Shadow Wireless Intelligence: Large Language Model-Driven Reasoning in Covert Communications

TL;DR

This paper introduces Shadow Wireless Intelligence (SWI), a novel approach that leverages large language models with retrieval-augmented generation to enable adaptive, real-time decision-making in covert wireless communications for 6G networks.

Contribution

The paper presents a new framework integrating LLMs with domain knowledge retrieval for intelligent, adaptive covert communication strategies, surpassing traditional optimization methods.

Findings

Achieved 85% symbolic derivation accuracy in CC scenarios.

Attained 94% correctness in simulation code generation.

Demonstrated robustness and adaptability of SWI in dynamic environments.

Abstract

Covert Communications (CC) can secure sensitive transmissions in industrial, military, and mission-critical applications within 6G wireless networks. However, traditional optimization methods based on Artificial Noise (AN), power control, and channel manipulation might not adapt to dynamic and adversarial environments due to the high dimensionality, nonlinearity, and stringent real-time covertness requirements. To bridge this gap, we introduce Shadow Wireless Intelligence (SWI), which integrates the reasoning capabilities of Large Language Models (LLMs) with retrieval-augmented generation to enable intelligent decision-making in covert wireless systems. Specifically, we utilize DeepSeek-R1, a mixture-of-experts-based LLM with RL-enhanced reasoning, combined with real-time retrieval of domain-specific knowledge to improve context accuracy and mitigate hallucinations. Our approach develops a structured CC knowledge base, supports context-aware retrieval, and performs semantic optimization, allowing LLMs to generate and adapt CC strategies in real time. In a case study on optimizing AN power in a full-duplex CC scenario, DeepSeek-R1 achieves 85% symbolic derivation accuracy and 94% correctness in the generation of simulation code, outperforming baseline models. These results validate SWI as a robust, interpretable, and adaptive foundation for LLM-driven intelligent covert wireless systems in 6G networks.