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

TL;DR

This paper introduces a novel collaborative deep reinforcement learning approach for wireless resource allocation in cyber-physical systems, effectively mitigating control channel DoS attacks by combining centralized and distributed decision-making.

Contribution

The paper presents a new CDC-DRL algorithm that integrates centralized and distributed strategies, improving robustness against control channel attacks in large-scale CPSs.

Findings

CDC-DRL outperforms existing DRL methods in simulations.

The approach effectively mitigates DoS attacks on control channels.

Enhanced resource allocation stability under attack conditions.

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.