Multi-User Covert Communications via Intelligent Spectrum Control

TL;DR

This paper presents an AI-driven spectrum control scheme for multi-user covert communications that enhances covertness, reliability, and capacity by dynamically avoiding interference and optimizing transmission parameters.

Contribution

It introduces an intelligent spectrum control method combining spectrum sensing and AI for improved multi-user covert communication performance.

Findings

Achieves higher detection error probability (DEP) for eavesdroppers.

Increases reliable transmission probability (RTP) for legitimate users.

Enables greater multi-user capacity under covertness constraints.

Abstract

This paper investigates the performance of multi-user covert communications over a fixed bandwidth in a multi-cell scenario with both eavesdroppers and malicious jammers. We propose an intelligent spectrum control (ISC) scheme that combines high-accuracy spectrum sensing with AI-assisted real-time decision-making to generate time-frequency dynamic occupation patterns for multiple legitimate users. The scheme can proactively avoid external interference and intra-system co-channel collisions, thereby improving covertness and reliability. Within this framework, we derive closed-form expressions for the detection error probability (DEP) of the eavesdropper and the reliable transmission probability (RTP) of legitimate users under multi-user joint detection. We then analytically optimize the transmission power that can maximize the covert rate (CR), as well as the maximum number of users that can access the system covertly and concurrently under given covertness and reliability constraints. Simulation results confirm the tight match between the analytical and Monte Carlo curves, and show that the proposed scheme can achieve a higher DEP, a larger RTP, and a greater multi-user capacity than the benchmark scheme.