Mi, Q;
Yang, N;
Zhang, H;
Zhang, H;
Wang, J;
(2022)
Joint Caching and Transmission in the Mobile Edge Network: An Multi-Agent Learning Approach.
In:
2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings.
IEEE: Madrid, Spain.
Preview |
PDF
2205.10750v1.pdf - Accepted Version Download (2MB) | Preview |
Abstract
Joint caching and transmission optimization problem is challenging due to the deep coupling between decisions. This paper proposes an iterative distributed multi-agent learning approach to jointly optimize caching and transmission. The goal of this approach is to minimize the total transmission delay of all users. In this iterative approach, each iteration includes caching optimization and transmission optimization. A multi-agent reinforcement learning (MARL)-based caching network is developed to cache popular tasks, such as answering which files to evict from the cache and which files to storage. Based on the cached files of the caching network, the transmission network transmits cached files for users by single transmission (ST) or joint transmission (JT) with multi-agent Bayesian learning automaton (MABLA) method. And then users access the edge servers with the minimum transmission delay. The experimental results demonstrate the performance of the proposed multi-agent learning approach.
| Type: | Proceedings paper |
|---|---|
| Title: | Joint Caching and Transmission in the Mobile Edge Network: An Multi-Agent Learning Approach |
| Event: | 2021 IEEE Global Communications Conference (GLOBECOM) |
| Dates: | 7 Dec 2021 - 11 Dec 2021 |
| ISBN-13: | 9781728181042 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/GLOBECOM46510.2021.9685590 |
| Publisher version: | http://dx.doi.org/10.1109/globecom46510.2021.96855... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Learning automata, Simulation, Reinforcement learning, Stability analysis, Delays, Servers,Iterative methods |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10194865 |
Archive Staff Only
 |
View Item |
