Large-Scale Beam Placement and Resource Allocation Design for MEO-Constellation SATCOM

用于卫星通信系统的实验室内载波聚合测试平台

In-Lab Carrier Aggregation Testbed for Satellite Communication Systems

本文设计并实现了一个基于SDR和卫星信道仿真器的实验室内载波聚合测试平台，通过网关与用户终端模块支持GEO、MEO及多轨道组合场景，实验验证了载波聚合在卫星通信系统中的性能优势。

低轨卫星通信中服务质量与恒模约束下的宽波束均匀矩形阵列系数设计

Broadened-beam Uniform Rectangular Array Coefficient Design in LEO SatComs Under Quality of Service and Constant Modulus Constraints

针对低轨卫星通信，提出联合考虑地球曲率路径损耗、恒模约束与波束外辐射抑制的宽波束均匀矩形阵列系数设计方法，经克罗内克积分解优化并验证其容量优势，适用于卫星广播。

无源大规模MIMO下行性能分析用于低轨卫星超大规模星座

Downlink Performance of Cell-Free Massive MIMO for LEO Satellite Mega-Constellation

本文研究了低轨卫星超大规模星座中无源大规模MIMO的下行性能，通过随机几何学工具建模并分析了覆盖概率，发现视距效应和服务范围对覆盖概率有显著影响。

非地面网络中的联邦学习：设计、架构与挑战

Federated Learning in NTNs: Design, Architecture and Challenges

本文提出一种基于HAPS星座的非地面网络分层联邦学习框架，融合LEO卫星与地面客户端训练，并利用GEO/MEO卫星中继实现全球模型交换，以提升隐私、精度、可扩展性并优化网络管理。

LEO-to-User Assignment and Resource Allocation for Uplink Transmit Power Minimization

This paper aims to develop satellite-user association and resource allocation mechanisms to minimize the total transmit power for integrated terrestrial and non-terrestrial networks wherein a constellation of LEO satellites provides the radio access services to both terrestrial base stations (BSs) and the satellite-enabled users (SUEs). In this work, beside maintaining the traditional SatCom connection for SUEs, the LEO satellites provide backhaul links to the BSs to upload the data received from their ground customers. Taking the individual SUE traffic demands and the aggregated BS demands, w…

面向多轨道卫星网络中联合切换管理与功率分配的多智能体强化学习

Multi-Agent Reinforcement Learning for Joint Handover Management and Power Allocation in Multi-Orbit Satellite Networks

针对多轨道卫星网络的联合切换管理与功率分配问题，提出结合MAPPO与TarMAC的多智能体强化学习策略，在保证接近贪心方案吞吐量的同时大幅减少切换次数，性能优于仅低轨方案与保守停留策略。