gym-d2d 0.0.3

Device-to-Device (D2D) communication OpenAI Gym environment

GymD2D: A Device-to-Device (D2D) Underlay Cellular Offload Evaluation Platform

GymD2D is a toolkit for building, evaluating and comparing D2D cellular offload resource allocation algorithms. It uses OpenAI Gym to make it easy to experiment with many popular reinforcement learning or AI algorithms. It is highly configurable, allowing users to experiment with UE configuration, path loss and traffic models.

GymD2D models a D2D cellular offload scenario containing a single macro base station surrounded with many cellular (CUE) and D2D (DUE) user equipment.

This project is still under active development and we haven't finished the first stable release yet. Some functionality, such as env.render() are not currently working.

If you have found this project useful in your research, please consider citing our white paper (preprint, forthcoming at IEEE WCNC 2021).

@article{cotton2021gymd2d,
  title={GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform},
  author={Cotton, David and Chaczko, Zenon},
  journal={arXiv preprint arXiv:2101.11188},
  year={2021}
}

Contents

• Requirements
• Installation
  • Dev Installation
• Usage
• Configuration
  • Environment configuration
  • Device Configuration
  • Observations and Rewards Configuration
• Examples

Requirements

• Python 3.7 or greater (dataclasses & __future__.annotations)
• OpenAI Gym 0.9.6 or greater (env_config)
• NumPy

Installation

Use pip to install

pip install gym-d2d

Dev Installation

Or, if you need to edit the source code, clone and install dev dependencies:

git clone [e-mail]:davidcotton/gym-d2d.git
cd gym-d2d
pip install -e ".[dev]"

Usage

Import OpenAI Gym and GymD2D

import gym
import gym_d2d

Build a new D2D environment via the usual Gym factory method

env = gym.make('D2DEnv-v0')

Then run in the standard Gym observation, action, reward loop.

obses = env.reset()
game_over = False
while not game_over:
    actions = {}
    for agent_id, obs in obses.items():
        action = env.action_space['due'].sample()  # or: action = agent.act(obs)
        actions[agent_id] = action

    obses, rewards, game_over, infos = env.step(actions)
    env.render()

The main difference between this environment and the usual classic control or ALE environments is that it is designed for multiple agents. The environment's observation and action spaces use gym.spaces.DictSpace, with 3 keys: due, cue & mbs. Observations, actions, rewards and info are passed via Python dicts like:

obs_dict = {
    'cue00': np.ndarray(...),
    'cue01': np.ndarray(...),
    'due00': np.ndarray(...),
    'due01': np.ndarray(...),
    ...
}
actions = {
    'cue00': 23,
    'cue01': 317,
    'due00': 13,
    'due01': 95,
    ...
}

We have some common usage examples in the examples directory.

Configuration

One of the design principles of this project is that environments should be easily configurable and customisable to meet the variety of research needs present in D2D cellular offload research.

Environment configuration

Following the Gym API (gym>=0.9.6), you can configure the environment via an env_config dictionary.

env = gym.make('D2DEnv-v0', env_config={'param': value})

The environment has the following configuration options:

Parameter	Description	Datatype	Default Value
num_rbs	The number of available resource blocks.	int	25
num_cues	The number of cellular users.	int	25
num_due_pairs	The number of D2D pairs	int	25
cell_radius_m	The macro base station's cell radius in metres. This parameter controls the radius in which all other devices are contained.	float	500.0
d2d_radius_m	The maximum distance between D2D pairs in metres.	float	20.0
due_min_tx_power_dBm	The minimum DUE transmission power in dBm.	int	0
due_max_tx_power_dBm	The maximum DUE transmission power in dBm.	int	20
cue_max_tx_power_dBm	The maximum CUE transmission power in dBm.	int	23
mbs_max_tx_power_dBm	The maximum MBS transmission power in dBm.	int	46
path_loss_model	The type of path loss model to use.	gym_d2d. PathLoss	gym_d2d. LogDistancePathLoss
traffic_model	The model to generate automated traffic.	gym_d2d. TrafficModel	gym_d2d. UplinkTrafficModel
obs_fn	The function to calculate agent observations.	gym_d2d.envs. ObsFunction	gym_d2d.envs. LinearObsFunction
reward_fn	The function to calculate agent rewards.	gym_d2d.envs. RewardFunction	gym_d2d.envs. SystemCapacityRewardFunction
carrier_freq_GHz	The carrier frequency used, in GHz.	float	2.1
num_subcarriers	The number of subcarriers.	int	12
subcarrier_spacing_kHz	The spacing between subcarriers.	int	15
channel_bandwidth_MHz	The channel bandwidth in MHz.	float	20.0
device_config_file	A path to a device configuration JSON file.	pathlib.Path	None (random device positions)

Device Configuration

By default, each time the environment is reset(), each UE is randomly assigned a new position. To make experiments repeatable and compare algorithms, you may wish to fix UE positions. This can be achieved by saving and loading device configurations. Gym D2D uses Python's Pathlib to make file handling easier.

from pathlib import Path

To save an environment's device configuration to file:

env = gym.make('D2DEnv-v0')
env.reset()  # generate random device positions (if not supplied)
env.save_device_config(Path.cwd() / 'device_config.json')

To load from an existing configuration:

env_config = {'device_config_file': Path.cwd() / 'device_config.json'}
env = gym.make('D2DEnv-v0', env_config=env_config)

Observations and Rewards Configuration

More info coming soon on how to customise observations and rewards...