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2021 ITU Kaleidoscope Academic Conference

Figure 2 – CAVIAR simulation overview.

User Equipment (UE). The RL agent is executed at the files, which are named episodes, containing the trajectory
Base Station (BS) and periodically takes actions based on the data of all moving objects within a simulation. To generate it,
information captured from the environment, which includes a waypoint file, which is a text file with reference points, must
channel estimates, buffer status, and positions from a Global be executed by AirSim. During its execution, the information
Navigation Satellite System, such as GPS. The RL agent from the mobile elements is stored in the episode. Each
receives a reward based on the service provided to the episode lasts about three minutes, with a sampling interval
users. The training occurs “offline”, without rendering the of ten milliseconds, and is composed by columns related to
3D scenes, but it is possible to render the output in a position and orientation for pedestrians and cars, with the
post-processing step and generate a video. addition of acceleration, linear, and angular velocities for
UAVs. To use the episode files to obtain information from
This work is organized as follows. In Section 2 we discuss Multiple-Input Multiple-Output (MIMO) channels and data
CAVIAR simulations in general and the specific RL problem traffic, one must execute them within the CAVIAR simulation
addressed in this paper. Sections 3 and 4 describe the environment.
communication and machine learning models, respectively.
Simulations results are presented in Section 5, while Section Unreal/AirSim
6 concludes the paper.
Waypoint
generator Episodes
2. CAVIAR SIMULATIONS
- MIMO channels
As proposed in [6] and shown in Figure 2, the CAVIAR - Combined channel magnitudes Simula on
framework incorporates three subsystems: AI/ML, virtual - Data traffic environment
world, and wireless communications. In the following
paragraphs we describe the framework, focusing first on the Figure 3 – CAVIAR data generation.
overall description of the methodology and then on how
it was realized in the user scheduling and beam selection
environment. 2.1 Overall CAVIAR description

RL tasks can be continuous or episodic; this last category As previously mentioned, Figure 2 displays an overview of the
assumes the context adopted in this work. Figure 3 expected components in a CAVIAR simulation. In summary,
exemplifies the CAVIAR data generation pipeline: the data the blocks encompassing the proposed simulation strategy
set is provided as a set of Comma-Separated Values (CSVs) can be described as follows: the Communications Engine

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