Wireless Resource Allocation with Collaborative Distributed and Centralized DRL under Control Channel Attacks
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915023095332864 |
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| author | Wang, Ke Liu, Wanchun Lim, Teng Joon |
| author_facet | Wang, Ke Liu, Wanchun Lim, Teng Joon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10702 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Wireless Resource Allocation with Collaborative Distributed and Centralized DRL under Control Channel Attacks Wang, Ke Liu, Wanchun Lim, Teng Joon Information Theory Machine Learning Systems and Control Signal Processing 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. |
| title | Wireless Resource Allocation with Collaborative Distributed and Centralized DRL under Control Channel Attacks |
| topic | Information Theory Machine Learning Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2411.10702 |