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Update citation in readme #14

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Jul 30, 2023
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3346e8c
Update citation in readme
stefanbschneider Jul 30, 2023
d15b8b3
link mobile-env in readme
stefanbschneider Jul 30, 2023
c42931e
delete citation.cff
stefanbschneider Jul 30, 2023
9dbf5a8
fix python version in setup.py
stefanbschneider Jul 30, 2023
998abb1
pin protobuf version
stefanbschneider Jul 30, 2023
01e250e
pin protobuf to 3.20.3
stefanbschneider Jul 30, 2023
1bcbbc6
limit pydantic in setup.py
stefanbschneider Jul 30, 2023
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17 changes: 0 additions & 17 deletions CITATION.cff
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16 changes: 9 additions & 7 deletions README.md
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Expand Up @@ -6,7 +6,8 @@

# DeepCoMP: Self-Learning Dynamic Multi-Cell Selection for Coordinated Multipoint (CoMP)

Deep reinforcement learning for dynamic multi-cell selection in CoMP scenarios.
Multi-Agent Deep Reinforcement Learning for Coordinated Multipoint in Mobile Networks

Three variants: DeepCoMP (central agent), DD-CoMP (distributed agents using central policy), D3-CoMP (distributed agents with separate policies).
All three approaches self-learn and adapt to various scenarios in mobile networks without expert knowledge, human intervention, or detailed assumptions about the underlying system.
Compared to other approaches, they are more flexible and achieve higher Quality of Experience.
Expand All @@ -15,6 +16,8 @@ For a high-level overview of DeepCoMP, please refer to my [blog post](https://st
More details are available in our research paper presenting DeepCoMP ([preprint](https://ris.uni-paderborn.de/download/33854/33855/preprint.pdf)).
I also talked about DeepCoMP at the Ray Summit 2021 ([YouTube](https://youtu.be/Qy4SzJKXlGE)).

The simulation environment used to train DeepCoMP is available separately as [mobile-env](https://github.com/stefanbschneider/mobile-env).

<p align="center">
<img src="https://raw.githubusercontent.com/CN-UPB/DeepCoMP/master/docs/gifs/dashboard_lossy.gif?raw=true"><br/>
<em>Visualized cell selection policy of DeepCoMP after 2M training steps.</em><br>
Expand All @@ -23,15 +26,14 @@ I also talked about DeepCoMP at the Ray Summit 2021 ([YouTube](https://youtu.be/

## Citation

If you use this code, please cite our [paper (preprint; under review)](https://ris.uni-paderborn.de/download/33854/33855/preprint.pdf):
If you use this code, please cite our [paper (preprint; accepted at IEEE TNSM 2023)](https://ris.uni-paderborn.de/download/33854/33855):

```
@article{schneider2021deepcomp,
title={DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning},
@article{schneider2023deepcomp,
title={Multi-Agent Deep Reinforcement Learning for Coordinated Multipoint in Mobile Networks},
author={Schneider, Stefan and Karl, Holger and Khalili, Ramin and Hecker, Artur},
journal={Under Review},
year={2021},
note={Open-source repository: \url{https://github.com/CN-UPB/DeepCoMP}}
journal={IEEE Transactions on Network and Service Management (TNSM)},
year={2023},
}
```

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8 changes: 6 additions & 2 deletions setup.py
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Expand Up @@ -27,6 +27,10 @@
# extra dependencies for Ray RLlib
# installing directly via ray[rllib] doesn't work with setup.py: https://github.com/ray-project/ray/issues/11274
'scipy==1.4.1',
# need to pin protobuf to avoid import errors: https://stackoverflow.com/q/71759248/2745116
'protobuf==3.20.3',
# same for pydantic: https://github.com/aws/aws-sdk-pandas/issues/2379#issuecomment-1621178909
'pydantic<2.0.0',
# 'lz4',
'svgpath2mpl>=0.2.1',
]
Expand All @@ -37,14 +41,14 @@

setup(
name='deepcomp',
version='1.4.1',
version='1.4.2',
author='Stefan Schneider',
description="DeepCoMP: Self-Learning Dynamic Multi-Cell Selection for Coordinated Multipoint (CoMP)",
long_description=long_description,
long_description_content_type='text/markdown',
url='https://github.com/CN-UPB/DeepCoMP',
packages=find_packages(),
python_requires=">=3.8.*",
python_requires=">=3.8.0",
install_requires=requirements + eval_requirements,
zip_safe=False,
entry_points={
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