41 citations · 229 across the 71 of their papers we have counts for
25 papers · 1 filter
BALANCE: Hybrid Autoregressive-Speculative LLM Inference at the Network Edge
Guanqiao Qu, Shuo Chen, Qian Chen +2
Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference mainly relies on two approaches: A…
Update the Unseen Only: Minimizing AoI for Collaborative Perception through Online Learning
Yanan Ma, Zhuoyi Zhao, Zhengru Fang +3
While collaborative perception (CP) enhances the safety of autonomous driving, limited bandwidth can cause severe shared data staleness in CP systems. Existing age-of-information (…
Collaborative Air-Ground Sensing, Communication, Computing, Storage, and Intelligence for Low-Altitude Economy
Yiqin Deng, Junhui Gao, Zihan Fang +3
Low-altitude economy (LAE) is transforming low-altitude airspace into a new cyber-physical infrastructure. Although air-ground communications have been widely studied, LAE is funda…
SpaceMoE: Towards Orbital General Intelligence with Distributed Mixture-of-Experts Inference
Qian Chen, Xianhao Chen, Min Sheng +1
As satellite networks evolve to support increasingly diverse services and artificial general intelligence (AGI), large language models (LLMs) are emerging as a critical foundation…
Birdcast: Interest-aware BEV Multicasting for Infrastructure-assisted Collaborative Perception
Yanan Ma, Zhengru Fang, Yihang Tao +4
Vehicle-to-infrastructure collaborative perception (V2I-CP) leverages a high-vantage node to transmit supplementary information, i.e., bird's-eye-view (BEV) feature maps, to vehicl…
SplitCom: Communication-efficient Split Federated Fine-tuning of LLMs via Temporal Compression
Tao Li, Yulin Tang, Yiyang Song +4
Federated fine-tuning of on-device large language models (LLMs) mitigates privacy concerns by preventing raw data sharing. However, the intensive computational and memory demands p…
SLIDE: Simultaneous Model Downloading and Inference at the Wireless Network Edge
Guanqiao Qu, Tao Li, Qian Chen +2
To support on-device inference, the next-generation mobile networks are expected to support real-time model downloading services to mobile users. However, powerful AI models typica…
Optimizing Split Federated Learning with Unstable Client Participation
Wei Wei, Zheng Lin, Xihui Liu +3
To enable training of large artificial intelligence (AI) models at the network edge, split federated learning (SFL) has emerged as a promising approach by distributing computation…
Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoring
Zihan Fang, Zheng Lin, Senkang Hu +4
Outdoor health monitoring is essential to detect early abnormal health status for safeguarding human health and safety. Conventional outdoor monitoring relies on static multimodal…
Pipelining Split Learning in Multi-hop Edge Networks
Wei Wei, Zheng Lin, Tao Li +2
To support large-scale model training, split learning (SL) enables multiple edge devices/servers to share the intensive training workload. However, most existing works on SL focus…
RAISE: Optimizing RIS Placement to Maximize Task Throughput in Multi-Server Vehicular Edge Computing
Yanan Ma, Zhengru Fang, Longzhi Yuan +3
Given the limited computing capabilities on autonomous vehicles, onboard processing of large volumes of latency-sensitive tasks presents significant challenges. While vehicular edg…
PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inference
Guanqiao Qu, Qian Chen, Xianhao Chen +2
By provisioning inference offloading services, edge inference drives the rapid growth of AI applications at network edge. However, how to reduce the inference latency remains a sig…
Space-ground Fluid AI for 6G Edge Intelligence
Qian Chen, Zhanwei Wang, Xianhao Chen +5
Edge artificial intelligence (AI) and space-ground integrated networks (SGINs) are two main usage scenarios of the sixth-generation (6G) mobile networks. Edge AI supports pervasive…
R-ACP: Real-Time Adaptive Collaborative Perception Leveraging Robust Task-Oriented Communications
Zhengru Fang, Jingjing Wang, Yanan Ma +4
Collaborative perception enhances sensing in multirobot and vehicular networks by fusing information from multiple agents, improving perception accuracy and sensing range. However,…
Resources on the Move for Smart City: A Disruptive Perspective on the Grand Convergence of Sensing, Communications, Computing, Storage, and Intelligence
Yuguang Fang, Yiqin Deng, Xianhao Chen
The most commonly seen things on streets in any city are vehicles. However, most of them are used to transport people or goods. What if they also carry resources and capabilities f…
Prioritized Information Bottleneck Theoretic Framework with Distributed Online Learning for Edge Video Analytics
Zhengru Fang, Senkang Hu, Jingjing Wang +3
Collaborative perception systems leverage multiple edge devices, such surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite their a…
PIB: Prioritized Information Bottleneck Framework for Collaborative Edge Video Analytics
Zhengru Fang, Senkang Hu, Liyan Yang +3
Collaborative edge sensing systems, particularly in collaborative perception systems in autonomous driving, can significantly enhance tracking accuracy and reduce blind spots with…
Mobile Edge Intelligence for Large Language Models: A Contemporary Survey
Guanqiao Qu, Qiyuan Chen, Wei Wei +3
On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest since they are more cost-effective, latency-efficient, and priv…
Multi-hop Multi-RIS Wireless Communication Systems: Multi-reflection Path Scheduling and Beamforming
Xiaoyan Ma, Haixia Zhang, Xianhao Chen +1
Reconfigurable intelligent surface (RIS) provides a promising way to proactively augment propagation environments for better transmission performance in wireless communications. Ex…
TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networks
Guanqiao Qu, Zheng Lin, Fangming Liu +2
Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end user…
TrimCaching: Parameter-sharing Edge Caching for AI Model Downloading
Guanqiao Qu, Zheng Lin, Qian Chen +4
Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end user…
PACP: Priority-Aware Collaborative Perception for Connected and Autonomous Vehicles
Zhengru Fang, Senkang Hu, Haonan An +5
Surrounding perceptions are quintessential for safe driving for connected and autonomous vehicles (CAVs), where the Bird's Eye View has been employed to accurately capture spatial…
Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities
Xianhao Chen, Yiqin Deng, Haichuan Ding +4
Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of ap…
Actions at the Edge: Jointly Optimizing the Resources in Multi-access Edge Computing
Yiqin Deng, Xianhao Chen, Guangyu Zhu +3
Multi-access edge computing (MEC) is an emerging paradigm that pushes resources for sensing, communications, computing, storage and intelligence (SCCSI) to the premises closer to t…