Regularized Diffusion-based Contract Model for Covert Semantic Entropy Control in LAENets

2026-03-02

AI Summary

This work addresses the challenge of efficiently transmitting task-oriented semantic information in low-altitude economic networks under stringent low-probability-of-detection constraints, where balancing semantic abstraction to avoid excessive information loss or redundancy remains difficult. To this end, the paper proposes a task-aware covert semantic communication mechanism that, for the first time, integrates prospect theory into semantic communication service modeling. By combining diffusion entropy and action entropy regularization, an information-asymmetric contract-theoretic framework is formulated, and a Regularized Diffusion Soft Actor-Critic (RDSAC) algorithm is developed to derive optimal contracts. The proposed approach effectively balances semantic fidelity and transmission efficiency, significantly enhancing task reliability and communication performance even under extreme covertness requirements.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Information Theory
• Game Theory and Economic Paradigms: Imperfect Information

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Security and Privacy: Data transparency and provenance

Abstract

Low-Altitude Economy Networks (LAENets) have emerged as a critical communication paradigm for operation-critical and regulation-aware applications, where Unmanned Aerial Vehicles (UAVs) transmit task-related information under stringent low-probability-of-detection constraints. These constraints severely limit the available transmission power and bandwidth, rendering conventional bit-level communication inefficient when task performance depends on high-level semantic understanding rather than raw data fidelity. Fortunately, Semantic Communication (SemCom) can be a promising solution by prioritizing task-relevant information over bit-level accuracy. However, different levels of semantic abstraction inherently introduce different degrees of information loss and redundancy, which may either compromise task reliability or incur excessive transmission overhead if not properly controlled. To this end, we propose an incentive-aware semantic entropy control framework for covert communications in LAENets. Specifically, we regulate semantic uncertainty at the receiver by adjusting the semantic abstraction level at the UAV side, thereby enabling reliable task information delivery under extreme covert constraints. Since the Base Station (BS) cannot directly observe the semantic processing capabilities and abstraction-dependent transmission costs of UAVs, information asymmetry naturally arises in SemCom service provision. Accordingly, we propose a contract theoretic model, where we adopt Prospect Theory (PT) to capture the subjective utility of the BS toward personalized semantic services. Furthermore, we design a Regularized Diffusion-based Soft Actor-Critic (RDSAC) algorithm for optimal contract design under PT. This algorithm enhances contract design by introducing diffusion entropy regularization together with action entropy regularization.

Problem

• Semantic Communication
• Covert Communication
• Information Asymmetry
• Semantic Entropy
• Low-Altitude Economy Networks

Innovation

• Semantic Communication
• Contract Theory
• Prospect Theory
• Diffusion Entropy Regularization
• Covert Communication