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

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.