GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform

TL;DR

GymD2D is an open-source simulation platform designed for research on device-to-device cellular offloading, enabling comparison and development of resource allocation algorithms to improve network efficiency.

Contribution

It introduces GymD2D, a configurable, open-source framework for simulating physical layer resource allocation in D2D communication, facilitating research and comparison.

Findings

Deep reinforcement learning algorithms improve efficiency in D2D offloading

GymD2D enables flexible scenario simulation and algorithm testing

The platform supports benchmarking and extending cellular offload research

Abstract

Cellular offloading in device-to-device communication is a challenging optimisation problem in which the improved allocation of radio resources can increase spectral efficiency, energy efficiency, throughout and reduce latency. The academic community have explored different optimisation methods on these problems and initial results are encouraging. However, there exists significant friction in the lack of a simple, configurable, open-source framework for cellular offload research. Prior research utilises a variety of network simulators and system models, making it difficult to compare results. In this paper we present GymD2D, a framework for experimentation with physical layer resource allocation problems in device-to-device communication. GymD2D allows users to simulate a variety of cellular offload scenarios and to extend its behaviour to meet their research needs. GymD2D provides researchers an evaluation platform to compare, share and build upon previous research. We evaluated GymD2D with state-of-the-art deep reinforcement learning and demonstrate these algorithms provide significant efficiency improvements.