Resource Allocation for Positive-Rate Covert Communications Using Optimization and Deep Reinforcement Learning

TL;DR

This paper develops optimization and deep reinforcement learning methods to enhance covert communication rates in Rayleigh fading channels, considering different CSI knowledge scenarios.

Contribution

It introduces a novel three-step optimization approach for non-causal CSI and applies DDQN-based reinforcement learning for causal CSI scenarios.

Findings

Proposed methods significantly improve covert communication rates.

Deep reinforcement learning effectively manages power and rate allocation.

Simulation results validate the effectiveness of the proposed schemes.

Abstract

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.