Multi-User Covert Communications via Intelligent Spectrum Control

2026-01-06

arXiv.org

AI Summary

This work addresses the joint threat of eavesdroppers, malicious jammers, and co-channel collisions in multi-cell multi-user covert communication by proposing an intelligent spectrum control scheme that integrates high-precision spectrum sensing with AI-driven real-time decision-making to dynamically generate user-specific time-frequency occupancy patterns, thereby mitigating both internal and external interference. The approach innovatively combines AI-assisted dynamic spectrum allocation with multi-user joint detection theory, yielding—for the first time—closed-form expressions for the eavesdropper’s detection error probability and the legitimate users’ reliable transmission probability. These analytical results are leveraged to optimize the covert rate and the upper bound on concurrent users. Simulations confirm the theoretical analysis and demonstrate that the proposed method significantly outperforms baseline schemes in terms of detection error probability, reliability of transmission, and system concurrency capacity.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Privacy
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• covert communications
• multi-user
• eavesdroppers
• malicious jammers
• spectrum control

Innovation

• intelligent spectrum control
• covert communications
• multi-user joint detection
• AI-assisted decision-making
• detection error probability