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Exclusive-Region-Map-Based Medium Access Control in Mobile Networks With Directional Antennas Through Deep Interference Learning IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-06-09 Zhe Chu, Fei Hu, Jiamiao Zhao, Linsheng He, Elizabeth Serena Bentley, Sunil Kumar
The medium access control (MAC) design in mobile networks with directional antennas is challenging due to the difficulty of defining the exact RF interference range between two neighboring directional links and the frequent changes of interference range due to node mobility. This research targets directional data reception (Rx) and transmission (Tx) coordination issues based on the computation of directional
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Adaptive Modulation for Wireless Federated Edge Learning IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-04-27 Xinyi Xu, Guanding Yu, Shengli Liu
Wireless federated edge learning (FEEL) has been recently proposed to support the mobile artificial intelligence (AI) applications. Instead of transmitting local data to the edge server, the local learning updates or model parameters are uploaded over the wireless channels to protect the data privacy and security. However, the unreliable wireless channel will cause random packet error, which eventually
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BD-SAS: Enabling Dynamic Spectrum Sharing in Low-Trust Environment IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-04-26 Yang Xiao, Shanghao Shi, Wenjing Lou, Chonggang Wang, Xu Li, Ning Zhang, Y. Thomas Hou, Jeffrey H. Reed
The spectrum access system (SAS) designated by the FCC follows a centralized server-client model where a spectrum user registers with one SAS service provider for spectrum allocation and other spectrum management functions. This model, however, is vulnerable to adversarial influence on individual SAS servers, causing concerns over system reliability and trustworthiness, especially when the ecosystem
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Spectrum Sharing Toward Delay Deterministic Wireless Network: Delay Performance Analysis IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-04-26 Zhiqing Wei, Ling Zhang, Gaofeng Nie, Huici Wu, Ning Zhang, Zeyang Meng, Zhiyong Feng
To accommodate Machine-type Communication (MTC) service, the wireless network needs to support low-delay and low-jitter data transmission, realizing delay deterministic wireless network. This paper analyzes the delay and jitter of the wireless network with and without spectrum sharing. When sharing the spectrum of the licensed network, the spectrum band of wireless network can be expanded, such that
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A Channel Allocation Framework Under Responsive Pricing in Heterogeneous Cognitive Radio Network IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-04-26 Min Zhang, Xiaoying Zhu, Shi Wang, Dayan Cao, Linxin Zhang
For a virtual network operator (VNO) of a heterogeneous cognitive radio network (CRN), finding a channel allocation method that can maximize income while guaranteeing the quality of service (QoS) of secondary users (SU) is a complex but crucial problem. Aim to this problem, based on the probability distribution vector (PDV) designed to describe all possible channel allocation results, a queueing analysis
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Physical Layer Security for Wireless-Powered Ambient Backscatter Cooperative Communication Networks IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-04-26 Xingwang Li, Junjie Jiang, Hao Wang, Congzheng Han, Gaojie Chen, Jianhe Du, Chunqiang Hu, Shahid Mumtaz
Low power consumption and high spectrum efficiency as the great challenges for multi-device access to Internet-of-Things (IoT) have put forward stringent requirements on the future intelligent network. Ambient backscatter communication (ABcom) is regarded as a promising technology to cope with the two challenges, where backscatter device (BD) can reflect ambient radio frequency (RF) signals without
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An Optimal Distributed Energy-Efficient Resource Scheduling for D2D-U Enabled Cellular Networks IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-04-26 Jiantao Yuan, Zheyi Wu, Rui Yin, Guangzhe Zhao, Xianfu Chen, Celimuge Wu
Rational sharing of unlicensed spectrum and efficient use of device-to-device (D2D) communication are two effective ways to improve energy efficiency (EE) and spectrum efficiency (SE) in 5G new radio system. In this paper, we extend the D2D technology to unlicensed spectrum and study the EE of user terminals in unlicensed D2D (D2D-U) system. To cope with the inherent distributed structure of the D2D-U
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Maximizing Packets Collection in Wireless Powered IoT Networks With Charge-or-Data Time Slots IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-04-26 Xiaoyu Song, Kwan-Wu Chin
This paper studies data collection in a wireless powered Internet of Things (IoT) network with a hybrid access point (HAP). A fundamental problem at the HAP is to determine the number of time slots over a given planning horizon that is used to charge and collect data from devices. To this end, we outline a mixed integer linear program (MILP) to determine (i) the mode (charge or data) of each slot,
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Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-04-25 Anu Jagannath, Jithin Jagannath
A scalable and computationally efficient framework is designed to fingerprint real-world Bluetooth devices. We propose an embedding-assisted attentional framework (Mbed-ATN) suitable for fingerprinting actual Bluetooth devices. Its generalization capability is analyzed in different settings and the effect of sample length and anti-aliasing decimation is demonstrated. The embedding module serves as
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SWIPT Enabled Cooperative Cognitive Radio Sensor Network With Non-Linear Power Amplifier IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-04-24 Deepak Kumar, Praveen Kumar Singya, Kwonhue Choi, Vimal Bhatia
In this work, performance of a simultaneous wireless information and power transfer (SWIPT) enabled cooperative cognitive radio sensor network (CRSN) is explored over generalized Nakagami- ${m}$ faded channels. A time switching (TS) protocol for SWIPT is used and the impact of TS factor on energy harvesting and information transmission phases, and information processing and broadcasting phases are
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Topology Planning Using Q-Learning for Microwave-Based Wireless Backhaul Networks IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-04-18 Longfei Li, Yongcheng Li, Sanjay Kumar Bose, Gangxiang Shen
Today, a microwave-based solution is the one commonly used for wireless backhaul networks as it has high capacity and can be easily deployed. For such a microwave-based wireless backhaul network, a well-designed topology is important for efficient capacity utilization and high-quality mobile services. In an earlier work, we presented an approach for topology planning for microwave-based wireless backhaul
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Enabling Fully-Decoupled Radio Access With Elastic Resource Allocation IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-04-11 Bo Qian, Ting Ma, Yunting Xu, Jiwei Zhao, Kai Yu, Yuan Wu, Haibo Zhou
Recently, an origin fully-decoupled radio access network (FD-RAN) inspired by neurotransmission has been proposed for B5G/6G mobile communication networks, which achieves the potentials of improving the spectrum efficiency, reducing the energy consumption and meeting personalized user requirement through profound resource cooperation. To explore new radio and networking technologies, in this paper
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A Transformer-Based Contrastive Semi-Supervised Learning Framework for Automatic Modulation Recognition IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-04-05 Weisi Kong, Xun Jiao, Yuhua Xu, Bolin Zhang, Qinghai Yang
The application of deep learning improves the processing speed and the accuracy of automatic modulation recognition (AMR). As a result, it realizes intelligent spectrum management and electronic reconnaissance. However, deep learning-aided AMR usually requires a large number of labeled samples to obtain a reliable neural network model. In practical applications, due to economic costs and privacy constraints
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DQN-ALrM-Based Intelligent Handover Method for Satellite-Ground Integrated Network IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-27 Junyi Yang, Zhenyu Xiao, Huanxi Cui, Jingjing Zhao, Guowei Jiang, Zhu Han
The satellite-ground integrated network (SGIN), consisting of satellites and ground base stations, is regarded as the trend to solve the problem of global coverage. However, highly dynamic scenarios pose great challenges to users’ mobility management, especially handover decisions. With the rapid development of machine learning, reinforcement learning (RL), such as Q-learning and deep Q-network (DQN)
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Blockchain-Empowered Resource Allocation in Multi-UAV-Enabled 5G-RAN: A Multi-Agent Deep Reinforcement Learning Approach IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-27 Abegaz Mohammed Seid, Aiman Erbad, Hayla Nahom Abishu, Abdullatif Albaseer, Mohamed Abdallah, Mohsen Guizani
In 5G and B5G networks, real-time and secure resource allocation with the common telecom infrastructure is challenging. This problem may be more severe when mobile users are growing and connectivity is interrupted by natural disasters or other emergencies. To address the resource allocation problem, the network slicing technique has been applied to assign virtualized resources to multiple network slices
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Federated Online Learning Aided Multi-Objective Proactive Caching in Heterogeneous Edge Networks IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-27 Tan Li, Linqi Song
To address the drastic increase in multimedia traffic volume, mobile edge caching (MEC) has been exploited to reduce redundant data transmissions by equipping computation and storage capacity at the edge network. Previous works on learning-based caching problems often only concern pre-storing popular contents to satisfy users’ demands. In this work, we investigate the cache strategy design problem
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Channel Estimation for Spectrum Sharing in Massive MIMO Communications IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-24 Zahra Pourgharehkhan, Shahram ShahbazPanahi, Majid Bavand, Gary Boudreau
In this paper, we investigate the problem of channel estimation in a multi-user massive multiple-input multiple-output (MIMO) secondary network (SN) aiming to access the licensed spectrum of a multi-user massive MIMO primary network (PN) using the underlay spectrum sharing approach. We estimate the channels of the single-antenna primary users (PUs) and those of the secondary users (SUs) at the secondary
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Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-24 Matteo Zecchin, Sangwoo Park, Osvaldo Simeone, Marios Kountouris, David Gesbert
This work takes a critical look at the application of conventional machine learning methods to wireless communication problems through the lens of reliability and robustness. Deep learning techniques adopt a frequentist framework, and are known to provide poorly calibrated decisions that do not reproduce the true uncertainty caused by limitations in the size of the training data. Bayesian learning
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Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-24 Rajeev Sahay, Minjun Zhang, David J. Love, Christopher G. Brinton
Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable to adversarial attacks. In the context of maMIMO power allocation, adversarial attacks refer to the injection of subtle perturbations into the deep learning model’s input, during inference (i.e., the adversarial perturbation
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Resource Allocation for NOMA-Enabled Cognitive Satellite–UAV–Terrestrial Networks With Imperfect CSI IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-24 Rui Liu, Kefeng Guo, Kang An, Fuhui Zhou, Yongpeng Wu, Yuzhen Huang, Gan Zheng
Recently, non-orthogonal multiple access (NOMA)-enabled cognitive satellite-unmanned aerial vehicle (UAV)-terrestrial networks have attracted extensive attention for the advantages of enhancing spectrum efficiency and coping with the exponential growth of access users. In this paper, we propose a joint subchannel assignment and power allocation algorithm for NOMA-enabled cognitive satellite-UAV-terrestrial
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Deep Stacked Autoencoder-Based Long-Term Spectrum Prediction Using Real-World Data IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-09 Guangliang Pan, Qihui Wu, Guoru Ding, Wei Wang, Jie Li, Fuyuan Xu, Bo Zhou
Spectrum prediction is challenging due to its multi-dimension, complex inherent dependency, and heterogeneity among the spectrum data. In this paper, we first propose a stacked autoencoder (SAE) and bi-directional long short-term memory (Bi-LSTM) based spectrum prediction method (SAEL-SP). Specifically, a SAE is designed to extract the hidden features (semantic coding) of spectrum data in an unsupervised
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Coalition Formation for Outsourced Spectrum Sensing in Cognitive Radio Network IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-08 Asif Ahmed Sardar, Dibbendu Roy, Washim Uddin Mondal, Goutam Das
We inspect an opportunistic Cognitive Radio Network formed between a Primary Network Provider (PNP) and secondary IoT operators. In our system, spectrum-sensing duty is offloaded to a Sensing Agent (SA), which allows the IoT operators to improve data rate and reduce energy consumption otherwise needed for spectrum-sensing. IoT operators pay PNP for accessing licensed spectrum and SA for providing sensing
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Deep-Learning-Based Resource Allocation for Transmit Power Minimization in Uplink NOMA IoT Cellular Networks IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-08 Hyun Jung Park, Hyeon Woong Kim, Sung Ho Chae
For Internet of Things (IoT) networks, it is important to develop energy-efficient communication schemes to extend the operating life of battery-powered IoT devices. Additionally, non-orthogonal multiple access (NOMA) can utilize frequency resources more efficiently than orthogonal multiple access, making it more suitable to support massive connectivity of IoT users. Motivated by these facts, we consider
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Toward the Automatic Modulation Classification With Adaptive Wavelet Network IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-06 Jiawei Zhang, Tiantian Wang, Zhixi Feng, Shuyuan Yang
With the evolutionary development of modern communications technology, automatic modulation classification (AMC) has played an increasing role in the complex wireless communication environment. Existing AMC schemes based on deep learning use a neural network to extract features and calculate feature maps, then feed them into fully connected layers for classification. However, existing schemes still
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Underlay Spectrum Sharing in Massive MIMO Systems IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-06 Rosa Saif, Zahra Pourgharehkhan, Shahram ShahbazPanahi, Majid Bavand, Gary Boudreau
We consider an underlay spectrum sharing scheme, where a multi-user massive MIMO network (referred to as the primary network (PN)) allows another multi-user massive MIMO network (called the secondary network (SN)) to use the spectrum, owned by the PN, to communicate with the SN’s users. For such a scheme, we devise joint power allocation and beamforming schemes at the SN for both conventional time-division
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Deep Reinforcement Learning-Aided Optimization of Multi-Interface Allocation for Short-Packet Communications IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-03-06 Hugo De Oliveira, Megumi Kaneko, Lila Boukhatem, Ellen Hidemi Fukuda
The severe spectrum scarcity and the stringent requirements of Beyond 5G applications call for an integrated use of low frequency Sub-6 GHz and high frequency millimeter Wave bands. Focusing on future Internet of Things (IoT) Short-Packet Communications (SPC), this paper investigates the optimized usage of such diverse wireless interfaces. We propose an unifying framework devoted to SPC that jointly
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Semantics-Native Communication via Contextual Reasoning IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-02-28 Hyowoon Seo, Jihong Park, Mehdi Bennis, Mérouane Debbah
Recently, machine learning (ML) has shown its effectiveness in improving communication efficiency by reinstating the semantics of bits. To understand its underlying principles, we propose a novel stochastic model of semantic communication, dubbed semantics-native communication (SNC). Inspired from human communication, we consider a point-to-point SNC scenario where a speaker has an intention of referring
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Detection of Stealthy Jamming for UAV-Assisted Wireless Communications: An HMM-Based Method IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-02-28 Chen Zhang, Leyi Zhang, Tianqi Mao, Zhenyu Xiao, Zhu Han, Xiang-Gen Xia
Due to the high mobility, low cost and high robustness of line-of-sight (LoS) channels, unmanned aerial vehicles (UAVs) have begun to play an important role in assisting wireless communications. However, the broadcasting nature of wireless communication networks makes the electromagnetic spectrum vulnerable to jamming attacks. To ensure communication security, this paper investigates the jamming detection
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MAESTRO-X: Distributed Orchestration of Rotary-Wing UAV-Relay Swarms IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-02-24 Bharath Keshavamurthy, Matthew A. Bliss, Nicolò Michelusi
This work details a scalable framework to orchestrate a swarm of rotary-wing UAVs serving as cellular relays to facilitate beyond line-of-sight connectivity and traffic offloading for ground users. First, a Multiscale Adaptive Energy-conscious Scheduling and TRajectory Optimization (MAESTRO) framework is developed for a single UAV. Aiming to minimize the time-averaged latency to serve user requests
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Intelligent Analog Beam Selection and Beamspace Channel Tracking in THz Massive MIMO With Lens Antenna Array IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-02-22 Hosein Zarini, Mohammad Robat Mili, Mehdi Rasti, Sergey Andreev, Pedro H. J. Nardelli, Mehdi Bennis
Beamspace multiple-input-multiple-output (MIMO) as a green technology can efficiently substitute for the conventional massive MIMO, provided that the beamspace channel is acquired precisely. The prior efforts in this area of study, especially the learning-driven ones, however, indicate remarkable performance losses owing to a lack of generalization. In this paper, we propose a modified non-linear auto-regressive
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Performance Analysis and Deep Learning Assessment of Full-Duplex Overlay Cognitive Radio NOMA Networks Under Non-Ideal System Imperfections IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-02-20 Chandan Kumar Singh, Prabhat Kumar Upadhyay, Janne J. Lehtomäki
In this paper, we investigate the effectiveness of an overlay cognitive radio (OCR) coupled with non-orthogonal multiple access (NOMA) system using a full-duplex (FD) cooperative spectrum access with a maximal ratio combining (MRC) scheme under the various non-ideal system imperfections. In view of practical realization, we ponder the impact of loop self-interference, transceiver hardware impairments
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Message From the New Editor-in-Chief IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-02-07 Shiwen Mao
As the new Editor-in-Chief (EiC), it is my great honor and pleasure to present you the first 2023 issue of IEEE Transactions on Cognitive Communications and Networking (TCCN), which includes 18 interesting papers reporting the latest advances in spectrum sharing and artificial intelligence (AI) empowered communications and networking. My work just started in January, following Dr. Ying-Chang Liang
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Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-02-10 Shilian Zheng, Xiaoyu Zhou, Luxin Zhang, Peihan Qi, Kunfeng Qiu, Jiawei Zhu, Xiaoniu Yang
Automatic modulation classification (AMC) can generally be divided into knowledge-based methods and data-driven methods. In this paper, we explore combining the knowledge-based method and data-driven technology to take full advantage of both and propose a hybrid knowledge and data-driven deep learning framework (HKDD) for AMC. To make the handcrafted features more discriminative, various traditional
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Collaboration With Cellular Networks for RFI Cancellation at Radio Telescope IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-02-06 Shuvam Chakraborty, Gregory Hellbourg, Maqsood Careem, Dola Saha, Aveek Dutta
The growing need for electromagnetic spectrum to support the next generation (xG) communication networks increasingly generate unwanted radio frequency interference (RFI) in protected bands for radio astronomy. RFI is commonly mitigated at the Radio Telescope without any active collaboration with the interfering sources. In this work, we provide a method of signal characterization and its use in subsequent
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Deep Mobile Path Prediction With Shift-and-Join and Carry-Ahead IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-02-06 Huigyu Yang, Syed M. Raza, Moonseong Kim, Hyunseung Choo
Importance of user mobility has rapidly increased in 5G due to reduced cell sizes, management of Multi-access Edge Computing (MEC), and ultra-low latency services. Reactive nature of existing management systems is a bottleneck, and it can be solved by building proactive systems that exploit temporal characteristics of time-series mobility data to predict long-term user movement (i.e., path). However
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Adversarial Autoencoder Empowered Joint Anomaly Detection and Signal Reconstruction From Sub-Nyquist Samples IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-02-02 Han Zhang, Zihang Song, Jian Yang, Yue Gao
Anomaly detection is an essential part of spectrum monitoring applications. Malicious users and malfunctioning nodes could be identified via anomaly detection methods. Meanwhile, the spectrum bands that would be utilized in future 6G or satellite communication system settings are going to be wider than ever. Acquiring Nyquist sampled data from such a spectrum would require components with a very high
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Channel Estimation for Indoor Massive MIMO Visible Light Communication With Deep Residual Convolutional Blind Denoising Network IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-02-02 Md. Habibur Rahman, Mostafa Zaman Chowdhury, Ida Bagus Krishna Yoga Utama, Yeong Min Jang
Massive multiple-input multiple-output (m-MIMO) visible light communication (VLC) has been considered as one of the promising components in the optical wireless communication system. However, the envisioned benefits may be limited due to the high computational complexity to estimate accurate channel state information (CSI). Besides, several propagation paths in the practical communication channels
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Secrecy Analysis of Underlay CRN in the Presence of Correlated and Imperfect Channel IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-31 Shilpa Thakur, Ajay Singh, Sudhan Majhi
The presence of correlated and imperfect channel state information (CSI) affects the secrecy performance of cognitive radio networks (CRN). In this paper, we propose a physical layer security analysis of underlay CRN in terms of secrecy outage probability (SOP), average secrecy rate (ASR), and amount of secrecy loss (ASL). Closed forms of SOP, ASR, and ASL are provided over imperfect CSI of Rayleigh
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Age of Information for Short-Packet Relay Communications in Cognitive-Radio-Based Internet of Things With Outdated Channel State Information IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-23 Yong Chen, Yueming Cai, Guoru Ding, Baoquan Yu, Chenglong Xu
It is crucial to consider time-critical communications in cognitive-radio-based Internet of Things (CR-IoT), which increasingly rely on the exchange of short-packet information to efficiently monitor and control. Therefore, the age of information (AoI) is exploited as a timeliness metric in this paper to investigate the freshness of received information at the remote server with dual-hop short-packet
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Thompson Sampling-Based Dynamic Spectrum Access in Non-Stationary Environments IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-17 Shuai Ye, Tianyu Wang, Shaowei Wang
In dynamic spectrum access (DSA), unlicensed secondary users (SUs) estimate the idle probabilities of primary channels by using historical sensing results and opportunistically access the channel with the highest idle probability for transmission. Due to the rapid traffic changes and irregular user mobility, primary channels can be highly dynamic and their idle probabilities are generally time-varying
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Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-13 Jiechen Chen, Nicolas Skatchkovsky, Osvaldo Simeone
Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips, when coupled with spike-based sensors, can inherently adapt to the “semantics” of the data distribution by consuming energy only when relevant events are recorded in the timing of spikes and by proving a low-latency response
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Joint Optimization of Video-Based AI Inference Tasks in MEC-Assisted Augmented Reality Systems IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-11 Guangjin Pan, Heng Zhang, Shugong Xu, Shunqing Zhang, Xiaojing Chen
The high computational complexity and energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. However, mobile edge computing (MEC) makes it possible to solve this problem. This paper considers the scene of completing video-based AI inference tasks in the MEC system. We formulate a mixed-integer nonlinear programming problem (MINLP) to
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A Multi-Blockchain Scheme for Distributed Spectrum Sharing in CBRS System IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-10 Zhiyang Cheng, Yifei Liang, Youping Zhao, Shuo Wang, Chen Sun
To overcome the spectrum scarcity issues, the citizens broadband radio service (CBRS) presents a centralized spectrum management solution. The efficiency of spectrum utilization could be further improved by introducing spectrum trading. Blockchain-based spectrum trading has been considered as a decentralized, flexible, and secure approach. However, current studies rarely investigate the interference
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Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-10 Zhenyu Tan, Danyang Wang, Qi Liu, Zan Li, Ning Zhang, Esam Abdel-Raheem
According to the regulations of the hybrid access strategy in cognitive radio network, the secondary user (SU) needs to identify the primary user’s (PU) specific transmit power level to avoid unacceptable interference with the PU. However, the conventional transmit power recognition methods cannot accurately identify the transmit power in conditions with low signal-to-noise ratio, fading channels and
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A Fast Deep Unfolding Learning Framework for Robust MU-MIMO Downlink Precoding IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-10 Jing Xu, Chaohui Kang, Jiang Xue, Yizhai Zhang
This paper reformulates a worst-case sum-rate maximization problem for optimizing robust multi-user multiple-input multiple-output (MU-MIMO) downlink precoding under realistic per-antenna power constraints. We map the fixed number of iterations in the developed mean-square-error uplink-downlink duality iterative algorithm into a layer-wise trainable network to solve it. In contrast to black-box approximation
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Robust Beamformer Design in Active RIS-Assisted Multiuser MIMO Cognitive Radio Networks IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-10 Raviteja Allu, Omid Taghizadeh, Sandeep Kumar Singh, Keshav Singh, Chih-Peng Li
In this work, we address the problem of a joint robust transmission, reflection and reception strategy design at an active reconfigurable intelligent surface (RIS)-assisted underlay multiple input multiple output (MIMO) cognitive radio networks in which a secondary transmitter serves multiple secondary receivers simultaneously. As such, we study the impact of imperfection in channel state information
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MAGNNETO: A Graph Neural Network-Based Multi-Agent System for Traffic Engineering IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-10 Guillermo Bernárdez, José Suárez-Varela, Albert López, Xiang Shi, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio
Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based solutions for Traffic Engineering (TE), which is a fundamental problem in Internet Service Provider (ISP) networks. Nowadays, state-of-the-art TE optimizers rely on traditional optimization techniques, such as Local search,
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Performance of CDRT-Based Underlay Downlink NOMA Network With Combining at the Users IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-09 Anand Jee, Kamal Agrawal, Deepali Johari, Shankar Prakriya
The performance of an underlay coordinated direct and relay transmission (CDRT) based non-orthogonal multiple access (NOMA) network is limited by the random nature of the transmit powers due to the interference temperature limit (ITL) imposed by the primary network. In this paper, we show that combining the signals at both near and far users can significantly enhance throughput. Considering the practical
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Deep Recurrent Reinforcement Learning-Based Distributed Dynamic Spectrum Access in Multichannel Wireless Networks With Imperfect Feedback IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-05 Amandeep Kaur, Jaismin Thakur, Mukul Thakur, Krishan Kumar, Arun Prakash, Rajeev Tripathi
This paper investigates Dynamic Spectrum Access (DSA) paradigm with imperfect feedback for multiuser wireless network. Each user selects an orthogonal channel in particular time slot to transmit packet with a certain transmission probability. In next time slot, the user who has transmitted a packet receives an ACK signal based on local observation. Bearing in mind the dynamic nature of wireless networks
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Modeling and Classifying Error-Correcting Codes With Soft Decision-Based HMMs for Intelligent Wireless Communication IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-05 Sida Li, Yimeng Zhang, Zhiping Huang, Jing Zhou, Yongjie Zhao
The classification of error correcting code (ECC) types is an important task for intelligent wireless communication. Previous solutions to this problem usually ignored the time sequences feature and considered the incoming data as separate code words. In this paper, a novel framework for code type classification is proposed based on the Hidden Markov Model (HMM). The structural differences of different
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Stackelberg Game for Secure CR-NOMA Networks Against Internal Eavesdropper IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-05 Shaima Abidrabbu, Abdelrahman Abushattal, Hüseyin Arslan
The combination of Cognitive Radio with Nonorthogonal Multiple Access (CR-NOMA) has enormous potential interest to improve spectral efficiency and increase system capacity in future wireless communication networks. Hence, according to the broadcast nature of the CR-NOMA wireless channel, handling the confidentiality of information is one of the most important requirements of the network. In this paper
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Efficient Computation Offloading and Resource Allocation Scheme for Opportunistic Access Fog-Cloud Computing Networks IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2023-01-05 Wen-Bin Sun, Jian Xie, Xin Yang, Ling Wang, Wei-Xiao Meng
Fog-cloud computing is one of cores techniques in wireless networks, where fog and cloud nodes provide high-speed and large-scale computing services to mobile users through a collaborative method. For the conventional fog-cloud computing schemes, the computing abilities of nodes and the computation task offloading methods are the main factors affecting the latency and energy consumption. However, when
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Data-Driven Spectrum Partition for Multiplexing URLLC and eMBB IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2022-12-29 Haoran Peng, Li-Chun Wang, Zhuofu Jian
Multiplexing ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) are critical in the next generation mobile network. URLLC requires ultra-high reliability and extremely low latency, whereas eMBB demands high data rates. Thus, the coexistence system of URLLC and eMBB faces the challenge of sharing the spectrum efficiently and effectively. In this study, we comprehensively
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LINA: A Fair Link-Grained Inter-Datacenter Traffic Scheduling Method With Deadline Guarantee IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2022-12-15 Xiaodong Dong
Inter-datacenter networks take the role of connecting edge clusters and datacenters distributed globally. Nowadays, inter-datacenter network topologies are becoming more and more complicated with multiple links on each path, multiple paths between two datacenters, and overlaps between different paths. Hence, a link-grained scheduling method is necessary for making routing decisions and bandwidth allocation
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Use Coupled LSTM Networks to Solve Constrained Optimization Problems IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2022-12-13 Zheyu Chen, Kin K. Leung, Shiqiang Wang, Leandros Tassiulas, Kevin Chan, Don Towsley
Gradient-based iterative algorithms have been widely used to solve optimization problems, including resource sharing and network management. When system parameters change, it requires a new solution independent of the previous parameter settings from the iterative methods. Therefore, we propose a learning approach that can quickly produce optimal solutions over a range of system parameters for constrained
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Learn to Adapt to New Environments From Past Experience and Few Pilot Blocks IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2022-12-13 Ouya Wang, Jiabao Gao, Geoffrey Ye Li
In recent years, deep learning has been widely applied in communications and achieved remarkable performance improvement. Most of the existing works are based on data-driven deep learning, which requires a significant amount of training data for the communication model to adapt to new environments and results in huge computing resources for collecting data and retraining the model. In this paper, we
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Table of Contents IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2022-12-08
Presents the table of contents for this issue of the publication.
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IEEE Communications Society IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2022-12-08
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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IEEE Communications Society Information IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2022-12-08
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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AoI-Based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching IEEE Trans. Cognit. Commun. Netw. (IF 8.6) Pub Date : 2022-12-09 Jianhang Zhu, Rongpeng Li, Guoru Ding, Chan Wang, Jianjun Wu, Zhifeng Zhao, Honggang Zhang
Along with the fast development of network technology and the rapid growth of network equipment, the data throughput is sharply increasing. To handle the problem of backhaul bottleneck in cellular network and satisfy people’s requirements about latency, the network architecture, like the information-centric network (ICN) intends to proactively keep limited popular content at the edge of network based