European Journal of Electrical Engineering and Computer Science

Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review

Sajjad Emdadi Mahdimahalleh — 2024-12-31

These days, with the rising computational capabilities of wireless user equipment such as smartphones, tablets, and vehicles, along with growing concerns about sharing private data, a novel machine learning model called federated learning (FL) has emerged. FL enables the separation of data acquisition and computation at the central unit, which is different from centralized learning that occurs in a data center. FL is typically used in a wireless edge network where communication resources are limited and unreliable. Bandwidth constraints necessitate scheduling only a subset of UEs for updates in each iteration, and because the wireless medium is shared, transmissions are susceptible to interference and are not assured. The article discusses the significance of Machine Learning in wireless communication and highlights Federated Learning (FL) as a novel approach that could play a vital role in future mobile networks, particularly 6G and beyond.

Optimizing Scheduling Techniques for Enhanced Carrier Aggregation in LTE-Advanced Networks

Sajjad Emdadi Mahdimahalleh — 2024-12-10

In this study, we investigate resource scheduling strategies for the downlink in LTE-Advanced networks, focusing specifically on systems that employ Carrier Aggregation (CA) with multiple Component Carriers (CCs). Effective scheduling is crucial in CA-enabled LTE-Advanced to maximize efficiency and support diverse user needs. We examine three conventional scheduling schemes—Joint User Scheduling (JUS), Separated Random User Scheduling (SRUS), and Separated Burst-Level Scheduling (SBLS). Although JUS yields superior performance, it is complex and does not factor in Quality of Experience (QoE) metrics. In contrast, SRUS and SBLS, while less computationally intensive, underutilize CA and fall short in resource allocation fairness. To overcome these limitations, we propose a new scheduling scheme, Quality of Service and Channel Scheduling (QSCS), which dynamically adjusts CC allocations at the burst level based on user service priority and signal quality. Simulation results show that QSCS achieves throughput similar to JUS while selecting optimal CCs based on Quality of Service (QoS) and channel parameters, resulting in enhanced QoE compared to other methods. This proposed approach demonstrates significant potential for improving resource allocation and user satisfaction in LTE-Advanced networks.

Estimating Battery Life in Electric Vehicles using Deeper Long Short-Term Memory (DLSTM) Algorithm

Le Viet Bach — 2024-11-16

The paper estimates electric vehicle battery life using a deep, long-term memory algorithm (DLSTM). This algorithm employs a Forget Gate with a sigmoid function to retain or discard information from previous states. The Input Gate, also using a sigmoid function, determines new information to add, while a tanh function creates a new vector for updating the cell state. The Cell State Update combines inputs from the forget and input gates. The Output Gate uses a sigmoid function to select which part of the cell state to output. This AI algorithm analyses voltage, current, and temperature during charging to predict the lithium-ion battery’s state.

A Data-Driven Model for Classification of Traditional Chinese Medicine Materials

Li Zhi-Jie — 2024-11-08

This paper proposes a data-driven classification model for traditional Chinese medicinal herbs based on mid-infrared spectral data. Addressing the limitations of traditional identification methods when the herbs’ appearance is damaged or incomplete, this study employs machine learning techniques to achieve accurate classification of medicinal herb types through spectral data preprocessing and feature extraction. Firstly, the Savitzky-Golay convolution smoothing method and Standard Normal Variate (SNV) transformation were used for denoising the spectral data. Then, Principal Component Analysis (PCA) was employed to reduce the dimensionality of the high-dimensional spectral data and extract the key features. Finally, the Gaussian Mixture Model (GMM) was applied to cluster the reduced data, categorizing the medicinal herbs into six classes. The results show that this method produces the accurate and stable classification. The constructed model is not only applicable to the classification and origin identification of medicinal herbs but also provides an important reference value for the classification and origin identification of other plant species.

Impact of Ad Blockers on Computer Power Consumption while Web Browsing: A Comparative Analysis

Khan Awais Khan — 2024-10-13

This study explores the impact of various ad blockers on power consumption during web browsing, focusing on different types of online content. By analyzing power use across ten popular websites, the study assesses the performance of five widely utilized ad blockers: AdBlock, AdBlock Plus, uBlock, uBlock Origin, and uBlock Origin Lite. Power consumption was measured under controlled conditions, comparing scenarios with and without ad blockers to gain insight into their efficiency. The findings indicate substantial differences in power savings, with some ad blockers significantly reducing power usage, particularly on media-heavy sites, while others unexpectedly increased consumption under certain conditions. The study underscores the potential of ad blockers to enhance power efficiency in digital environments, highlighting the importance of optimizing ad-blocking techniques to reduce the environmental impact of online activities. Through comprehensive analysis and comparison, this research offers insights into selecting effective ad blockers to minimize power consumption, promoting more sustainable web browsing practices.