method for constructing asynchronous channel-hopping sequences using interleaving technique. The new construction exhibits better rendezvous time performance compared to prior research, and can adapt to the demands of different communication scenarios.
Article
Computer Science, Hardware & Architecture
Jun Fang, Bin Wang, Hongbin Li, Ying-Chang Liang
Summary: Cognitive radio technology shows promise in efficiently utilizing spectrum resources for future wireless systems, with advanced wideband spectrum sensing being crucial for operating over a wide frequency range. Recent advances in sub-Nyquist sampling-based WBSS techniques, such as compressive covariance sensing, offer competitive solutions for reliable real-time spectrum sensing.
IEEE WIRELESS COMMUNICATIONS
(2021)
Review
Chemistry, Analytical
Rodrigo Fuchs Miranda, Carlos Henrique Barriquello, Vitalio Alfonso Reguera, Gustavo Weber Denardin, Djeisson Hoffmann Thomas, Felipe Loose, Leonardo Saldanha Amaral
Summary: This paper reviews the latest developments in cognitive hybrid RF-VLC systems and emphasizes the importance of seamless integration of CRSNs and VLC technologies in WSNs. The paper explores the complexity of this integration and highlights the potential of machine learning and deep learning methods in developing advanced cognitive radio strategies.
Article
Nanoscience & Nanotechnology
Kalpana Devi Perumal, E. D. Kanmani Ruby, M. Dhivya, G. Aloy Anuja Mary, V. Kavitha, Umamahesawari Kandasamy
Summary: Spectrum sensing is a crucial technology for detecting relevant signals in the presence of interference, enabling reliable communication in cognitive radio systems. Dynamic spectrum management techniques efficiently allocate channels to an increasing number of users, playing a vital role in cognitive radio systems.
JOURNAL OF NANOMATERIALS
(2022)
Article
Engineering, Electrical & Electronic
Zhiying Wu, Yan Wang, Aibo Zhang, Junlin Xiong, Min Xie
Summary: This paper introduces an event-triggered control method in unmanned aerial vehicle (UAV) systems based on cognitive radio networks, taking into consideration the deception attacks. By proposing a Quality of Service (QoS)-dependent event-triggered scheme (ETS), the system parameters are adjusted to ensure stability and performance.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Yicheng Liao, Yufei Li, Minjie Chen, Lars Nordstrom, Xiongfei Wang, Prateek Mittal, H. Vincent Poor
Summary: Data-driven approaches are promising for addressing modeling issues in modern power electronics-based power systems. This article introduces a deep learning approach using multilayer feedforward neural networks (FNNs) to train the frequency-domain impedance model of power electronic systems. The proposed approaches for FNN design and learning have been proven to be simple, effective, and optimal based on case studies on a power electronic converter, showing potential for future industrial applications.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Keke Ying, Zhen Gao, Sheng Chen, Mingyu Zhou, Dezhi Zheng, Symeon Chatzinotas, Bjorn Ottersten, H. Vincent Poor
Summary: In this paper, we investigate the random access problem in massive multiple-input multiple-output (MIMO)-based low earth orbit (LEO) satellite systems. We propose a training sequence padded multi-carrier system to overcome imperfect synchronization at edge satellite nodes. The orthogonal approximate message passing-multiple measurement vector algorithm is used to estimate delay coefficients and user terminal activity considering the sparsity of terrestrial-satellite links and sporadic traffic feature of IoT terminals.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Emil Bjornson, Yonina C. Eldar, Erik G. Larsson, Angel Lozano, H. Vincent Poor
Summary: Wireless communication technology has made significant progress in the past 25 years, both in terms of societal adoption and technical sophistication. By 1998, mobile phones had become compact and affordable devices that could be widely used in developed and developing countries. The introduction of cellular networks and competition in the telecommunications market led to the popularity of mobile phones as fashion accessories with unique designs. This breakthrough technology allowed people to communicate with each other, rather than being limited to communicating with specific locations.
IEEE SIGNAL PROCESSING MAGAZINE
(2023)
Article
Engineering, Electrical & Electronic
Yongjeong Oh, Namyoon Lee, Yo-Seb Jeon, H. Vincent Poor
Summary: In this paper, a communication-efficient federated learning framework is presented, inspired by quantized compressed sensing. The framework includes gradient compression for wireless devices and gradient reconstruction for a parameter server. By leveraging both dimension reduction and quantization, a higher compression ratio than one-bit gradient compression can be achieved. An approximate minimum mean square error (MMSE) approach for gradient reconstruction using the expectation-maximization generalized-approximate-message-passing (EM-GAMP) algorithm is proposed for accurate aggregation of local gradients from the compressed signals.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Multidisciplinary Sciences
Mansi Sood, Anirudh Sridhar, Rashad Eletreby, Chai Wah Wu, Simon A. Levin, Osman Yagan, H. Vincent Poor
Summary: A key scientific challenge during the outbreak of novel infectious diseases is predicting changes in the epidemic under countermeasures that limit population interaction. Pathogens have the capacity to mutate and new strains can emerge, posing a threat to public health. Different transmission risks in different settings and the emergence of new strains should be considered when evaluating the impact of mitigation measures.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Article
Telecommunications
Nunzio A. Letizia, Andrea M. Tonello, H. Vincent Poor
Summary: In this letter, the problem of determining the capacity of a communication channel is approached as a cooperative game between a generator and a discriminator. Deep learning techniques are used to solve this problem. The generator's task is to produce channel input samples that can be ideally distinguished by the discriminator. The approach, known as cooperative channel capacity learning (CORTICAL), provides both the optimal input signal distribution and the estimated capacity of the channel. Numerical results show that the proposed framework can learn the capacity-achieving input distribution even in non-Shannon settings.
IEEE COMMUNICATIONS LETTERS
(2023)
Article
Telecommunications
Jiancheng An, Chau Yuen, Chongwen Huang, Merouane Debbah, H. Vincent Poor, Lajos Hanzo
Summary: This article introduces the unique properties and open challenges of holographic multiple-input multiple-output (HMIMO) technology, emphasizing the interplay between HMIMO and other emerging technologies in next-generation networks.
IEEE COMMUNICATIONS LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Malong Ke, Zhen Gao, Mingyu Zhou, Dezhi Zheng, Derrick Wing Kwan Ng, H. Vincent Poor
Summary: This paper proposes a unified semi-blind detection framework for sourced and unsourced random access (RA) to achieve ultra-reliable low-latency communications. The active devices transmit their uplink access signals in a grant-free manner, while the base station aims to achieve reliable data detection without explicit channel state information. The proposed framework includes efficient transmitter design and interference cancellation-based detection scheme, as well as other enabling techniques to satisfy the requirements of massive URLLC.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Bo Yang, Xuelin Cao, Jindan Xu, Chongwen Huang, George C. Alexandropoulos, Linglong Dai, Merouane Debbah, H. Vincent Poor, Chau Yuen
Summary: The future 6G wireless networks will integrate communications and computing intelligently to meet various application demands. Reconfigurable intelligent surfaces (RISs), which offer programmable propagation of electromagnetic signals, are a promising technology for realizing smart radio environments. However, conventional RISs' purely reflective nature poses challenges for computation-based applications. New materials, like metamaterials, will be needed to complement existing technologies and enable further electronic diversification and applications.
IEEE WIRELESS COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Chuan Ma, Jun Li, Kang Wei, Bo Liu, Ming Ding, Long Yuan, Zhu Han, H. Vincent Poor
Summary: Motivated by the increasing computational capacity of distributed end-user equipment and concerns about privacy, there has been considerable interest in machine learning and artificial intelligence that can be processed on distributed devices. However, this new paradigm also introduces new risks in terms of privacy and security. In this article, the authors provide a survey of the emerging security and privacy risks of distributed machine learning and discuss defense methods and future research directions.
PROCEEDINGS OF THE IEEE
(2023)
Article
Computer Science, Information Systems
Bing-Jia Chen, Ronald Y. Chang, Feng-Tsun Chien, H. Vincent Poor
Summary: This letter examines a downlink MISO system where a BS sends data to multiple users with the help of RIS and DF relay. The proposed two-phase GNN model learns the joint beamforming strategy by exchanging and updating relevant relational information embedded in the graph representation of the transmission system. The proposed method demonstrates superior performance, robustness, and complexity advantages compared to existing approaches.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2023)
Article
Computer Science, Information Systems
Zhiguo Ding, Robert Schober, H. Vincent Poor
Summary: This letter discusses a legacy massive MIMO network that uses preconfigured spatial beams for near-field users and proposes using NOMA principle to serve additional far-field users. The results show that NOMA can effectively support coexistence between near-field and far-field communications, and that increasing the number of antennas at the base station can improve the performance of NOMA-assisted massive MIMO.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2023)
Article
Computer Science, Theory & Methods
Mansoor Ali, Georges Kaddoum, Wen-Tai Li, Chau Yuen, Muhammad Tariq, H. Vincent Poor
Summary: The rapid growth of electric vehicles has led to the development of more flexible and reliable vehicle-to-grid-enabled cyber-physical systems. However, with the increasing complexity, these systems are at a higher risk of cyber-physical threats. This paper presents a resilient and secure framework for detecting and mitigating coordinated cyber attacks in vehicle-to-grid-enabled cyber-physical systems, leveraging smart digital twin technology.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Theory & Methods
H. D. Tuan, Ali A. A. Nasir, Y. Chen, E. Dutkiewicz, H. V. Poor
Summary: This paper focuses on a network scenario where a multi-antenna access point serves multiple single-antenna users in the presence of multiple eavesdroppers, with the aid of a reconfigurable intelligent surface (RIS). The RIS employs low-resolution programmable reflecting elements (PREs) for cost-effective implementation. In order to establish secure links for all users, we consider the joint design of the transmit beamformers and PREs to maximize either the geometric mean of secrecy rates or the worst user's secrecy rate. Novel computational algorithms of low computational complexity are developed for the solution of these mixed discrete continuous optimization problems. Simulations show the merit of the proposed designs in achieving fair secrecy rate distributions and ensuring secure links for all users.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Theory & Methods
Kang Wei, Jun Li, Chuan Ma, Ming Ding, Wen Chen, Jun Wu, Meixia Tao, H. Vincent Poor
Summary: Personalized federated learning (PFL) generates personalized models for heterogenous clients and improves convergence with few-shot training. This paper proposes a differential privacy (DP) based PFL (DP-PFL) framework and analyzes its convergence performance. The developed convergence bounds reveal optimal model size and tradeoff among communication rounds, convergence performance, and privacy budget.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
