TY - GEN
T1 - Robust Deep Reinforcement Learning Based Network Slicing under Adversarial Jamming Attacks
AU - Wang, Feng
AU - Gursoy, M. Cenk
AU - Velipasalar, Senem
AU - Sagduyu, Yalin E.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Network slicing
KW - deep reinforcement learning
KW - dynamic channel access
KW - jamming attacks
UR - http://www.scopus.com/inward/record.url?scp=85142818153&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142818153&partnerID=8YFLogxK
U2 - 10.1109/PIMRC54779.2022.9977913
DO - 10.1109/PIMRC54779.2022.9977913
M3 - Conference contribution
AN - SCOPUS:85142818153
T3 - IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
SP - 752
EP - 757
BT - 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022
Y2 - 12 September 2022 through 15 September 2022
ER -