卫星到设备的直接通信：从协作任务卸载到非协作接入监测
Direct Satellite-to-Device Communications: From Cooperative Task Offloading to Non-Cooperative Access Monitoring
- Beijing University of Posts and Telecommunications（北京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对6G非地面网络中DS2D通信的协作任务卸载与非协作接入监测挑战，提出集成D3QN优化卸载策略及Transformer模型实现信号检测与AMC的系统，仿真验证其延迟降低、检测概率及精度增益优势。
AI中文摘要：
卫星到设备的直接通信（DS2D）作为6G非地面网络中将泛在连接和边缘计算能力扩展到偏远及服务不足地区的变革性范式正在兴起。但实际部署面临两大关键挑战：一是协作场景中动态卫星信道条件（如严重多普勒频移、快衰落）与卫星计算资源受限；二是非协作场景中未授权卫星接入会带来重大频谱安全威胁。为应对这些挑战，本文提出一种支持协作任务卸载和非协作接入监测的多功能DS2D系统。针对协作DS2D通信，将信道估计模块与对决双深度Q网络（D3QN）集成，以动态优化任务卸载策略；针对非协作DS2D通信，提出基于Transformer的模型以实现盲信号检测和自动调制分类（AMC）。仿真结果显示：1）与静态关联策略相比，D3QN算法可将平均延迟降低多达225%；2）本文提出的信号检测模型对DS2D信号的平均存在检测概率达90.5%；3）所提AMC算法在不同信噪比（SNR）下均表现出优异性能，在低信噪比区域相比现有方法获得9.4%的精度增益。
英文摘要：
Direct satellite-to-device (DS2D) communication is emerging as a transformative paradigm for extending ubiquitous connectivity and edge computing capabilities to remote and underserved regions within 6G non-terrestrial networks. However, practical deployment faces dual critical challenges: i) dynamic satellite channel conditions (e.g., severe Doppler shifts, fast fading) and constrained satellite computing resources in cooperative scenarios; and ii) unauthorized satellite access introduces significant spectrum security threats in non-cooperative scenarios. To address these challenges, we propose a versatile DS2D system that supports cooperative task offloading and non-cooperative access monitoring. For cooperative DS2D communications, we integrate a channel estimation module with a dueling double deep Q-network (D3QN) to dynamically optimize task offloading strategy. For non-cooperative DS2D communications, we propose Transformer-based models to enable blind signal detection and automatic modulation classification (AMC). Simulation results show that: 1) The D3QN algorithm reduces average latency by up to 225\% compared to static association policies. 2) Our signal detection model achieves an average presence detection probability of 90.5\% for DS2D signals. 3) The proposed AMC algorithm achieves superior performance across different signal-to-noise ratios (SNRs), with a 9.4\% accuracy gain in low-SNR regimes compared to existing methods.
