Abstract
We present a cell-free massive MIMO-enabled mo-edge network with the aim of meeting the stringent rements of the newly introduced multimedia services. For considered framework, we propose a distributed deep-orcement learning (DRL)-based joint communication and uting resource allocation wherein each user is implemented n independent agent to make joint resource allocation ion relying on local observation only. The simulation results nstrate that the agents learn robust policies that reduce gy consumption while attaining the ultra-low delay requires of the advanced services.
Original language | English |
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Title of host publication | 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 344-346 |
Number of pages | 3 |
ISBN (Electronic) | 9781728176383 |
DOIs | |
Publication status | Published - 2021 Apr 13 |
Event | 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 - Jeju Island, Korea, Republic of Duration: 2021 Apr 13 → 2021 Apr 16 |
Publication series
Name | 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 |
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Conference
Conference | 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 |
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Country/Territory | Korea, Republic of |
City | Jeju Island |
Period | 21/4/13 → 21/4/16 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- cell-free massive MIMO
- distributive deep reinforcementing
- joint communication omputing resource allocation
- mobile edge network
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Information Systems
- Information Systems and Management