---
_id: '33854'
abstract:
- lang: eng
  text: "Macrodiversity is a key technique to increase the capacity of mobile networks.
    It can be realized using coordinated multipoint (CoMP), simultaneously connecting
    users to multiple overlapping cells. Selecting which users to serve by how many
    and which cells is NP-hard but needs to happen continuously in real time as users
    move and channel state changes. Existing approaches often require strict assumptions
    about or perfect knowledge of the underlying radio system, its resource allocation
    scheme, or user movements, none of which is readily available in practice.\r\n\r\nInstead,
    we propose three novel self-learning and self-adapting approaches using model-free
    deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages
    central observations and control of all users to select cells almost optimally.
    DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and
    highly scalable coordination. All three approaches learn from experience and self-adapt
    to varying scenarios, reaching 2x higher Quality of Experience than other approaches.
    They have very few built-in assumptions and do not need prior system knowledge,
    making them more robust to change and better applicable in practice than existing
    approaches."
author:
- first_name: Stefan Balthasar
  full_name: Schneider, Stefan Balthasar
  id: '35343'
  last_name: Schneider
  orcid: 0000-0001-8210-4011
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
- first_name: Ramin
  full_name: Khalili, Ramin
  last_name: Khalili
- first_name: Artur
  full_name: Hecker, Artur
  last_name: Hecker
citation:
  ama: 'Schneider SB, Karl H, Khalili R, Hecker A. <i>DeepCoMP: Coordinated Multipoint
    Using Multi-Agent Deep Reinforcement Learning</i>.; 2021.'
  apa: 'Schneider, S. B., Karl, H., Khalili, R., &#38; Hecker, A. (2021). <i>DeepCoMP:
    Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>.'
  bibtex: '@book{Schneider_Karl_Khalili_Hecker_2021, title={DeepCoMP: Coordinated
    Multipoint Using Multi-Agent Deep Reinforcement Learning}, author={Schneider,
    Stefan Balthasar and Karl, Holger and Khalili, Ramin and Hecker, Artur}, year={2021}
    }'
  chicago: 'Schneider, Stefan Balthasar, Holger Karl, Ramin Khalili, and Artur Hecker.
    <i>DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>,
    2021.'
  ieee: 'S. B. Schneider, H. Karl, R. Khalili, and A. Hecker, <i>DeepCoMP: Coordinated
    Multipoint Using Multi-Agent Deep Reinforcement Learning</i>. 2021.'
  mla: 'Schneider, Stefan Balthasar, et al. <i>DeepCoMP: Coordinated Multipoint Using
    Multi-Agent Deep Reinforcement Learning</i>. 2021.'
  short: 'S.B. Schneider, H. Karl, R. Khalili, A. Hecker, DeepCoMP: Coordinated Multipoint
    Using Multi-Agent Deep Reinforcement Learning, 2021.'
date_created: 2022-10-20T16:44:19Z
date_updated: 2022-11-18T09:59:27Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
  content_type: application/pdf
  creator: stschn
  date_created: 2022-10-20T16:41:10Z
  date_updated: 2022-10-20T16:41:10Z
  file_id: '33855'
  file_name: preprint.pdf
  file_size: 2521656
  relation: main_file
file_date_updated: 2022-10-20T16:41:10Z
has_accepted_license: '1'
keyword:
- mobility management
- coordinated multipoint
- CoMP
- cell selection
- resource management
- reinforcement learning
- multi agent
- MARL
- self-learning
- self-adaptation
- QoE
language:
- iso: eng
oa: '1'
project:
- _id: '4'
  name: 'SFB 901 - C: SFB 901 - Project Area C'
- _id: '16'
  name: 'SFB 901 - C4: SFB 901 - Subproject C4'
- _id: '1'
  name: 'SFB 901: SFB 901'
status: public
title: 'DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning'
type: working_paper
user_id: '477'
year: '2021'
...