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Cellular offloading in device-to-device communication is a challengingoptimisation problem in which the improved allocation of radio resources canincrease spectral efficiency, energy efficiency, throughout and reduce latency.The academic community have explored different optimisation methods on theseproblems and initial results are encouraging. However, there exists significantfriction in the lack of a simple, configurable, open-source framework forcellular offload research. Prior research utilises a variety of networksimulators and system models, making it difficult to compare results. In thispaper we present GymD2D, a framework for experimentation with physical layerresource allocation problems in device-to-device communication. GymD2D allowsusers to simulate a variety of cellular offload scenarios and to extend itsbehaviour to meet their research needs. GymD2D provides researchers anevaluation platform to compare, share and build upon previous research. Weevaluated GymD2D with state-of-the-art deep reinforcement learning anddemonstrate these algorithms provide significant efficiency improvements.

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