The paper deals with simulations of power line channel transfer functions in Network Simulator version 3. Firstly, an empirical model and calculation of the channel transfer function are given to reflect the necessity of channel transfer function for Power Line Communication system design. The framework for Power Line Communication in Network Simulator version 3 and then the necessary extension implementation are introduced. Other simulators are also mentioned. Secondly, various scenarios were implemented for the analysis and simulation of power line channel transfer functions. New scenarios for large topologies and for different approaches to calculate primary parameters were created. In the simulations, various kinds of topologies are considered for an analysis of the power line transfer function. The simulation part also focuses on the simulation of channel transfer function where the time- and frequency-selective impedances are considered. Finally, the last part focuses on measurements and a comparison of the simulation results with real measurements are given

Building real Smart Metering and Smart Grid networks is very expensive and time-consuming and also it is impossible to install different technologies in the same environment only for comparison. Therefore, simulation and experimental pilot measurements are an easy, economical, and time-affordable solution for a first comparison and evaluation of different technologies and solutions. The local area networks (LAN) are the core of Smart Metering and Smart Grid networks. The two predominant technologies are mostly sufficient for LAN networks, Power Line Communication (PLC), and radio frequency (RF) solutions. For PLC it is hard to allow prediction of the behaviour. Performance assessment for point-to-point connection is easy, but for complex PLC networks with repeaters it is quite expensive. Therefore, a simulation is an easy, fast, and cheap solution for understanding the grid configuration, influence of particular topological components, and performance possibilities. Simulation results can, thus, provide material for the design of a telecommunication infrastructure for Smart Metering. This paper presents results of such a simulation study. It is based on realistic PLC channel model implementation in Network Simulator 3, our modification and extension of this implementation for our use case scenario. It uses Shannon’s formula to calculate theoretical maximum channel capacity. In particular, it provides channel capacity and achievable distances of broadband PLC (BB-PLC). In this article we also exploit our novel idea of simple performance assessment of broadband PLC communication via simulation. It is supposed to be used to understand, evaluate, and test the grid configuration before deployment.

Vehicle-to-vehicle (V2V) communication, which plays an important role in intelligent transportation systems, has been statistically proven to improve traffic efficiency and reduce the probability of accidents. In real-world applications, it is critical to accurately estimate the path loss parameter in communication channels due to the variable and complex propagation environments often encountered in inter-vehicle communication scenarios. This paper presents a study on various machine learning methods to improve path loss estimation in V2V communication using a dataset (192,000 observations) obtained from field measurements of highway environments in the Trabzon and Gümüşhane provinces in Türkiye. For this purpose, path loss estimation was carried out with different machine learning algorithms such as Artificial Neural Networks, Random Forest, Linear Regression, Gradient Boosting, Support Vector Regression, and AdaBoost by using various environmental and system features. Then, performance comparisons were conducted between machine learning methods and traditional empirical approaches such as log-distance, two-ray, and log-ray. Examining the outputs reveals that machine learning methods outperform traditional methods and yield results quickly. As a result, the Random Forest and Gradient Boosting methods demonstrated the highest prediction performances, with R² values of 0.97 and 0.96, MAE values of 0.0557 and 0.0701, and RMSE values of 0.0774 and 0.0964, respectively, outperforming both empirical methods, other machine learning techniques, and the existing studies based on V2V. Overall, our study provides significant contributions to the existing literature by providing a comprehensive parameter set for highway environments, examining the path loss prediction performance of machine learning models with different capabilities, and comparing them with traditional methods. This study not only fills a critical gap in the existing literature but also highlights the necessity, efficiency, and originality of machine learning approaches for improving reliable V2V communication systems.