8 citations · 37 across the 28 of their papers we have counts for
12 papers · 1 filter
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…