8 citations · 37 across the 28 of their papers we have counts for
28 papers
Aidos: A Hybrid Optimization Algorithm for Beam Hopping Scheduling in NGSO Mega-Constellations
Lingkai Zhao, Zhe Chen, Kun Qiu +1
With the rapid proliferation of non-geostationary orbit (NGSO) mega-constellations, beam hopping (BH) has become indispensable for resource scheduling in multi-satellite, multi-cov…
Agon: A Semi-Supervised Framework for Robust Satellite Interference Detection
Boyu Yang, Chunyu Yang, Zhe Chen +2
The rapid expansion of non-geostationary orbit (NGSO) satellites alongside existing geostationary orbit (GSO) systems has intensified spectrum congestion and inter-system interfere…
GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems
Zheng Lin, Ons Aouedi, Zihan Fang +4
The increasing complexity of neural networks poses significant challenges for democratizing federated learning (FL) on resource-constrained edge devices. Parallel split learning (P…
Eunomia: A Multicontroller Domain Partitioning Framework in Hierarchical Satellite Network
Qi Zhang, Kun Qiu, Zhe Chen +4
With the rise of mega-satellite constellations, the integration of hierarchical non-terrestrial and terrestrial networks has become a cornerstone of 6G coverage enhancements. In th…
Tyche: A Hybrid Computation Framework of Illumination Pattern for Satellite Beam Hopping
Ziheng Yang, Kun Qiu, Zhe Chen +2
High-Throughput Satellites (HTS) use beam hopping to handle non-uniform and time-varying ground traffic demand. A significant technical challenge in beam hopping is the computation…
LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systems
Tianyang Duan, Zongyuan Zhang, Zheng Lin +10
Multi-agent reinforcement learning (MARL) has been increasingly adopted in many real-world applications. While MARL enables decentralized deployment on resource-constrained edge de…
Enabling Near-realtime Remote Sensing via Satellite-Ground Collaboration of Large Vision-Language Models
Zihan Li, Jiahao Yang, Yuxin Zhang +2
Large vision-language models (LVLMs) have recently demonstrated great potential in remote sensing (RS) tasks (e.g., disaster monitoring) conducted by low Earth orbit (LEO) satellit…
HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
Zheng Lin, Zhe Chen, Xianhao Chen +2
Split federated learning (SFL) has emerged as a promising paradigm to democratize machine learning (ML) on edge devices by enabling layer-wise model partitioning. However, existing…
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
Zheng Lin, Yuxin Zhang, Zhe Chen +6
Recently, large language models (LLMs) have achieved remarkable breakthroughs, revolutionizing the natural language processing domain and beyond. Due to immense parameter sizes, fi…
SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework
Jiasheng Wu, Jingjing Zhang, Zheng Lin +4
Recently, the rapid development of LEO satellite networks spurs another widespread concern-data processing at satellites. However, achieving efficient computation at LEO satellites…
SigChord: Sniffing Wide Non-sparse Multiband Signals for Terrestrial and Non-terrestrial Wireless Networks
Jinbo Peng, Junwen Duan, Zheng Lin +3
While unencrypted information inspection in physical layer (e.g., open headers) can provide deep insights for optimizing wireless networks, the state-of-the-art (SOTA) methods heav…
Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks
Zongyuan Zhang, Tianyang Duan, Zheng Lin +8
Deep reinforcement learning (DRL) has emerged as a promising approach for robotic control, but its realworld deployment remains challenging due to its vulnerability to environmenta…
Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective
Tianyang Duan, Zongyuan Zhang, Zheng Lin +7
Deep Reinforcement Learning (DRL) suffers from uncertainties and inaccuracies in the observation signal in realworld applications. Adversarial attack is an effective method for eva…
Constructing 4D Radio Map in LEO Satellite Networks with Limited Samples
Haoxuan Yuan, Zhe Chen, Zheng Lin +6
Recently, Low Earth Orbit (LEO) satellite networks (i.e., non-terrestrial network (NTN)), such as Starlink, have been successfully deployed to provide broader coverage than terrest…
LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networks
Zheng Lin, Yuxin Zhang, Zhe Chen +5
Recently, the increasing deployment of LEO satellite systems has enabled various space analytics (e.g., crop and climate monitoring), which heavily relies on the advancements in de…
Hierarchical Split Federated Learning: Convergence Analysis and System Optimization
Zheng Lin, Wei Wei, Zhe Chen +4
As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, split federated le…
MERIT: Multimodal Wearable Vital Sign Waveform Monitoring
Yongyang Tang, Zhe Chen, Ang Li +6
Cardiovascular disease (CVD) is the leading cause of death and premature mortality worldwide, with occupational environments significantly influencing CVD risk, underscoring the ne…
Efficient Satellite-Ground Interconnection Design for Low-orbit Mega-Constellation Topology
Wenhao Liu, Jiazhi Wu, Quanwei Lin +5
The low-orbit mega-constellation network (LMCN) is an important part of the space-air-ground integrated network system. An effective satellite-ground interconnection design can res…
SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework
Yuxin Zhang, Zheng Lin, Zhe Chen +5
Traditional federated learning (FL) frameworks rely heavily on terrestrial networks, where coverage limitations and increasing bandwidth congestion significantly hinder model conve…
SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
Zheng Lin, Xuanjie Hu, Yuxin Zhang +6
The scalability of large language models (LLMs) in handling high-complexity models and large-scale datasets has led to tremendous successes in pivotal domains. While there is an ur…
LEO Satellite Networks Assisted Geo-distributed Data Processing
Zhiyuan Zhao, Zhe Chen, Zheng Lin +4
Nowadays, the increasing deployment of edge clouds globally provides users with low-latency services. However, connecting an edge cloud to a core cloud via optic cables in terrestr…
Hurry: Dynamic Collaborative Framework For Low-orbit Mega-Constellation Data Downloading
Handong Luo, Wenhao Liu, Qi Zhang +6
Low-orbit mega-constellation network, which utilize thousands of satellites to provide a variety of network services and collect a wide range of space information, is a rapidly gro…
Sums: Sniffing Unknown Multiband Signals under Low Sampling Rates
Jinbo Peng, Zhe Chen, Zheng Lin +7
Due to sophisticated deployments of all kinds of wireless networks (e.g., 5G, Wi-Fi, Bluetooth, LEO satellite, etc.), multiband signals distribute in a large bandwidth (e.g., from…
SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensing
Haoxuan Yuan, Zhe Chen, Zheng Lin +5
Low Earth Orbit satellite Internet has recently been deployed, providing worldwide service with non-terrestrial networks. With the large-scale deployment of both non-terrestrial an…