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Dynamic Resource Allocation in LTE Radio Access Network Using Machine Learning Techniques

Journal of computer and Communications 2023年第6期11卷 73-93页

作者： Eric Michel Deussom Djomadji Ivan Basile Kabiena Valery Nkemeni Ayrton Garcia Belinga À Njere Michael Ekonde Sone Department of Electrical and Electronic Engineering College of Technology University of Buea Buea Cameroon Division of ICT National Advanced School of Post Telecommunication and ICT University of Yaounde I Yaounde Cameroon Department of Computer Engineering and Telecommunications National Advanced School of Engineering University of Douala Douala Cameroon Department of Computer Engineering Faculty of Engineering and Technology University of Buea Buea Cameroon

Current LTE networks are experiencing significant growth in the number of users worldwide. The use of data services for online browsing, e-learning, online meetings and initiatives such as smart cities means that subscribers stay connected for long periods, thereby saturating a number of signalling resources. One of such resources is the Radio Resource Connected (RRC) parameter, which is allocated to eNodeBs with the aim of limiting the number of connected simultaneously in the network. The fixed allocation of this parameter means that, depending on the traffic at different times of the day and the geographical position, some eNodeBs are saturated with RRC resources (overused) while others have unused RRC resources. However, as these resources are limited, there is the problem of their underutilization (non-optimal utilization of resources at the eNodeB level) due to static allocation (manual configuration of resources). The objective of this paper is to design an efficient machine learning model that will take as input some key performance indices (KPIs) like traffic data, RRC, simultaneous users, etc., for each eNodeB per hour and per day and accurately predict the number of needed RRC resources that will be dynamically allocated to them in order to avoid traffic and financial losses to the mobile network operator. To reach this target, three machine learning algorithms have been studied namely: linear regression, convolutional neural networks and long short-term memory (LSTM) to train three models and evaluate them. The model trained with the LSTM algorithm gave the best performance with 97% accuracy and was therefore implemented in the proposed solution for RRC resource allocation. An interconnection architecture is also proposed to embed the proposed solution into the Operation and maintenance network of a mobile network operator. In this way, the proposed solution can contribute to developing and expanding the concept of Self Organizing Network (SON) used in 4

关键词： RRC Resources 4G Network Linear Regression Convolutional Neural Networks Long Short-Term Memory Precision

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Groundbreaking Application of the Corona-Virus Search Optimizer Algorithm for Resilient Speed control and Stability Assessment of Direct Current Motors Under Uncertain Conditions

Groundbreaking Application of the Corona-Virus Search Optimi...

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electrical Machines and Drives (ICEMD), International Conference on

作者： Peyman Zare Amir Mohammadian SeyedJalal SeyedShenava Babak Mohamadi Department of Electrical and Computer Engineering Faculty of Engineering University of Mohaghegh Ardabili Ardabil Iran Province Electricity Distribution Company (APED Co.) University of Mohaghegh Ardabili and Ardabil Ardabil Iran

ISBN: (数字)9798331507510

ISBN: (纸本)9798331507527

In contemporary industrial systems where precision is paramount, the Direct Current (DC) motor plays a critical role, necessitating a highly efficient method for speed control that ensures both accuracy and responsiveness. Effective motor speed regulation is essential for the flawless execution of mechanical operations. This paper presents an innovative control strategy specifically designed for DC motor velocity regulation. The proposed methodology employs a Fractional Order (FO) controller, utilizing both Proportional-Integral-Derivative (PID) and Fractional Order PID (FOPID) controllers. These controllers are fine-tuned through the application of the Corona-Virus Search Optimizer (CVSO) algorithm, a novel metaheuristic technique inspired by the propagation dynamics of the coronavirus. This marks the first instance of the robust CVSO algorithm being applied in the domain of DC motor speed control. For performance evaluation, the Integral of Squared Time multiplied by Absolute Error (ISTAE) criterion has been adopted as the objective function. Simulations conducted in the MATLAB/Simulink environment have validated the efficacy of the proposed control approach. The controllers were rigorously tested against step torque profiles, with considerations for parametric uncertainties such as variations in electrical resistance and motor torque constants. The results reveal significant improvements in key performance metrics. Specifically, the FOPID controller outperforms the conventional PID controller with an 83.86% improvement in overshoot, a 40.17% improvement in settling time, and a 0.93% improvement in rise time under certain conditions. These findings demonstrate that the proposed FOPID/CVSO controller offers superior speed regulation and enhanced system stability compared to the traditional PID controller, making it a more effective solution for DC motor speed control.

关键词： Resistance Torque Uncertainty Heuristic algorithms Velocity control Stability criteria Metaheuristics DC motors Regulation PD control

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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Feature-Level Cross-Attentional PPG and Motion Signal Fusion for Heart Rate Estimation

Feature-Level Cross-Attentional PPG and Motion Signal Fusion...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Panagiotis Kasnesis Lazaros Toumanidis Alessio Burrello Christos Chatzigeorgiou Charalampos Z. Patrikakis ThinGenious PC Maroussi Greece Department of Electrical and Electronics Engineering University of West Attica Egaleo Greece Department of Control and Computer Engineering Politecnico di Torino Torino Italy

Nowadays, Hearth Rate (HR) monitoring is a key feature of almost all wrist-worn devices exploiting photoplethysmography (PPG) sensors. However, arm movements affect the performance of PPG-based HR tracking. This issue is usually addressed by fusing the PPG signal with data produced by inertial measurement units. Thus, deep learning algorithms have been proposed, but they are considered too complex to deploy on wearable devices and lack the explainability of results. In this work, we present a new deep learning model, PULSE, which exploits temporal convolutions and feature-level multi-head cross-attention to improve sensor fusion’s effectiveness and achieve a step towards explainability. We evaluate the performance of PULSE on three publicly available datasets, reducing the mean absolute error by 7.56% on the most extensive available dataset, PPG-DaLiA. Finally, we demonstrate the explainability of PULSE and the benefits of applying attention modules to PPG and motion data.

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Practical Implementation of RIS-Aided Spectrum Sensing: A Deep Learning-Based Solution

arXiv

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arXiv 2023年

作者： Kayraklik, Sefa Yildirim, Ibrahim Basar, Ertugrul Hokelek, Ibrahim Gorcin, Ali Lab. TUBITAK-BİLGEM Kocaeli Turkey Department of Electrical and Electronics Engineering Koç University Sariyer Istanbul Turkey The Faculty of Electrical and Electronics Engineering Istanbul Technical University Istanbul Turkey The Department of Electrical and Computer Engineering McGill University MontrealQC Canada The Department of Electronics and Telecommunications Engineering Istanbul Technical University Istanbul Turkey

This paper presents reconfigurable intelligent surface (RIS)-aided deep learning (DL)-based spectrum sensing for next-generation cognitive radios. To that end, the secondary user (SU) monitors the primary transmitter (PT) signal, where the RIS plays a pivotal role in increasing the strength of the PT signal at the SU. The spectrograms of the synthesized dataset, including the 4G LTE and 5G NR signals, are mapped to images utilized for training the state-of-the-art object detection approaches, namely Detectron2 and YOLOv7. By conducting extensive experiments using a real RIS prototype, we demonstrate that the RIS can consistently and significantly improve the performance of the DL detectors to identify the PT signal type along with its time and frequency utilization. This study also paves the way for optimizing spectrum utilization through RIS-assisted CR application in next-generation wireless communication systems. © 2023, CC BY-NC-ND.

关键词： Object detection

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Low-Cost Green Computing-as-a-Service Testbed for SMEs: Leveraging AI and 6G for Enhanced Productivity

Low-Cost Green Computing-as-a-Service Testbed for SMEs: Leve...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Mohammad N. Patwary Samiya Khan Syed Junaid Nawaz Faculty of Science and Engineering University of Wolverhampton Wolverhampton UK School of Computing and Mathematical Sciences University of Greenwich London UK Department of Electrical & Computer Engineering COMSATS University Islamabad Islamabad Pakistan

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

The exponential growth in computing devices and the emergence of high-computing-dependent applications have led to a significant increase in energy demand. In response, this paper proposes the development of a novel green computing testbed aimed at addressing energy efficiency and sustainability in computing infrastructure. The testbed integrates highly energy-efficient computing infrastructure, a virtual machine-driven on-demand computing ecosystem, and Green AI model development as a service. The state-of-the-art 5G/6G infrastructure and services, such as ultra-low-latency communications, are investigated and identified as suitable for connecting the zero clients (with minimal capabilities) to remote computing and other facilities. Through a comprehensive measurement campaign, the testbed’s energy efficiency and sustainability have been evaluated. This research presents a pioneering effort towards achieving sustainability in computing to support Small and Medium-sized Enterprises (SMEs) to enhance productivity with AI, with reduced cost, energy, and minimizing e-waste.

关键词： Productivity 6G mobile communication Social computing Costs Green products Graphics processing units Green computing Sustainable development Artificial intelligence Low latency communication

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Prediction of induction motor faults using machine learning

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Heliyon 2025年第1期11卷 e41493页

作者： Abdulkareem, Ademola Anyim, Tochukwu Popoola, Olawale Abubakar, John Ayoade, Agbetuyi Electrical and Information Engineering Department Covenant University P.M.B 1023 Ogun State Ota 112212 Nigeria Electrical Engineering Department Centre for Energy and Electric Power Faculty of Engineering and the Built Environment Tshwane University of Technology Nigeria Department of Computer Science and Engineering University of Bologna BO Italy

Unplanned downtime in industrial sectors presents significant challenges, impacting both production efficiency and profitability. To tackle this issue, companies are actively working towards optimizing their operations and reducing disruptions that hinder their ability to meet customer demands and financial goals. Predictive maintenance, utilizing advanced technologies like data analytics, machine learning, and IoT devices, offers real-time equipment data monitoring and analysis. This research study centers on the development of a versatile machine-learning model for predicting faults in induction motors within industrial environments. Implementing such a model can enable proactive maintenance, ultimately leading to decreased downtime in industrial operations. The study involved the acquisition of a dataset comprising healthy and faulty conditions of four 3-phase induction motors, along with relevant features for fault prediction. Multiple machine learning algorithms were trained using this dataset, exhibiting promising performance. The Random Forest (RF) model achieved the highest accuracy at 0.91, closely followed by the Artificial Neural Network (ANN) and k-nearest Neighbors (k-NN) models, both achieving an accuracy of 0.9. Meanwhile, the Decision Tree (DT) model showed the lowest accuracy at 0.89. Further model evaluation was carried out using a confusion matrix, which provided a detailed breakdown of the models' performance for each class, revealing the number of correctly and incorrectly classified induction motor conditions. The results from the confusion matrix indicate that the models effectively classified the various states and conditions of the induction motors. To enhance model performance in future work, potential avenues include refining the ANN and RF models, exploring transfer learning or ensemble methods, and incorporating diverse datasets to improve generalization. © 2024

关键词： Artificial neural network classifier Decision tree classifier Induction motors k-NN classifier Predictive maintenance Random Forest classifier

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Performance Comparison of Lightweight CNN Models for Drone Collision Avoidance Dataset

Performance Comparison of Lightweight CNN Models for Drone C...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Rifqi Nabila Zufar David Banjerdpongchai Department of Electrical Engineering Faculty of Engineering Center of Excellence in Intelligent Control Automation of Process Systems Chulalongkorn University254 Phayathai Road Pathumwan Bangkok Thailand

Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work.

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Measurement of Muscle Stimulation and Hot Compression for Rehabilitation 3

Measurement of Muscle Stimulation and Hot Compression for Re...

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3rd International Conference on electrical, Communication and computer engineering, ICECCE 2021

作者： Chirakanphaisarn, Neramitr Thongkanluang, Thadsanee Thammasat University Faculty of Engineering Department of Electrical and Computer Engineering Thailand Suratthani Rajabhat University Program in Materials Technology Manufacturing Faculty of Science and Technology Thailand

ISBN: (纸本)9781665438971

This research is to study the electric muscle stimulation system and hot compress. As well as focusing on building tools for applications in rehabilitation medicine and physical therapy. The neuromuscular system is an important mechanism in human life. In the event that the muscles are stressed or contracted while the subject is recovering, the need for recovery and stimulation tools is an important part of the physical therapy. We designed electrical circuits for automatic control and used an ARDUINO microcontroller to connect analog and digital ports to transmit and receive clinical parameters. The number of voluntary subjects was 20. Each subject remembered to perform 5 stimulation sessions for 10 minutes. Total period that the researcher collects data is approximately 3 months. The trials were based on ethical studies in humans, where trials and researches showed that subjects were 980% satisfied with very few side effects, and subjects had improved muscle development. © 2021 IEEE.

关键词： Muscle

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DialogNTM: Context Reconstruction in Multi-Turn Dialogue Generation using Neural Turing Machines

DialogNTM: Context Reconstruction in Multi-Turn Dialogue Gen...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haohao Zhao Meiling Liu Jiyun Zhou Kaiquan Fu College of Computer and Control Engineering Northeast Forestry University Harbin China Lieber Institute Johns Hopkins University Baltimore USA Department of Electrical Engineering and Computer Science South Dakota State University Brookings South Dakota USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In actual conversational scenarios, we can often determine which parts of the previous dialogue are more critical based on the current inquiry. However, the existing contextual modeling methods often encode the query sentence and the dialogue history in a unified manner, which fails to effectively highlight the inference effect of the query sentence. Moreover, these methods typically process the dialogue history only at the information extraction level, neglecting the treatment of the context itself. In this paper, we propose a novel conversational context modeling technique called DialogNTM. Based on the guidance of the query sentence, the technology can effectively eliminate redundant information by reconstructing the representation of the context. Specifically, we have tweaked the memory and input flow of the Neural Turing Machine (NTM) to encode contextual information in memory and guide the read, write, and erase operations of memory through query sentence. This design simulates the human brain's dynamic retrieval and renewal mechanism of previous memories when dealing with current problems. We have conducted extensive experiments on three publicly available datasets to verify the effectiveness of the DialogNTM model. Compared to the benchmark model, DialogNTM showed significant performance improvements ranging from 11% to 73% across multiple automated evaluation metrics (3.52% to 8.68% in absolute terms).

关键词： Adaptation models Turing machines Text summarization Benchmark testing Brain modeling Information retrieval Distance measurement History Cybernetics Context modeling

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