Wireless Resource Allocation with Collaborative Distributed and Centralized DRL under Control Channel Attacks
This addresses resource allocation problems for large-scale cyber-physical systems under control channel attacks, representing an incremental improvement over existing DRL methods.
The paper tackles wireless resource allocation in cyber-physical systems under denial-of-service attacks on control channels by proposing a collaborative distributed and centralized approach, and it shows that their CDC-DRL algorithm significantly outperforms state-of-the-art DRL benchmarks in simulations.
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.