NedgeSIM-RL: Simulating Smart Offloading in NOMA-VEc Environments

The advent of cooperative intelligent transport systems (C-ITS) and connected vehicles has brought with it a raft of novel services, including autonomous driving and dynamic traffic management, which necessitate both real-time processing and substantial data processing capacity. These technological advancements have imposed significant network constraints, requiring the development of robust network infrastructures to guarantee the reliable transmission of critical information in real time. In consequence, numerous studies have concentrated on NOMA-assisted Multi-Access Edge Computing (NOMA-MEC) to facilitate the provision of new services and, in particular, on their management through the utilisation of Multi-Agent Deep Reinforcement Learning (MADRL) algorithms. Nevertheless, the absence of a unified framework for the rigorous evaluation and comparison of these different approaches represents a significant issue. For this reason, we have established a unified, adaptable, and extensible framework, NedgeSIM-RL, with the objective of evaluating decentralised offloading strategies within the context of NOMA-VEC architectures. In this paper, we illustrate the functionality of the framework, the potential configurations, and the areas of research that can be pursued. We also present a basic simulation that can be executed on the framework to manage decentralised offloading via NOMA on a single MEC server by multiple vehicles.