Multi-Agent Reinforcement Learning with Pointer Networks for Network Slicing in Cellular Systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present a multi-agent deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment with multiple base stations. We first introduce the wireless network virtualization (WNV) and the interference channel model. Then, we formulate the network slicing problem in the dynamic environment in which fading varies, users have mobility, and requests are randomly generated over time. Subsequently, we propose a deep RL framework with multiple actors and centralized critic (MACC) to maximize the reward over all base stations instead of pursuing local optimization. The actors are implemented as pointer networks to fit the varying dimension of input. Finally, we evaluate the performance of the proposed deep RL algorithm via simulations to demonstrate its effectiveness.

Original languageEnglish (US)
Title of host publicationICC 2022 - IEEE International Conference on Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1841-1846
Number of pages6
ISBN (Electronic)9781538683477
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Communications, ICC 2022 - Seoul, Korea, Republic of
Duration: May 16 2022May 20 2022

Publication series

NameIEEE International Conference on Communications
Volume2022-May
ISSN (Print)1550-3607

Conference

Conference2022 IEEE International Conference on Communications, ICC 2022
Country/TerritoryKorea, Republic of
CitySeoul
Period5/16/225/20/22

Keywords

  • deep reinforcement learning
  • dynamic channel access
  • multi-agent actor-critic
  • Network slicing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Multi-Agent Reinforcement Learning with Pointer Networks for Network Slicing in Cellular Systems'. Together they form a unique fingerprint.

Cite this