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Table of Contents IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-06-08
Presents the table of contents for this issue of the publication.
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IEEE Communications Society IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-06-08
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 IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-06-08
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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A Fair and Cooperative MAC Protocol for Heterogeneous Cognitive Radio Enabled Vehicular Ad-Hoc Networks IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-04-19 Jahnvi Tiwari, Arun Prakash, Rajeev Tripathi, Kshirasagar Naik
Advancements in intelligent transportation necessitate a dependable medium access control (MAC) protocol to enable high-priority safety broadcasts. Implementing cognitive radio-based vehicular ad-hoc networks (CR-VANETs) alleviates the significant growth in spectrum demand. The architecture must deploy multiple networks to make CR-VANET viable; however, the MAC design is a substantial challenge. The
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Turbo Detection Aided Autoencoder for Multicarrier Wireless Systems: Integrating Deep Learning Into Channel Coded Systems IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-04-19 Chao Xu, Thien Van Luong, Luping Xiang, Shinya Sugiura, Robert G. Maunder, Lie-Liang Yang, Lajos Hanzo
A variety of deep learning schemes have endeavoured to integrate deep neural networks (DNNs) into channel coded systems by jointly designing DNN and the channel coding scheme in specific channels. However, this leads to limitations concerning the choice of both the channel coding scheme and the channel paramters. We circumvent these impediments and conceive a turbo-style multi-carrier auto-encoder
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Cooperative Task Offloading and Content Delivery for Heterogeneous Demands: A Matching Game-Theoretic Approach IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-04-19 Tao Fang, Dan Wu, Jiaxin Chen, Dianxiong Liu
In the mobile edge computing (MEC) networks, the heterogeneous demands can be met through device-to-device (D2D) technology due to the idle local resources from the neighbors. Although the overload of mobile edge computing servers can be relieved by sharing idle local resources among neighbors, the active impact of the cooperation in the close neighbors is ignored in the most existing works. In this
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Real-Time 3-D MIMO Antenna Tuning With Deep Reinforcement Learning IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-04-14 Yitong Liu, Yubo Shen, Zhe Lyu, Yanping Liang, Wei He, Yuehong Gao, Hongwen Yang, Li Yu
The 3D MIMO in 5G system requires adaptive and real-time adjustment of antenna azimuth angle, downtilt and beam combination according to the user distribution, which is a powerful technique to improve coverage and capacity. However, due to the complicated interactions between cells and complex environments, it is challengeable to jointly tune the antenna of multiple cells. Besides, the user distribution
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Distributed Beamforming Techniques for Cell-Free Wireless Networks Using Deep Reinforcement Learning IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-04-08 Firas Fredj, Yasser Al-Eryani, Setareh Maghsudi, Mohamed Akrout, Ekram Hossain
In a cell-free network, a large number of mobile devices are served simultaneously by several base stations (BSs)/access points(APs) using the same time/frequency resources. However, this creates high signal processing demands (e.g., for beamforming) at the transmitters and receivers. In this work, we develop centralized and distributed deep reinforcement learning (DRL)-based methods to optimize beamforming
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A Novel Average Autoencoder-Based Amplify-and-Forward Relay Networks With Hardware Impairments IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-04-05 Ankit Gupta, Mathini Sellathurai
In this paper, we propose a novel Average autoencoder (AE)-based amplify-and-forward (AF) relay networks impacted by the I/Q imbalance (IQI) and additional hardware impairments (AHI), where the source and destination nodes are equipped with neural network (NN)-based encoder and decoder, while a conventional AF relay node assists the transmission. The average AE employs multiple small NN-based decoders
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Series-Constellation Feature Based Blind Modulation Recognition for Beyond 5G MIMO-OFDM Systems With Channel Fading IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-04-05 Zeliang An, Tianqi Zhang, Ming Shen, Elisabeth De Carvalho, Baoze Ma, Chen Yi, Tiecheng Song
Due to the shortage of radio spectrum in the current 5G and upcoming 6G systems, the cognitive radio (CR) technique is indispensable for spectrum management and can put the unutilized spectrum to good use. As the core technology of CR, blind modulation recognition (BMR) plays a pivotal role in improving spectral efficiency. However, the BMR research on MIMO-OFDM systems still lacks enough attention
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Cooperative Hybrid Nonorthogonal Multiple Access-Based Mobile-Edge Computing in Cognitive Radio Networks IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-04-05 Dawei Wang, Fuhui Zhou, Wensheng Lin, Zhiguo Ding, Naofal Al-Dhahir
In order to efficiently compute the primary data and support the secondary quality-of-service (QoS) requirement, we propose a cooperative hybrid non-orthogonal multiple access (NOMA) scheme for mobile edge computing (MEC) assisted cognitive radio networks. In the proposed scheme, the primary computation task is securely offloaded to the secondary base station, and the hybrid NOMA technique is adopted
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Deep Neural Network: An Alternative to Traditional Channel Estimators in Massive MIMO Systems IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-04-05 Antonio Melgar, Alejandro de la Fuente, Leopoldo Carro-Calvo, Óscar Barquero-Pérez, Eduardo Morgado
Fifth-generation (5G) requires a highly accurate estimate of the channel state information (CSI) to exploit the benefits of massive multiple-input-multiple-output (MaMIMO) systems. 5G systems use pilot sequences to estimate channel behaviour using traditional methods like least squares (LS), or minimum mean square error (MMSE) estimation. However, traditional methods do not always obtain reliable estimations:
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Random Fourier Feature-Based Deep Learning for Wireless Communications IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-04-05 Rangeet Mitra, Georges Kaddoum
Deep-learning (DL) has emerged as a powerful machine-learning technique for several problems encountered in generic wireless communications. Also, random Fourier Features (RFF) based DL has emerged as an attractive solution for several machine-learning problems; yet existing works lack rigorous analytical results to justify the viability of RFF based DL. To address this gap, we analytically quantify
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An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-03-24 Ali Krayani, Atm S. Alam, Lucio Marcenaro, Arumugam Nallanathan, Carlo Regazzoni
In this paper, we propose to introduce an emergent Self-Awareness (SA) module at the physical layer (PHY) in Cognitive Unmanned Aerial Vehicle (UAV) Radios to improve PHY security, especially against jamming attacks. SA is based on learning a hierarchical representation of the radio environment by means of a proposed Hierarchical Dynamic Bayesian Network (HDBN). It is shown how the acquired knowledge
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Energy Efficient Power Control for Cognitive Multibeam-Satellite Terrestrial Networks With Poisson Distributed Users IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-03-24 Yuhan Ruan, Yongzhao Li, Rui Zhang, Lijuan Jiang
With great potential to alleviate spectrum scarcity in the next generation wireless communication, cognitive satellite terrestrial networks (CSTNs) have attracted considerable attention recently. To realize efficient spectrum sharing between satellite and terrestrial networks, in this paper, we propose an energy efficient power control scheme for CSTNs, where a multibeam satellite network acts as the
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Residual-Aided End-to-End Learning of Communication System Without Known Channel IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-03-24 Hao Jiang, Shuangkaisheng Bi, Linglong Dai, Hao Wang, Jiankun Zhang
Leveraging powerful deep learning techniques, the end-to-end (E2E) learning of communication system is able to outperform the classical communication system. Unfortunately, this communication system cannot be trained by deep learning without known channel. To deal with this problem, a generative adversarial network (GAN) based training scheme has been recently proposed to imitate the real channel.
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Multi-Layer Computation Offloading in Distributed Heterogeneous Mobile Edge Computing Networks IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-03-24 Pengfei Wang, Boya Di, Lingyang Song, Nicholas R. Jennings
In this paper, we consider distributed heterogeneous multi-layer mobile edge computing (HetMEC) networks, where resource-poor edge devices (EDs) upload computing tasks for processing to the mobile edge computing (MEC) servers and a cloud center (CC). To reduce total energy consumption, computation offloading and resource allocation are independently performed by each device and each server. However
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Table of Contents IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-03-08
Presents the table of contents for this issue of the publication.
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IEEE Communications Society IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-03-08
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 IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-03-08
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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Multi-Agent Reinforcement Learning for Network Selection and Resource Allocation in Heterogeneous Multi-RAT Networks IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-03-02 Mhd Saria Allahham, Alaa Awad Abdellatif, Naram Mhaisen, Amr Mohamed, Aiman Erbad, Mohsen Guizani
The rapid production of mobile devices along with the wireless applications boom is continuing to evolve daily. This motivates the exploitation of wireless spectrum using multiple Radio Access Technologies (multi-RAT) and developing innovative network selection techniques to cope with such intensive demand while improving Quality of Service (QoS). Thus, we propose a distributed framework for dynamic
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Machine Learning in NextG Networks via Generative Adversarial Networks IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-02-23 Ender Ayanoglu, Kemal Davaslioglu, Yalin E. Sagduyu
Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mitigation of anomalous behavior. In this paper, we investigate their use in next-generation (NextG) communications within the context of cognitive networks to address i) spectrum sharing, ii) detecting anomalies, and iii)
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New Asynchronous Channel-Hopping Sequences for Cognitive-Radio Wireless Networks IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-02-23 Li-Hsin Yang, Guu-Chang Yang, Wing C. Kwong
In this paper, one family of identification-(ID)-based and one family of non-ID-based asynchronous channel-hopping (CH) sequences are constructed for supporting cognitive-radio wireless networks (CRWNs) in a homogeneous channel setting. The novelty lies in the use of two-dimensional (2-D) algebraic algorithms to properly arrange the linear- and quadratic-congruence sequences in the prime field; channel
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A Data-Driven Self-Optimization Solution for Inter-Frequency Mobility Parameters in Emerging Networks IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-02-18 Muhammad Umar Bin Farooq, Marvin Manalastas, Waseem Raza, Syed Muhammad Asad Zaidi, Ali Rizwan, Adnan Abu-Dayya, Ali Imran
Densification and multi-band operation means inter-frequency handovers can become a bottleneck for mobile user experience in emerging cellular networks. The challenge is aggravated by the fact that there does not exist a method to optimize key inter-frequency handover parameters namely A5 time-to-trigger, A5-threshold1 and A5-threshold2. This paper presents a first study to analyze and optimize the
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A Potential Game Approach for Decentralized Resource Coordination in Coexisting IWNs IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-02-18 Jialin Zhang, Wei Liang, Bo Yang, Huaguang Shi, Ke Wang, Qi Wang
To meet the requirements of various emerging manufacturing applications, multiple Industrial Wireless Networks (IWNs) are employed to operate in the same region. However, the limited communication resources inevitably incur interference in the time and frequency domains, which is known as the coexistence problem. Existing centralized mechanisms suffer from a low computational efficiency in a large-scale
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Three-Dimensional Resource Matching for Internet of Things Underlaying Cognitive Capacity Harvesting Networks IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-02-16 Baoshan Lu, Shijun Lin, Jianghong Shi
In this paper, we propose a cognitive capacity harvesting network (CCHN) based Internet of Things (IoT) architecture, which allows the lightweight IoT devices without spectrum monitoring/sensing capabilities to enjoy the benefits of cognitive radio networks (CRNs). We investigate the sum-rate maximization of IoT links in this proposed architecture. In particular, we formulate the considered problem
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OFDM-Guided Deep Joint Source Channel Coding for Wireless Multipath Fading Channels IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-02-16 Mingyu Yang, Chenghong Bian, Hun-Seok Kim
We investigate joint source channel coding (JSCC) for wireless image transmission over multipath fading channels. Inspired by recent works on deep learning based JSCC and model-based learning methods, we combine an autoencoder with orthogonal frequency division multiplexing (OFDM) to cope with multipath fading. The proposed encoder and decoder use convolutional neural networks (CNNs) and directly map
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Low-Bitwidth Convolutional Neural Networks for Wireless Interference Identification IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-02-04 Pengyu Wang, Yufan Cheng, Qihang Peng, Binhong Dong, Shaoqian Li
Wireless interference identification (WII) is critical for non-cooperative communication systems in both civilian and military scenarios. Recently, deep learning (DL) based WII methods have been proposed with impressive performances. However, these methods did not consider the quantization problem for DL based methods of WII, which is an indispensable process when deploying deep neural networks into
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Multiuser Scheduling in Centralized Cognitive Radio Networks: A Multi-Armed Bandit Approach IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-02-04 Amir Alipour-Fanid, Monireh Dabaghchian, Raman Arora, Kai Zeng
In wireless communication networks, the network provider serves certain licensed primary users who pay for a dedicated use of the frequency channels. However, not all the channels are occupied by the primary users at all times. For efficient spectrum utilization, in centralized cognitive radio networks (CRNs), a cognitive base station (CBS) dynamically identifies the spectrum holes and allocates the
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Delay Optimization in Mobile Edge Computing: Cognitive UAV-Assisted eMBB and mMTC Services IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-02-04 Saifur Rahman Sabuj, Derek Kwaku Pobi Asiedu, Kyoung-Jae Lee, Han-Shin Jo
Mobile edge computing (MEC) in cognitive radio networks is an optimistic technique for improving the computational capability and spectrum utilization efficiency. In this study, we developed an MEC system assisted by a cognitive unmanned aerial vehicle (CUAV), where a CUAV equipped with an MEC server can serve as a relay node and computing node. In such networks, a non-orthogonal multiple access scheme
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Applications of Auction and Mechanism Design in Edge Computing: A Survey IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-28 Houming Qiu, Kun Zhu, Nguyen Cong Luong, Changyan Yi, Dusit Niyato, Dong In Kim
Edge computing as a promising technology provides lower latency, more efficient transmission, and faster speed of data processing since the edge servers are closer to the user devices. Each edge server with limited resources can offload latency-sensitive and computation-intensive tasks from nearby user devices. However, edge computing faces challenges such as resource allocation, energy consumption
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Cognitive Resource Analyzer for Cellular Network Ecosystems IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-28 Brian W. Stevens, Jacob Ray, Eric Daugherty, Jared S. Everett, Yongwen Yang, Mohamed F. Younis
Modern cellular infrastructure has grown in complexity and become an ecosystem that includes the host technologies, such as 4G Long Term Evolution (LTE) and 5G New Radio (NR), along with subsystems such as narrowband Internet of Things (NB-IoT), category M1 (Cat-M1/LTE-M), observed time difference of arrival (OTDOA) and support for 4G/5G coexistence using dynamic spectrum sharing (DSS). Such an increased
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Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-28 B. R. Manoj, Meysam Sadeghi, Erik G. Larsson
The successful emergence of deep learning (DL) in wireless system applications has raised concerns about new security-related challenges. One such security challenge is adversarial attacks. Although there has been much work demonstrating the susceptibility of DL-based classification tasks to adversarial attacks, regression-based problems in the context of a wireless system have not been studied so
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Enabling Cooperative Relay Selection by Transfer Learning for the Industrial Internet of Things IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-28 Sina Shaham, Shuping Dang, Miaowen Wen, Shahid Mumtaz, Varun G. Menon, Chengzhong Li
Large manufacturing sites with movable obstacles and dynamic network topology call for reliable and efficient strategies to transmit data through the industrial Internet of Things (IoT). Cooperative communications and relay selection have shown a great potential to improve throughput and energy efficiency at the expanse of high end-to-end transmission latency. To reduce this latency, we propose to
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Deep Reinforcement Learning Optimal Transmission Algorithm for Cognitive Internet of Things With RF Energy Harvesting IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-13 Shaoai Guo, Xiaohui Zhao
Spectrum scarcity and energy limitation are becoming two critical issues in designing Internet of Things (IoT). As two promising technologies, cognitive radio (CR) and radio frequency (RF) energy harvesting can be used together to improve both energy and spectral efficiency. In this paper, an optimal transmission problem in a cognitive IoT (CIoT) with RF energy harvesting capability is investigated
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Performance of Adaptive Multi-User Underlay NOMA Transmission With Simple User Selection IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-13 Anand Jee, Komal Janghel, Shankar Prakriya
In this paper, we demonstrate that despite the power constraints on all transmit nodes caused by the interference temperature limit (ITL) imposed by the primary user, power domain cooperative non-orthogonal multiple access (NOMA) can ensure much higher throughput in underlay cognitive radio networks than conventional orthogonal multiple access (OMA), provided the network is adaptive. We use intelligent
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Three-Dimensional RF Sensor Networks for Widespread Spectrum Monitoring IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-06 Nikolaus Kleber, Jonathan D. Chisum, Bertrand Hochwald, J. Nicholas Laneman
Spectrum monitoring could improve spectral management and efficiency by enabling spectrum sharing, strengthening policy enforcement, and facilitating data-driven modeling of RF environments. This paper considers spectrum monitoring sensor networks in three dimensions to extend previous work on two-dimensional models. We derive a closed-form expression for the probability of emitter detection, which
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Federated Learning Over Noisy Channels: Convergence Analysis and Design Examples IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-06 Xizixiang Wei, Cong Shen
Does Federated Learning (FL) work when both uplink and downlink communications have errors? How much communication noise can FL handle and what is its impact on the learning performance? This work is devoted to answering these practically important questions by explicitly incorporating both uplink and downlink noisy channels in the FL pipeline. We present several novel convergence analyses of FL over
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Sustainability Analysis of Opportunistic CR-IoT Network Employing Microwave Power Transfer IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Asif Ahmed Sardar,Dibbendu Roy,Washim Uddin Mondal,Goutam Das
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Security Outage Probability Analysis of Cognitive Networks with Multiple Eavesdroppers for Industrial Internet of Things IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Meiling Li,Hu Yuan,Carsten Maple,Ying Li,Osama Alluhaibi
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A Radio Signal Recognition Approach Based on Complex-Valued CNN and Self-Attention Mechanism IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Zhi Liang,Mingliang Tao,Jian Xie,Xin Yang,Ling Wang
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Achieving AI-enabled Robust End-to-End Quality of Experience over Backhaul Radio Access Networks IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Dibbendu Roy,Aravinda S. Rao,Tansu Alpcan,Goutam Das,Marimuthu Palaniswami
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Interference-aware spectrum resource management in dynamic environment: strategic learning with higher-order statistic optimization IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Yihang Du,Xiaoqiang Qiao,Yu Zhang,Lei Xue,Tao Zhang,Pengfei Ma,Chun Chen
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Intelligent Reflecting Surfaces and Spectrum Sensing for Cognitive Radio Networks IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 A. Nasser,H. Al Haj Hassan,A. Mansour,K. C. Yao,L. Nuaymi
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Outage Analysis in SWIPT Enabled Cooperative AF/DF Relay Assisted Two-Way Spectrum Sharing Communication IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Sutanu Ghosh,Tamaghna Acharya,Santi P. Maity
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Context-Based Semantic Communication via Dynamic Programming IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Yichi Zhang,Haitao Zhao,Jibo Wei,Jiao Zhang,Mark F. Flanagan,Jun Xiong
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Toward the Coexistence of Cognitive Networks for Vehicular Communications on TVWS for IEEE Std. 802.22 IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Adriana Arteaga,Sandra Cespedes,Cesar A. Azurdia-Meza
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Massive MIMO Based Underlay Spectrum Access Under Incomplete And/Or Imperfect Channel State Information IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 E. Venkata Pothan,Salil Kashyap
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Radio Frequency Fingerprinting Improved by Statistical Noise Reduction IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Weidong Wang,Lu Gan
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NAS-AMR: Neural Architecture Search Based Automatic Modulation Recognition for Integrated Sensing and Communication Systems IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Xixi Zhang,Haitao Zhao,Hongbo Zhu,Bamidele Adebisi,Guan Gui,Haris Gacanin,Fumiyuki Adachi
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Edge Computing Aided Coded Vertical Federated Linear Regression IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Mingjun Dai,Ziying Zheng,Zhaoyan Hong,Shengli Zhang,Hui Wang
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Detection of Direct Sequence Spread Spectrum Signals Based on Deep Learning IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Fei Wei,Shilian Zheng,Xiaoyu Zhou,Luxin Zhang,Caiyi Lou,Zhijin Zhao,Xiaoniu Yang
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Index Modulation Recognition Based on Projection Residual Analysis IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2022-01-01 Zixuan Zhang,Fulai Liu,Juan Sheng,Caimei Huang,Baozhu Shi
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Dynamic Topology Design of NFV-Enabled Services Using Deep Reinforcement Learning IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2021-12-31 Omar Alhussein, Weihua Zhuang
Next-generation networks are endowed with enhanced capabilities thanks to software-defined networking and network function virtualization (NFV). There is a radical shift from device-centric to experience-driven environments of which data is the primary driver behind its running engines. In this paper, we consider joint topology design, traffic routing and NF placement for unicast NFV-enabled services
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Spatio-Temporal Spectrum Load Prediction Using Convolutional Neural Network and ResNet IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2021-12-29 Xiangyu Ren, Hamed Mosavat-Jahromi, Lin Cai, David Kidston
Radio spectrum is a limited and increasingly scarce resource, which motivates alternative usage methods such as dynamic spectrum allocation (DSA). However, DSA requires an accurate prediction of spectrum usage in both time and spatial domains with minimal sensing cost. In this paper, we propose NN-ResNet prediction model to address this challenge in two steps. First, in order to make the best use of
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Deep Learning Aided Physical-Layer Security: The Security Versus Reliability Trade-Off IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2021-12-27 Tiep M. Hoang, Dong Liu, Thien Van Luong, Jiankang Zhang, Lajos Hanzo
This paper considers a communication system whose source can learn from channel-related data, thereby making a suitable choice of system parameters for security improvement. The security of the communication system is optimized using deep neural networks (DNNs). More explicitly, the associated security vs reliability trade-off problem is characterized in terms of the symbol error probabilities and
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Evolutionary Optimization of Residual Neural Network Architectures for Modulation Classification IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2021-12-23 Erma Perenda, Sreeraj Rajendran, Gerome Bovet, Sofie Pollin, Mariya Zheleva
Automatic modulation classification receives significant interest in the context of current and future wireless communication systems. Deep learning emerged as a powerful tool for modulation classification, as it allows for joint discriminative features learning and signal classification. However, the optimization of deep neural network architectures for modulation classification is a manual and time-consuming
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Cognitive Radio Timing Protocol for Interference-Constrained Throughput Maximization IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2021-12-22 Arifa Ahmed, Deepak Mishra, Ganesh Prasad, Krishna Lal Baishnab
In this paper, we propose a cognitive radio timing protocol (CRTP) to optimize the time allocation for different phases of cognitive radio (CR) operation to maximize the throughput of the secondary user (SU) while satisfying the interference constraint of the primary user (PU). To investigate it, first, we present a three-phase transmission approach for a multi-antenna SU, where, in the first phase
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Intelligent Joint Network Slicing and Routing via GCN-Powered Multi-Task Deep Reinforcement Learning IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2021-12-17 Tianjian Dong, Zirui Zhuang, Qi Qi, Jingyu Wang, Haifeng Sun, F. Richard Yu, Tao Sun, Cheng Zhou, Jianxin Liao
In 6G mobile systems, network slicing is an emerging technology to support services with distinct requirements by dividing a common infrastructure into multiple logical networks. However, as a network management method, it is difficult for network slicing to achieve a real-time resource allocation to satisfy the stringent requirement of services in 6G networks. This paper introduces a joint network
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Table of contents IEEE Trans. Cognit. Commun. Netw. (IF 6.359) Pub Date : 2021-12-08
Presents the table of contents for this issue of this publication.