Design of an interference mitigation platform for roundabout vehicular ad-hoc networks: a smart signal processing and control learning approach

The vehicular Ad-hoc Network (VANET) paradigm is integral to contemporary transportation systems, enabling the Internet of Vehicles (IoV) and Intelligent Transportation Systems (ITS). This research aims to deepen the knowledge of vehicular wireless communication by investigating the influence of smart signal processing and control learning strategies on interference mitigation by the Signal-to-Interference-plus-Noise Ratio (SINR) and outage performance in VANETs. Initially, the study sought to identify the best digital beamforming techniques to reduce co-channel interference and enhance communication reliability in VANET settings. Following this, it examines the incorporation of control mechanisms such as Proportional-Integral-Derivative (PID) controllers and Reinforcement Learning (RL) algorithms into VANET simulations for dynamic SINR management. Finally, the research investigates the relative efficiency of different control strategies and digital beamforming methods in reducing interference and improving communication reliability under fair conditions on a simulated roundabout VANET platform with different scenarios. Through rigorous experiments and assessments in the simulated roundabout VANET environment, the objective is to confirm the effectiveness of the suggested interference mitigation methods and offer valuable perspectives for future studies and practical implementation in real-world contexts taking advantage of the rise of cellular technologies such as 5G and 6G to advance in the development of vehicular networks with intelligent interference mitigation and SINR management framework for smart cities.

https://orcid.org/0000-0003-1770-471X