5G Routing Interfered Environment
This work addresses routing challenges in 5G networks for researchers and developers, but it appears incremental as it builds on existing tools like Gym and Stable-Baselines 3.
The paper tackles the problem of routing packets in 5G networks by developing a Python-based simulation environment called 5GRIE, which allows testing of algorithms like deep reinforcement learning and heuristics, but no specific performance results or numbers are provided.
5G is the next-generation cellular network technology, with the goal of meeting the critical demand for bandwidth required to accommodate a high density of users. It employs flexible architectures to accommodate the high density. 5G is enabled by mmWave communication, which operates at frequencies ranging from 30 to 300 GHz. This paper describes the design of the 5G Routing Interfered Environment (5GRIE), a python-based environment based on Gym's methods. The environment can run different algorithms to route packets with source and destination pairs using a formulated interference model. Deep Reinforcement Learning algorithms that use Stable-Baselines 3, as well as heuristic-based algorithms like random or greedy, can be run on it. Profitable is an algorithm that is provided.