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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

IEEE Wireless Communications Letters 2025年第6期14卷 1618-1622页

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems China The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of HongKong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent algorithm to iteratively optimize the antenna position vector, array rotation vector, receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio. Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. © 2025 IEEE. All rights reserved.

关键词： Aircraft communication

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Design and Analysis of Graphene Based Tunnel Field Effect Transistor with Various Ambipolar Reducing Techniques

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computers, Materials & Continua 2023年第7期76卷 1309-1320页

作者： Puneet Kumar Mishra Amrita Rai Nitin Sharma Kanika Sharma Nitin Mittal Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Department of Electronics and Communication Engineering G L Bajaj Institute of Technology and ManegmentGretear NoidaIndia Department of Electronics and Communication Engineering Chandigarh UnievrsityMohaliPunjabIndia Department of Electronics and Communication Engineering NITTTRChandigarhIndia University Center of Research&Development Chandigarh UniversityMohali121102India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah University11952Al-MajmaahSaudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

The fundamental advantages of carbon-based graphene material,such as its high tunnelling probability,symmetric band structure(linear dependence of the energy band on the wave direction),low effective mass,and characteristics of its 2D atomic layers,are the main focus of this research *** impact of channel thickness,gate under-lap,asymmetric source/drain doping method,workfunction of gate contact,and High-K material on Graphene-based Tunnel Field Effect Transistor(TFET)is analyzed with 20 nm *** modelling and electrical characteristic performance have been simulated using the Atlas device simulator of SILVACO TCAD with user-defined material syntax for the newly included graphene material in comparison to silicon carbide(SiC).The simulation results in significant suppression of ambipolar current to voltage characteristics of TFET and modelled device exhibits a significant improvement in subthreshold swing(0.0159 V/decade),the ratio of Ion/Ioff(1000),and threshold voltage(-0.2 V with highly doped p-type source and 0.2 V with highly doped n-type drain)with power supply of 0.5 V,which make it useful for low power digital applications.

关键词： Graphene tunnel field effect transistor(TFET) band to band tunnelling subthreshold swing

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THÖR-Magni: Comparative Analysis of Deep Learning Models for Role-conditioned Human Mtion Prediction

THÖR-Magni: Comparative Analysis of Deep Learning Models fo...

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2023 IEEE/CVF International Conference on computer Vision Workshops, ICCVW 2023

作者： De Almeida, Tiago Rodrigues Rudenko, Andrey Schreiter, Tim Zhu, Yufei Maestro, Eduardo Gutierrez Morillo-Mendez, Lucas Kucner, Tomasz P. Martinez Mozos, Oscar Magnusson, Martin Palmieri, Luigi Arras, Kai O. Lilienthal, Achim J. Örebro University Sweden Corporate Research Robert Bosch GmbH Stuttgart Germany Aalto University Mobile Robotics Group Department of Electrical Engineering and Automation Finland Finnish Center for Artificial Intelligence Fcai Finland

ISBN: (纸本)9798350307443

Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel THÖR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics. © 2023 IEEE.

关键词： deep learning human motion dataset human trajectory prediction

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Onboard UAV State Estimation and Trajectory Prediction Using Multi-Task Reservoir Computing

Onboard UAV State Estimation and Trajectory Prediction Using...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Nicolas Souli Panayiotis Kardaras Yiannis Grigoriou Panayiotis Kolios Georgios Ellinas Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Computer Science KIOS Research and Innovation Center of Excellence University of Cyprus

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

The rapid advancements in unmanned aerial vehicle (UAV) technology have led to their use in different applications, ranging from critical infrastructure monitoring and search-and-rescue to remote sensing. However, UAV operations are easily affected by environmental conditions and sensor malfunctions that lead to the need for an efficient, accurate, and trustworthy state identification and trajectory prediction framework. This work proposes an innovative real-time UAV system with the two-fold objective of state identification and trajectory prediction, employing a lightweight multi-task learning framework based on reservoir computing (RC) network architecture to achieve reliable and robust UAV operations. Specifically, custom multi-task models are designed and finetuned to obtain multi-modal sequential data (related to drone movement) by exploiting the ability of shared feature learning in an RC-based network architecture to accurately achieve and enhance real-time and simultaneous drone state classification and trajectory prediction. A real-world dataset is also created to train and evaluate the proposed multi-task model, encompassing drone movements recorded during numerous outdoor experiments. Finally, a UAV prototype system is implemented and extensively tested in a real-world environment to demonstrate its enhanced performance in trajectory prediction and drone state identification compared to existing methods.

关键词： Computational modeling Reservoir computing Predictive models Network architecture Autonomous aerial vehicles Multitasking Real-time systems Data models Trajectory Drones

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Real-Time Beehive Condition Improving Frequency Selection and Bee Welfare Monitoring and Analysis

Real-Time Beehive Condition Improving Frequency Selection an...

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2024 IEEE Wireless Antenna and Microwave Symposium, WAMS 2024

作者： Singh, Sandeep Kumar, Yogendra Alkhayyat, Ahmed Singh, Devendra Anand, Rohit Chohan, Jasgurpreet Singh University Center for Research and Development Chandigarh University Mohali14013 India GLA University Department of Electrical Engineering Mathura India College of Technical Engineering The Islamic University Najaf Iraq Uttaranchal Institute of Technology Department of Computer Science & Engineering Uttaranchal University Dehradun248007 India Department of ECE New Delhi India

ISBN: (纸本)9798350358926

Beekeeping is a crucial agricultural activity that is needed for pollination and the production of honey. For bee colonies to remain healthy and productive, ideal hive conditions must be maintained. In this study, a real-time hive condition management system is described for beekeepers based on IoT. An ESP8266 microcontroller, a DHT11 sensor for measuring temperature and humidity, and a buzzer are all components of our system. We have collected and evaluated temperature and humidity data from beehives over a long period of time by deploying our monitoring system. We go over our system's practical applications for beekeeping, such as the early identification of unfavorable environmental circumstances and the avoidance of hive stress. We recognize some issues that need additional adjustment, such as sensor accuracy and power supply. This research presents an approach for beekeepers looking to enhance hive management techniques and contributes to the expanding field of IoT in agriculture. © 2024 IEEE.

关键词： Internet of things

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Interference Estimation via Model-Based Deep Learning in Grant-Free Networks

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IEEE Wireless Communications Letters 2025年第6期14卷 1663-1667页

作者： Pereira, Diogo Oliveira, Rodolfo da Costa, Daniel Benevides Kim, Hyong Instituto de Telecomunicações Lisbon1049-001 Portugal Universidade Nova de Lisboa Dep. de Engenharia Electrotécnica e de Computadores Faculdade de Ciências e Tecnologia Caparica2829-516 Portugal Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Carnegie Mellon University Electrical and Computer Engineering PittsburghPA United States

This paper presents a novel approach for estimating interference distribution parameters in grant-free access networks using a model-based deep learning (DL) framework. Our method integrates the precision of analytical models with the adaptability of deep learning algorithms. Specifically, we employ an analytical model to generate labeled data, which enhances the deep learning model’s ability to estimate interference levels. Through extensive validation, we demonstrate that our approach accurately estimates interference across a broad range of scenarios, including operating regions not covered during the model’s training. Moreover, our method also estimates the spatial density of interfering nodes, making it a valuable tool for interference management in grant-free access networks. This methodology offers a robust solution for improving interference estimation accuracy, aiding decision-making at the Medium Access Control (MAC) and physical layers in grant-free access schemes. © 2025 IEEE. All rights reserved.

关键词： Contrastive Learning

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Distributed AI-native Architecture for 6G Networks

Distributed AI-native Architecture for 6G Networks

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2022 International Conference on Information Processing and Network Provisioning, ICIPNP 2022

作者： Li, Pengyu Xing, Yanxia Li, Wei 6G Research Center China Telecom Research Institute Beijing China University of Victoria Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9781665464055

How to achieve native intelligence in 6G networks has attracted widespread attention and discussion in the communication field. In the core network, NWDAF (Network Data Analytics Function) is introduced as an AI (Artificial Intelligence) service NF (Network Function) to provide intelligence analysis, such as supporting network load and congestion prediction, UE behavior, and service experience statistics. However, there are still problems such as the lack of AI capability of NFs (Network Functions), weak cross-domain collaboration, and little support for distributed learning methods, etc. This paper presents our solutions on native intelligence in 6G networks, including the overall distributed network architecture of native intelligence, emphasizing the capabilities of each intelligent node and the collaboration among nodes, and further proposes some thoughts and potential solutions from the perspective of cross-domain coordination, and the combination of distributed learning and the network. © 2022 IEEE.

关键词： Learning systems

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Real-Time Text Classification Algorithm Based On Combined Machine Learning Models 2

Real-Time Text Classification Algorithm Based On Combined Ma...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Ravivarman, G. Mahajan, Sulabh Sowmiya, S. Goyal, Shitij Gupta, Rahul Wanjale, Kirti H. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350388916

Web text categorization is a prominent area of research and a vital technology in the fields of Web information retrieval and data mining. A great deal of attention and quick progress in this field has occurred in recent years. One sort of deep learning model that has been more useful for extracting important characteristics from textual input while reducing model complexity is the convolutional neural network (CNN). In the meanwhile, a time-tested machine learning method known as support vector machine (SVM) has a stellar reputation for reliability and accuracy. By merging an improved CNN with SVM, this research suggests a new method for Web text categorization. Text characteristics are extracted using the CNN model, which has a five-layer network structure. These features are then used for classification as well as forecasting using SVM. This method presents a new approach to Web text categorization using enhanced models by combining the capabilities of convolutional neural networks (CNNs) and support vector machines (SVMs). Applying this method to datasets with mixed text yields outstanding results, all things considered. © 2024 IEEE.

关键词： Adversarial machine learning

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EEG based stress classification by using difference plots of variational modes and machine learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16347-16360页

作者： Salankar, Nilima Qaisar, Saeed Mian School of Computer Science University of Petroleum and Energy Studies Dehradun India Persistent System Limited IT Park Maharashtra Nagpur India Department of Electrical and Computer Engineering EFfat University Jeddah22332 Saudi Arabia Communication and Signal Processing Lab Energy and Technology Research Center Effat University Jeddah22332 Saudi Arabia

The recent trend in healthcare is to use the automated biomedical signals processing for an augmented and precise diagnosis. In this context, an original approach is presented for categorization of stress and non-stress classes by processing the multichannel Electroencephalogram (EEG) signals. The EEG signals are decomposed by using the "Empirical Mode Decomposition" (EMD) and "Variational Mode Decomposition" (VMD), respectively render the "Intrinsic Mode Functions" (IMFs) and Modes. The Second Order Difference Plots (SODPs) are traced for each IMF and Mode. Shapes of SODPs are used to discriminate the two intended categories. The feature space includes areas, Central Tendency Measures (CTMs) and means of SODPs. It is derived from first 7 IMFs and Modes. The identification of stress and non-stress categories is performed by using the robust machine learning algorithms namely, the Support Vector Machine (SVM), Boosting with Random Forest (RF) and Multilayer Perceptron Neural Network (MLPNN). The applicability is tested by using a publicly available EEG signals dataset, recorded during mental arithmetic test. A detailed evaluation is carried out at the subset, individual channel and lobes wise. Noteworthy highest accuracies of subset wise 99.89%, channel wise 98.89% and lobe wise 99.99% are obtained respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Empirical mode decomposition

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Leaf Classification of Jackfruit (Artocarpus heterophyllus) and Cempedak (Radermachera integra) Using Deep Learning 14

Leaf Classification of Jackfruit (Artocarpus heterophyllus) ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Budiman, Gelar Utoro, Rio Korio Rizqyawan, Muhammad Ilham Rudawan, Rikman A. Novianty, Astri Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University The University Center of Excellence for Advanced Intelligent Communications Bandung Indonesia Research Center of Smart Mechatronics National Research and Innovation Agency Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia

ISBN: (纸本)9798350335095

Plant identification and categorization tasks require accurate leaf classification. In this study, we investigate the use of deep learning approaches for categorizing the leaves of cempedak (Artocarpus integra) and jackfruit (Artocarpus heterophyllus). We use the well-known deep learning architectures VGG16 and Inception v3 to correctly categorize the leaf images. A dataset of 204 leaf images, with equal representation from jackfruit and cempedak, is used to train and assess the models. According to the experimental findings, the VGG16 and Inception v3 architectures both perform exceptionally well when classifying leaves. Jackfruit and cempedak leaves are accurately classified with 97.57% accuracy by the VGG16 model. These high accuracy rates demonstrate how well deep learning models are able to differentiate between the two leaf varieties. © 2023 IEEE.

关键词： Deep learning

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