IEEE Transactions on Machine Learning in Communications and Networking最新文献_第10页

Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming 学习大规模多输入多输出波束成形的高能效发射机配置

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-27 DOI: 10.1109/TMLCN.2024.3419728

Hamed Hojatian;Zoubeir Mlika;Jérémy Nadal;Jean-François Frigon;François Leduc-Primeau

{"title":"Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming","authors":"Hamed Hojatian;Zoubeir Mlika;Jérémy Nadal;Jean-François Frigon;François Leduc-Primeau","doi":"10.1109/TMLCN.2024.3419728","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3419728","url":null,"abstract":"Hybrid beamforming (HBF) and antenna selection are promising techniques for improving the energy efficiency (EE) of massive multiple-input multiple-output (mMIMO) systems. However, the transmitter architecture may contain several parameters that need to be optimized, such as the power allocated to the antennas and the connections between the antennas and the radio frequency chains. Therefore, finding the optimal transmitter architecture requires solving a non-convex mixed integer problem in a large search space. In this paper, we consider the problem of maximizing the EE of fully digital precoder (FDP) and HBF transmitters. First, we propose an energy model for different beamforming structures. Then, based on the proposed energy model, we develop a self-supervised learning (SSL) method to maximize the EE by designing the transmitter configuration for FDP and HBF. The proposed deep neural networks can provide different trade-offs between spectral efficiency and energy consumption while adapting to different numbers of active users. Finally, towards obtaining a system that can be trained using in-the-field measurements, we investigate the ability of the model to be trained exclusively using imperfect channel state information (CSI), both for the input to the deep learning model and for the calculation of the loss function. Simulation results show that the proposed solutions can outperform conventional methods in terms of EE while being trained with imperfect CSI. Furthermore, we show that the proposed solutions are less complex and more robust to noise than conventional methods.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"939-955"},"PeriodicalIF":0.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10574840","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141618104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GenAI-Based Models for NGSO Satellites Interference Detection 基于 GenAI 的 NGSO 卫星干扰探测模型

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-25 DOI: 10.1109/TMLCN.2024.3418933

Almoatssimbillah Saifaldawla;Flor Ortiz;Eva Lagunas;Abuzar B. M. Adam;Symeon Chatzinotas

{"title":"GenAI-Based Models for NGSO Satellites Interference Detection","authors":"Almoatssimbillah Saifaldawla;Flor Ortiz;Eva Lagunas;Abuzar B. M. Adam;Symeon Chatzinotas","doi":"10.1109/TMLCN.2024.3418933","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3418933","url":null,"abstract":"Recent advancements in satellite communications have highlighted the challenge of interference detection, especially with the new generation of non-geostationary orbit satellites (NGSOs) that share the same frequency bands as legacy geostationary orbit satellites (GSOs). Despite existing radio regulations during the filing stage, this heightened congestion in the spectrum is likely to lead to instances of interference during real-time operations. This paper addresses the NGSO-to-GSO interference problem by proposing advanced artificial intelligence (AI) models to detect interference events. In particular, we focus on the downlink interference case, where signals from low-Earth orbit satellites (LEOs) potentially impact the signals received at the GSO ground stations (GGSs). In addition to the widely used autoencoder-based models (AEs), we design, develop, and train two generative AI-based models (GenAI), which are a variational autoencoder (VAE) and a transformer-based interference detector (TrID). These models generate samples of the expected GSO signal, whose error with respect to the input signal is used to flag interference. Actual satellite positions, trajectories, and realistic system parameters are used to emulate the interference scenarios and validate the proposed models. Numerical evaluation reveals that the models exhibit higher accuracy for detecting interference in the time-domain signal representations compared to the frequency-domain representations. Furthermore, the results demonstrate that TrID significantly outperforms the other models as well as the traditional energy detector (ED) approach, showing an increase of up to 31.23% in interference detection accuracy, offering an innovative and efficient solution to a pressing challenge in satellite communications.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"904-924"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10570488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incremental Adversarial Learning for Polymorphic Attack Detection 多态攻击检测的增量对抗学习

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-24 DOI: 10.1109/TMLCN.2024.3418756

Ulya Sabeel;Shahram Shah Heydari;Khalil El-Khatib;Khalid Elgazzar

{"title":"Incremental Adversarial Learning for Polymorphic Attack Detection","authors":"Ulya Sabeel;Shahram Shah Heydari;Khalil El-Khatib;Khalid Elgazzar","doi":"10.1109/TMLCN.2024.3418756","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3418756","url":null,"abstract":"AI-based Network Intrusion Detection Systems (NIDS) provide effective mechanisms for cybersecurity analysts to gain insights and thwart several network attacks. Although current IDS can identify known/typical attacks with high accuracy, current research shows that such systems perform poorly when facing atypical and dynamically changing (polymorphic) attacks. In this paper, we focus on improving detection capability of the IDS for atypical and polymorphic network attacks. Our system generates adversarial polymorphic attacks against the IDS to examine its performance and incrementally retrains it to strengthen its detection of new attacks, specifically for minority attack samples in the input data. The employed attack quality analysis ensures that the adversarial atypical/polymorphic attacks generated through our system resemble original network attacks. We showcase the high performance of the IDS that we have proposed by training it using the CICIDS2017 and CICIoT2023 benchmark datasets and evaluating its performance against several atypical/polymorphic attack flows. The results indicate that the proposed technique, through adaptive training, learns the pattern of dynamically changing atypical/polymorphic attacks, identifies such attacks with approximately 90% balanced accuracy for most of the cases, and surpasses various state-of-the-art detection and class balancing techniques.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"869-887"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10570491","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Machine Learning Aided Reference-Tone-Based Phase Noise Correction Framework for Fiber-Wireless Systems 基于参考音的机器学习辅助光纤无线系统相位噪声校正框架

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-24 DOI: 10.1109/TMLCN.2024.3418748

Guo Hao Thng;Said Mikki

{"title":"A Machine Learning Aided Reference-Tone-Based Phase Noise Correction Framework for Fiber-Wireless Systems","authors":"Guo Hao Thng;Said Mikki","doi":"10.1109/TMLCN.2024.3418748","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3418748","url":null,"abstract":"In recent years, the research involving the use of machine learning in the field of communication networks have shown promising results, in particular, improving receiver sensitivity against noise and link impairment. The proposal of analog radio-over-fiber fronthaul solutions simplifies the overall base station configuration by generating wireless signals at the desired transmission frequency, directly after photodiode heterodyne detection, without requiring additional frequency upconversion components. However, analog radio-over-fiber signals is more susceptible to nonlinear distortions originating from the optical transmission system. This paper explores the use of machine learning in an analog radio-over-fiber link, improving receiver sensitivity in the presence of phase noise. The machine learning algorithm is implemented at the receiver. To evaluate the feasibility of the proposed machine learning based phase noise correction approach, software simulations were conducted to collect data needed for machine leanring algorithm training. Initial findings suggests that the proposed machine-learning-based receiver’s can perform close to conventional heterodyned-based receivers in terms of detection accuracy, exhibiting great tolerance against phase-induced noise, with a symbol error rate improvement from \u0000<inline-formula> <tex-math>$10^{-2}$ </tex-math></inline-formula>\u0000 to \u0000<inline-formula> <tex-math>$10^{-5}$ </tex-math></inline-formula>\u0000, using a relatively simple machine learning algorithm with only 3 hidden layers consisting of fully connected feedforward neural networks.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"888-903"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10570478","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods 利用深度学习方法进行物联网设备物理层欺骗检测和认证

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-21 DOI: 10.1109/TMLCN.2024.3417806

Da Huang;Akram Al-Hourani

{"title":"Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods","authors":"Da Huang;Akram Al-Hourani","doi":"10.1109/TMLCN.2024.3417806","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3417806","url":null,"abstract":"The proliferation of the Internet of Things (IoT) has created significant opportunities for future telecommunications. A popular category of IoT devices is oriented toward low-cost and low-power applications. However, certain aspects of such category, including the authentication process, remain inadequately investigated against cyber vulnerabilities. This is caused by the inherent trade-off between device complexity and security rigor. In this work, we propose an authentication method based on radio frequency fingerprinting (RFF) using deep learning. This method can be implemented on the base station side without increasing the complexity of the IoT devices. Specifically, we propose four representation modalities based on continuous wavelet transform (CWT) to exploit tempo-spectral radio fingerprints. Accordingly, we utilize the generative adversarial network (GAN) and convolutional neural network (CNN) for spoof detection and authentication. For empirical validation, we consider the widely popular LoRa system with a focus on the preamble of the radio frame. The presented experimental test involves 20 off-the-shelf LoRa modules to demonstrate the feasibility of the proposed approach, showing reliable detection results of spoofing devices and high-level accuracy in authentication of 92.4%.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"841-854"},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10568158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141474855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Game Strategies for Data Transfer Infrastructures Against ML-Profile Exploits 数据传输基础设施与 ML-Profile漏洞的博弈策略

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-21 DOI: 10.1109/TMLCN.2024.3417889

Nageswara S. V. Rao;Chris Y. T. Ma;Fei He

{"title":"Game Strategies for Data Transfer Infrastructures Against ML-Profile Exploits","authors":"Nageswara S. V. Rao;Chris Y. T. Ma;Fei He","doi":"10.1109/TMLCN.2024.3417889","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3417889","url":null,"abstract":"Data transfer infrastructures composed of Data Transfer Nodes (DTN) are critical to meeting distributed computing and storage demands of clouds, data repositories, and complexes of supercomputers and instruments. The infrastructure’s throughput profile, estimated as a function of the connection round trip time using Machine Learning (ML) methods, is an indicator of its operational state, and has been utilized for monitoring, diagnosis and optimization purposes. We show that the inherent statistical variations and precision of throughput profiles estimated by ML methods can be exploited for unauthorized use of DTNs’ computing and network capacity. We present a game theoretic formulation that captures the cost-benefit trade-offs between an attacker that attempts to hide under the profile’s statistical variations and a provider that attempts to balance compromise detection with the cost of throughput measurements. The Nash equilibrium conditions adapted to this game provide qualitative insights and bounds for the success probabilities of the attacker and provider, by utilizing the generalization equation of ML-estimate. We present experimental results that illustrate this game wherein a significant portion of DTN computing capacity is compromised without being detected by an attacker that exploits the ML estimate properties.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"925-938"},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10568232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141539198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reservoir Computing-Based Digital Self-Interference Cancellation for In-Band Full-Duplex Radios 基于储层计算的带内全双工无线电数字自干扰消除技术

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-13 DOI: 10.1109/TMLCN.2024.3414296

Zhikai Liu;Haifeng Luo;Tharmalingam Ratnarajah

{"title":"Reservoir Computing-Based Digital Self-Interference Cancellation for In-Band Full-Duplex Radios","authors":"Zhikai Liu;Haifeng Luo;Tharmalingam Ratnarajah","doi":"10.1109/TMLCN.2024.3414296","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3414296","url":null,"abstract":"Digital self-interference cancellation (DSIC) has become a pivotal strategy for implementing in-band full-duplex (IBFD) radios to overcome the hurdles posed by residual self-interference that persist after propagation and analog domain cancellation. This work proposes a novel reservoir computing-based DSIC (RC-DSIC) technique and compares it with traditional polynomial-based (PL-DSIC) and various existing neural network-based (NN-DSIC) approaches. We begin by delineating the structure of the RC and exploring its capability to address the DSIC task, highlighting its potential advantages over current methodologies. Subsequently, we examine the computational complexity of these approaches and undertake extensive simulations to compare the proposed RC-DSIC approach against PL-DSIC and existing NN-DSIC schemes. Our results reveal that the RC-DSIC scheme attains 99.84% of the performance offered by PL-based DSIC algorithms while requiring only 1.51% of the computational demand. Compared to many existing NN-DSIC schemes, the RC-DSIC method achieves at least 99.73% of its performance with no more than 36.61% of the computational demand. This performance justifies the viability of RC-DSIC as an effective and efficient solution for DSIC in IBFD, striking it is a better implementation method in terms of computational simplicity.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"855-868"},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10556632","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141474856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Conditional Generative Adversarial Networks for Efficient Channel Estimation in AmBC Systems 用于 AmBC 系统高效信道估计的深度条件生成对抗网络

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-12 DOI: 10.1109/TMLCN.2024.3413669

Shayan Zargari;Chintha Tellambura;Amine Maaref;Geoffrey Ye Li

{"title":"Deep Conditional Generative Adversarial Networks for Efficient Channel Estimation in AmBC Systems","authors":"Shayan Zargari;Chintha Tellambura;Amine Maaref;Geoffrey Ye Li","doi":"10.1109/TMLCN.2024.3413669","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3413669","url":null,"abstract":"In ambient backscatter communication (AmBC), battery-free devices (tags) harvest energy from ambient radio frequency (RF) signals and communicate with readers. Although reliable channel estimation (CE) is critical, classical pilot-based estimators tend to perform poorly. To address this challenge, we treat CE as a denoising problem using conditional generative adversarial networks (CGANs). A three-dimensional (3D) denoising block leverages spatial and temporal characteristics of pilot signals, considering both real and imaginary components of channel matrices. The proposed CGAN estimator is extensively evaluated against traditional estimators like minimum mean-squared error (MMSE), least squares (LS), convolutional neural network (CNN), CNN-based deep residual learning denoiser (CRLD), and blind estimation. Simulation results show 82% gain of the proposed estimator over CRLD and MMSE estimators at an SNR of 5 dB. Moreover, it has advanced learning capabilities and accurately replicates complex channel characteristics.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"805-822"},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10555303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141453439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint SNR and Rician K-Factor Estimation Using Multimodal Network Over Mobile Fading Channels 利用移动衰减信道上的多模态网络进行联合 SNR 和 Rician K 因子估计

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-11 DOI: 10.1109/TMLCN.2024.3412054

Kosuke Tamura;Shun Kojima;Phuc V. Trinh;Shinya Sugiura;Chang-Jun Ahn

引用次数: 0

Sybil Attack Detection Based on Signal Clustering in Vehicular Networks 基于车载网络信号聚类的仿冒攻击检测

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-05 DOI: 10.1109/TMLCN.2024.3410208

Halit Bugra Tulay;Can Emre Koksal

{"title":"Sybil Attack Detection Based on Signal Clustering in Vehicular Networks","authors":"Halit Bugra Tulay;Can Emre Koksal","doi":"10.1109/TMLCN.2024.3410208","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3410208","url":null,"abstract":"With the growing adoption of vehicular networks, ensuring the security of these networks is becoming increasingly crucial. However, the broadcast nature of communication in these networks creates numerous privacy and security concerns. In particular, the Sybil attack, where attackers can use multiple identities to disseminate false messages, cause service delays, or gain control of the network, poses a significant threat. To combat this attack, we propose a novel approach utilizing the channel state information (CSI) of vehicles. Our approach leverages the distinct spatio-temporal variations of CSI samples obtained in vehicular communication signals to detect these attacks. We conduct extensive real-world experiments using vehicle-to-everything (V2X) data, gathered from dedicated short-range communications (DSRC) in vehicular networks. Our results demonstrate a high detection rate of over 98% in the real-world experiments, showcasing the practicality and effectiveness of our method in realistic vehicular scenarios. Furthermore, we rigorously test our approach through advanced ray-tracing simulations in urban environments, which demonstrates high efficacy even in complex scenarios involving various vehicles. This makes our approach a valuable, hardware-independent solution for the V2X technologies at major intersections.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"753-765"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10550012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0