Wireless Resource Allocation with Collaborative Distributed and Centralized DRL under Control Channel Attacks

• URL: http://arxiv.org/abs/2411.10702v1
• Date: Sat, 16 Nov 2024 04:56:23 GMT
• Title: Wireless Resource Allocation with Collaborative Distributed and Centralized DRL under Control Channel Attacks
• Authors: Ke Wang, Wanchun Liu, Teng Joon Lim,
• Abstract summary: We consider a wireless resource allocation problem in a cyber-physical system (CPS) where the control channel is subjected to denial-of-service (DoS) attacks. We propose a novel concept of collaborative distributed and centralized (CDC) resource allocation to effectively mitigate the impact of these attacks. We develop a new CDC-deep reinforcement learning (DRL) algorithm, whereas existing DRL frameworks only formulate either centralized or distributed decision-making problems.
• Score: 9.981962772130025
• License:
• Abstract: In this paper, we consider a wireless resource allocation problem in a cyber-physical system (CPS) where the control channel, carrying resource allocation commands, is subjected to denial-of-service (DoS) attacks. We propose a novel concept of collaborative distributed and centralized (CDC) resource allocation to effectively mitigate the impact of these attacks. To optimize the CDC resource allocation policy, we develop a new CDC-deep reinforcement learning (DRL) algorithm, whereas existing DRL frameworks only formulate either centralized or distributed decision-making problems. Simulation results demonstrate that the CDC-DRL algorithm significantly outperforms state-of-the-art DRL benchmarks, showcasing its ability to address resource allocation problems in large-scale CPSs under control channel attacks.

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