Resource Allocation for Positive-Rate Covert Communications Using Optimization and Deep Reinforcement Learning
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917320036712448 |
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| author | Zhang, Yubo ZivariFard, Hassan Wang, Xiaodong |
| author_facet | Zhang, Yubo ZivariFard, Hassan Wang, Xiaodong |
| contents | We aim to achieve keyless covert communication with a positive-rate in Rayleigh block-fading channels. Specifically, the transmitter and the legitimate receiver are assumed to have either causal or non-causal knowledge of the \ac{CSI} for both the legitimate and the warden channels, while the warden only knows the statistical distribution of the \ac{CSI}. Two problem formulations are considered in this work: (a) Power allocation: maximizing the sum covert rate subject to a maximum power constraint, and (b) Rate allocation: minimizing the power consumption subject to a minimum covert rate constraint. Both problems are formulated based on recent information theoretical results on covert communication over state-dependent channels. When the \ac{CSI} of each fading block is known non-causally, we propose a novel three-step method to solve both the power and rate allocation problems. In the case where the \ac{CSI} is known causally, the power allocation problem can be formulated as \ac{MDP} and be solved using a \ac{DDQN} approach. Although the rate allocation problem under causal \ac{CSI} does not directly conform to an \ac{MDP} structure, it can be approximately solved using the \ac{DDQN} trained for power allocation. Simulation results demonstrate the effectiveness of the proposed power and rate allocation methods and provide comprehensive performance comparisons across different allocation schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13555 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Resource Allocation for Positive-Rate Covert Communications Using Optimization and Deep Reinforcement Learning Zhang, Yubo ZivariFard, Hassan Wang, Xiaodong Information Theory Signal Processing We aim to achieve keyless covert communication with a positive-rate in Rayleigh block-fading channels. Specifically, the transmitter and the legitimate receiver are assumed to have either causal or non-causal knowledge of the \ac{CSI} for both the legitimate and the warden channels, while the warden only knows the statistical distribution of the \ac{CSI}. Two problem formulations are considered in this work: (a) Power allocation: maximizing the sum covert rate subject to a maximum power constraint, and (b) Rate allocation: minimizing the power consumption subject to a minimum covert rate constraint. Both problems are formulated based on recent information theoretical results on covert communication over state-dependent channels. When the \ac{CSI} of each fading block is known non-causally, we propose a novel three-step method to solve both the power and rate allocation problems. In the case where the \ac{CSI} is known causally, the power allocation problem can be formulated as \ac{MDP} and be solved using a \ac{DDQN} approach. Although the rate allocation problem under causal \ac{CSI} does not directly conform to an \ac{MDP} structure, it can be approximately solved using the \ac{DDQN} trained for power allocation. Simulation results demonstrate the effectiveness of the proposed power and rate allocation methods and provide comprehensive performance comparisons across different allocation schemes. |
| title | Resource Allocation for Positive-Rate Covert Communications Using Optimization and Deep Reinforcement Learning |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2508.13555 |