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IEEE Transactions on Cognitive Communications and Networking
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Year: 2022 | Volume: 8 | Issue: 3
IEEE Transactions on Cognitive Communications and Networking Publication Information
Publication Year: 2022,Page(s):C2 - C2
IEEE Transactions on Cognitive Communications and Networking Publication Information
Year: 2022 | Volume: 8 | Issue: 3
Toward the Coexistence of Cognitive Networks for Vehicular Communications on TVWS for IEEE Std. 802.22
Publication Year: 2022,Page(s):1316 - 1331
Cited by: Papers (4)
The IEEE 802.22 is a standard for wireless regional area networks to opportunistically operate in TV bands; however, sharing TV White Space (TVWS) spectrum creates a problem of coexistence because no mechanism is defined for coordinating channel usage among secondary networks. We propose a White Space Resource Sharing mechanism (WSRS) for fixed secondary and mobile IEEE 802.22 networks to improve ...Show More
Spectrum Sharing Among Multiple-Seller and Multiple-Buyer Operators of a Mobile Network: A Stochastic Geometry Approach
Elaheh Ataeebojd;Mehdi Rasti;Hossein Pedram;Pedro H. J. Nardelli
Publication Year: 2022,Page(s):1332 - 1347
Cited by: Papers (2)
Sharing the spectrum among mobile network operators (MNOs) is a promising approach to improve the spectrum utilization and to increase the monetary income of MNOs. In this paper, we model a nonorthogonal spectrum sharing system for a large-scale cellular network where multiple seller MNOs lease their licensed sub-bands to several buyer MNOs. We first analyze the per-user expected rate and the per-...Show More
Signal Modulation Classification Based on the Transformer Network
Publication Year: 2022,Page(s):1348 - 1357
Cited by: Papers (54)
In this work, the Transformer Network (TRN) is applied to the automatic modulation classification (AMC) problem for the first time. Different from the other deep networks, the TRN can incorporate the global information of each sample sequence and exploit the information that is semantically relevant for classification. In order to illustrate the performance of the proposed model, it is compared wi...Show More
A Radio Signal Recognition Approach Based on Complex-Valued CNN and Self-Attention Mechanism
Publication Year: 2022,Page(s):1358 - 1373
Cited by: Papers (27)
Automatic modulation recognition (AMR) of radio signals is becoming increasingly important due to its key role in wireless communication system management, monitoring, and control. In this paper, we propose an end-to-end AMR framework based on deep learning (DL), named CCNN-Atten. First, the complex-valued convolutional neural network (CCNN) extracts the features of the radio signal, and the featu...Show More
NAS-AMR: Neural Architecture Search-Based Automatic Modulation Recognition for Integrated Sensing and Communication Systems
Xixi Zhang;Haitao Zhao;Hongbo Zhu;Bamidele Adebisi;Guan Gui;Haris Gacanin;Fumiyuki Adachi
Publication Year: 2022,Page(s):1374 - 1386
Cited by: Papers (65)
Automatic modulation recognition (AMR) technique plays an important role in the identification of modulation types of unknown signal of integrated sensing and communication (ISAC) systems. Deep neural network (DNN) based AMR is considered as a promising method. Considering the complexity of a typical ISAC system, devising the DNN manually with limited knowledge of its various classifications will ...Show More
Index Modulation Recognition Based on Projection Residual Analysis
Publication Year: 2022,Page(s):1387 - 1398
Cited by: Papers (3)
Index modulation recognition (IMR) at secondary user (SU) receiver is a challenging topic for MIMO-OFDM cognitive radio network (MIMO-OFDM-CRN) with index modulation scheme, in order to make SU preferably adapt to the communication environment by adjusting own parameters. For index modulated signals, this paper proposes an effective IMR algorithm based on projection residual analysis (PRA). The pr...Show More
Detection of Direct Sequence Spread Spectrum Signals Based on Deep Learning
Fei Wei;Shilian Zheng;Xiaoyu Zhou;Luxin Zhang;Caiyi Lou;Zhijin Zhao;Xiaoniu Yang
Publication Year: 2022,Page(s):1399 - 1410
Cited by: Papers (16)
Direct spread spectrum communication has the advantages of strong anti-jamming ability and low probability of interception, which plays an essential role in both civil and military communications. The detection of direct sequence spread spectrum (DSSS) signal becomes very difficult because of its low power spectral density. In this paper, the detection of DSSS signals under non-cooperative conditi...Show More
Sustainability Analysis of Opportunistic CR-IoT Network Employing Microwave Power Transfer
Asif Ahmed Sardar;Dibbendu Roy;Washim Uddin Mondal;Goutam Das
Publication Year: 2022,Page(s):1411 - 1421
Cited by: Papers (1)
In this paper, we analyze a Cognitive Radio-based Internet-of-Things (CR-IoT) network comprising a Primary Network Provider (PNP) and an IoT operator. The IoT operator opportunistically accesses the licensed band to serve its IoT nodes. We employ the Microwave Power Transfer technique to harvest energy at IoT nodes for its superior energy transfer efficiency over long-distance. Due to the imperfec...Show More
Security Outage Probability Analysis of Cognitive Networks With Multiple Eavesdroppers for Industrial Internet of Things
Publication Year: 2022,Page(s):1422 - 1433
Cited by: Papers (8)
The Industrial Internet of Things (IIoT) has been recognised as having the potential to benefit a range of industrial sectors substantially. However, widespread development and deployment of IIoT systems are limited for some reasons, the most significant of which are a shortage of spectrum resources and network security issues. Given the heterogeneity of IIoT devices, typical cryptographic securit...Show More
Outage Analysis in SWIPT Enabled Cooperative AF/DF Relay Assisted Two-Way Spectrum Sharing Communication
Publication Year: 2022,Page(s):1434 - 1443
Cited by: Papers (12)
This paper reports a relative performance analysis of decode-and-forward (DF) and amplify-and-forward (AF) relaying in a multi-antenna cooperative cognitive radio network (CCRN) that supports device-to-device (D2D) communications using spectrum sharing technique in cellular network. The system model considers cellular system as primary users (PUs) while Internet of Things Devices (IoDs), involved ...Show More
Radio Frequency Fingerprinting Improved by Statistical Noise Reduction
Publication Year: 2022,Page(s):1444 - 1452
Cited by: Papers (7)
Radio frequency (RF) fingerprinting as an important non-password authentication technology has received extensive attention in recent years. It can be used as an underlying intrusion detection component for various wireless networks to enhance their wireless security further. In order to achieve more robust RF fingerprinting with deep learning, this paper develops a practical signal data augmentat...Show More
Context-Based Semantic Communication via Dynamic Programming
Yichi Zhang;Haitao Zhao;Jibo Wei;Jiao Zhang;Mark F. Flanagan;Jun Xiong
Publication Year: 2022,Page(s):1453 - 1467
Cited by: Papers (20)
Standard digital communication techniques allow us to set aside the meaning of the messages to concentrate on the transmission of bits efficiently and reliably. However, with the integration of artificial intelligence into communications technology and the merging of communication and computation within devices, increasing evidence suggests that the semantic aspect of communication cannot be set a...Show More
Achieving AI-Enabled Robust End-to-End Quality of Experience Over Backhaul Radio Access Networks
Dibbendu Roy;Aravinda S. Rao;Tansu Alpcan;Goutam Das;Marimuthu Palaniswami
Publication Year: 2022,Page(s):1468 - 1481
Cited by: Papers (1)
Emerging applications such as Augmented Reality, the Internet of Vehicles and Remote Surgery require both computing and networking functions working in harmony. The End-to-end (E2E) quality of experience (QoE) for these applications depends on the synchronous allocation of networking and computing resources. However, the relationship between the resources and the E2E QoE outcomes is typically stoc...Show More
Massive MIMO-Based Underlay Spectrum Access Under Incomplete and/or Imperfect Channel State Information
Publication Year: 2022,Page(s):1482 - 1496
Cited by: Papers (2)
We investigate use of massive number of antennas at cognitive base station (BS) in reducing interference caused to primary users (PUs) under incomplete and/or imperfect channel state information (CSI) without deteriorating sum spectral efficiency of cognitive users (CUs). We derive analytical expressions for complement of interference outage probability with incomplete and/or imperfect CSI. These ...Show More
Intelligent Reflecting Surfaces and Spectrum Sensing for Cognitive Radio Networks
Abbass Nasser;Hussein Al Haj Hassan;Ali Mansour;Koffi-Clement Yao;Loutfi Nuaymi
Publication Year: 2022,Page(s):1497 - 1511
Cited by: Papers (26)
In Cognitive Radio (CR) networks, Primary User (PU) and Secondary User (SU) coexist to efficiently share the spectrum. PU has the right to access its dedicated channel at any time, while SU, operating in an opportunistic mode. can access only when PU is absent. Thus, SU should continuously monitor the channel to avoid any interference with PU when transmitting. Several factors, such as fading and ...Show More
Interference-Aware Spectrum Resource Management in Dynamic Environment: Strategic Learning With Higher-Order Statistic Optimization
Yihang Du;Xiaoqiang Qiao;Yu Zhang;Lei Xue;Tao Zhang;Pengfei Ma;Chun Chen
Publication Year: 2022,Page(s):1512 - 1528
Cited by: Papers (2)
Wireless spectrum environment is complex and uncertain in practical scenarios. For spectrum resource management, there are two crucial challenges to be resolved. First, channel states are always time-varying, thus violent fluctuations of interference links bring difficulties to interference mitigation. Second, the optimization efficiency degrades when jointly considering frequency coordination and...Show More
A Hybrid Architecture for Federated and Centralized Learning
Ahmet M. Elbir;Sinem Coleri;Anastasios K. Papazafeiropoulos;Pandelis Kourtessis;Symeon Chatzinotas
Publication Year: 2022,Page(s):1529 - 1542
Cited by: Papers (32)
Many of the machine learning tasks rely on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) entailing huge communication overhead. To overcome this, federated learning (FL) has been suggested as a promising tool, wherein the clients send only the model updates to the PS instead of the whole dataset. However, FL demands powerfu...Show More
Edge Computing-Aided Coded Vertical Federated Linear Regression
Mingjun Dai;Ziying Zheng;Zhaoyan Hong;Shengli Zhang;Hui Wang
Publication Year: 2022,Page(s):1543 - 1551
Cited by: Papers (5)
For the training process of federated linear regression (FLR), which is the simplest form of federated learning, the integrated computation at each company is slowed down either by huge volume data or by time-consuming homomorphic encryption. Targetted at accelerating the training process of FLR, through the incorporation of edge computing aided coded distributed computing (CDC) into intensive com...Show More
An HTTP Anomaly Detection Architecture Based on the Internet of Intelligence
Yufei An;Ying He;F. Richard Yu;Jianqiang Li;Jianyong Chen;Victor C. M. Leung
Publication Year: 2022,Page(s):1552 - 1565
Cited by: Papers (4)
The prompt expansion of the Internet of Things (IoT) and its wide application in smart homes and transportation has brought tremendous convenience to people’s lives. However, the increase of IoT devices has also brought huge security problems, threatening people’s information and property security. This paper designs a new anomaly detection architecture based on the concept of the “Internet of int...Show More
Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective
Xuezhen Tu;Kun Zhu;Nguyen Cong Luong;Dusit Niyato;Yang Zhang;Juan Li
Publication Year: 2022,Page(s):1566 - 1593
Cited by: Papers (91)
Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners’ raw data. In FL, the data owners can train ML models based on their local data and only send the model updates rather than raw data to the model owner for aggregation. To improve learning performance in terms of model accuracy and training complet...Show More
IEEE Communications Society Information
Year: 2022 | Volume: 8 | Issue: 3
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