arXiv:2603.01478 · v2

Contract-Theoretic Semantic Abstraction for Covert Communication in LAENets

首次提交 2026-03-02 13:43

版本更新 2026-11-11 09:30

雷达观测 2026-11-13 11:11

摘要

In Low-Altitude Economy Networks (LAENets), Unmanned Aerial Vehicles (UAVs) transmit task-related information to the Base Station (BS) under stringent low-probability-of-detection constraints. These constraints limit transmission resources, hindering reliable information delivery through conventional bit-level communication. Semantic Communication (SemCom) addresses this challenge by prioritizing task-relevant information over bit-level accuracy. However, semantic abstraction must balance information loss and redundancy to preserve task reliability and transmission efficiency. Moreover, the BS cannot directly observe or reliably verify UAVs' semantic processing capabilities and abstraction-dependent transmission costs, leading to information asymmetry. We propose an incentive-aware semantic abstraction framework for covert communications in LAENets. Specifically, we adjust UAV-side semantic abstraction levels to provide differentiated covert semantic services under fixed-dimensional transmission payloads. To address information asymmetry, we develop a contract-theoretic model and adopt Prospect Theory (PT) to capture risk-sensitive semantic abstraction decisions under uncertainty. We further design a Regularized Diffusion-based Soft Actor-Critic (RDSAC) algorithm for optimal contract design under PT, incorporating diffusion entropy and action entropy regularization. Numerical results validate the proposed framework and show that RDSAC improves average rewards by 6.77% over SAC and 31.78% over proximal policy optimization.