Feature articles – April 2025
Our journal welcomes not only original high-quality papers covering the theoretical, experimental and operational aspects of electrical and electronics engineering in mobile radio, motor vehicles and land transportation, but also industry-focused publication focusing on research findings and suggesting ideas that may be useful to those conducting similar research.
In the following, let us provide you with a one-stop destination to know about a recently published article from our industry authors related to satellite communications, as well as a survey article from highly cited researchers on electric vehicle charging demand forecasting techniques. We hope that the short summary of these featue articles wrritten in layman language may make your reading a pleasure!
Optimal Linear Precoding Under Realistic Satellite Communications Scenarios
Authors: Geoffrey Eappen; Jorge Luis Gonzalez; Vibhum Singh; Rakesh Palisetty; Alireza Haqiqtnejad; Liz Martinez Marrero; Jevgenij Krivochiza; Jorge Querol; Nicola Maturo; Juan Carlos Merlano Duncan; Eva Lagunas; Stefano Andrenacci; Symeon Chatzinotas
Published in volume 6, IEEE Open Journal of Vehicular Technology
IEEExplore version
Summary contributed by Vibhum Singh (Author):
We introduce a significant advancement in the field of satellite communications by developing a novel linear precoding strategy explicitly designed for multi-beam geostationary earth orbit (GEO) satellite systems. This innovative approach effectively tackles the challenge of multi-user interference, a prevalent issue in environments where multiple users operate within the same frequency and time blocks. By implementing optimal linear precoders at satellite gateways, the technique utilizes channel state information from user terminals to optimize the transmission power and reduce interference, thereby enhancing the overall system throughput and spectral efficiency. This method not only improves signal quality across the satellite communication network but also enhances the data transmission capacity, demonstrating its potential to significantly impact future satellite communication systems.
A Comprehensive Survey of Electric Vehicle Charging Demand Forecasting Techniques
Authors: Mamunur Rashid; Tarek Elfouly; Nan Chen
Published in volume 5, IEEE Open Journal of Vehicular Technology
IEEExplore version
Summary contributed by Nan Chen (Author):
As the electric vehicle (EV) market grows, finding the best places to install charging stations and determining the number of chargers installed is crucial. Researchers have studied different ways to predict EV charging demand using mathematical models and probability-based approaches. While these methods provide a strong foundation, they often result in complicated models that don t easily scale. Emerging machine learning techniques show great promise, but many important challenges remain, such as dealing with limited data, protecting user privacy, handling unpredictable charging patterns, and more. In this paper, we review existing research on EV charging demand forecasting, putting a heavy focus on machine learning techniques. Each technique is introduced with its core functionality, how it is used in EV charging demand forecasting and what needs to be done in the future. We break down the forecasting process, from gathering and processing data to identifying key EV charging patterns. We also highlight gaps in current research and suggest future directions to help improve charging infrastructure planning for businesses, policymakers, and researchers.
About IEEE Open Journal of Vehicular Technology (OJVT)
The IEEE Open Journal of Vehicular Technology covers the theoretical, experimental and operational aspects of electrical and electronics engineering in mobile radio, motor vehicles and land transportation. (a) Mobile radio shall include all terrestrial mobile services. (b) Motor vehicles shall include the components and systems and motive power for propulsion and auxiliary functions. (c) Land transportation shall include the components and systems used in both automated and non-automated facets of ground transport technology