Robust Deep Reinforcement Learning Based Network Slicing under Adversarial Jamming Attacks

Feng Wang, M. Cenk Gursoy, Senem Velipasalar, Yalin E. Sagduyu

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

Abstract

In this paper, we first present a deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment. We propose three different deep RL algorithms, namely actor-critic, deep Q learning (DQN), and soft DQN, to select slices from the best recorded subset which is updated over time to adapt to the dynamic environment. We evaluate the performances of the proposed deep RL agents for network slicing and provide comparisons. Subsequently, we design intelligent jammers also as deep RL agents that significantly degrade the user's sum reward. Finally, we propose effective defensive measures to mitigate jamming attacks by determining the proper time instants to retrain the network slicing policy. Via simulations, we quantify the improvements in the performance with the defensive retraining.

Original languageEnglish (US)
Title of host publication2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages752-757
Number of pages6
ISBN (Electronic)9781665480536
DOIs
StatePublished - 2022
Event33rd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022 - Virtual, Online, Japan
Duration: Sep 12 2022Sep 15 2022

Publication series

NameIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
Volume2022-September

Conference

Conference33rd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022
Country/TerritoryJapan
CityVirtual, Online
Period9/12/229/15/22

Keywords

  • deep reinforcement learning
  • dynamic channel access
  • jamming attacks
  • Network slicing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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