23 papers
Jointly Optimizing Memory, Computing and Offloading for Energy-Efficient DNN Inference
Yunchu Han, Zhaojun Nan, Sheng Zhou +1
In edge intelligence systems, computation offloading and dynamic voltage and frequency scaling (DVFS) are widely used to improve energy efficiency. However, as applications like de…
FedPGT: Progressive Gradient Transmission for Vehicular Federated Learning over Time-Varying Channels
Jintao Yan, Tan Chen, Yuxuan Sun +2
Vehicular federated learning (VFL) enables privacy-preserving collaborative model training for intelligent transportation systems, where communication resource allocation and gradi…
Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning
Yuxuan Sun, Yuxuan Bai, Tan Chen +2
Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it important for Internet of Things app…
Joint Optimization of Memory and Computing Frequency for Energy-Efficient DNN Inference
Yunchu Han, Zhaojun Nan, Sheng Zhou +1
Deep neural network (DNN) inference on mobile devices often incurs high latency and energy consumption due to limited computing and memory resources. To enable energy-efficient DNN…
DH-VLM: Dual-Horizon Cooperative Latent Reasoning for Autonomous Driving
Ziyi Song, Chen Xia, Hang Yu +2
Large-scale language models for autonomous driving enable enhanced global understanding and long-horizon planning. However, when deployed in isolated vehicles, limited sensing rang…
GeoFovea-GS: Geometry-Aware Cross-Layer Gaussian Splatting for Wireless Aerial VR
Zeyi Ren, Wencheng Yan, Jiawen Zhang +3
Wireless aerial virtual reality (VR) aims to provide immersive access to large-scale scenes, but high-resolution view generation and delivery are jointly constrained by limited ban…
TGPP: Trajectory-Guided Plug-and-Play Priors for Sparse Radio Map Reconstruction
Jiawen Zhang, Zhiyuan Jiang, Sheng Zhou +1
Radio map (RM) reconstruction is essential for environment-aware wireless networks, but practical measurements are often collected along mobility trajectories rather than randomly…
DiP-SD: Distributed Pipelined Speculative Decoding for Efficient LLM Inference at the Edge
Yaodan Xu, Sheng Zhou, Zhisheng Niu
Speculative decoding has emerged as a promising technique for large language model (LLM) inference by accelerating autoregressive decoding via draft-then-verify. This paper studies…
Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction
Zeyi Ren, Jialin Dong, Wei Zuo +4
Large-scale three-dimensional (3D) scene reconstruction in low-altitude intelligent networks (LAIN) demands highly efficient wireless image transmission. However, existing schemes…
UniMM-V2X: MoE-Enhanced Multi-Level Fusion for End-to-End Cooperative Autonomous Driving
Ziyi Song, Chen Xia, Chenbing Wang +3
Autonomous driving holds transformative potential but remains fundamentally constrained by the limited perception and isolated decision-making with standalone intelligence. While r…
Joint Memory Frequency and Computing Frequency Scaling for Energy-efficient DNN Inference
Yunchu Han, Zhaojun Nan, Sheng Zhou +1
Deep neural networks (DNNs) have been widely applied in diverse applications, but the problems of high latency and energy overhead are inevitable on resource-constrained devices. T…
FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Yuxuan Bai, Yuxuan Sun, Tan Chen +3
Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume…
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
Tan Chen, Jintao Yan, Yuxuan Sun +2
Federated learning (FL) is a promising paradigm for multiple devices to cooperatively train a model. When applied in wireless networks, two issues consistently affect the performan…
Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification
Jintao Yan, Tan Chen, Yuxuan Sun +3
Asynchronous Federated Learning (AFL) enables distributed model training across multiple mobile devices, allowing each device to independently update its local model without waitin…
Joint Optimization of Offloading, Batching and DVFS for Multiuser Co-Inference
Yaodan Xu, Sheng Zhou, Zhisheng Niu
With the growing integration of artificial intelligence in mobile applications, a substantial number of deep neural network (DNN) inference requests are generated daily by mobile d…
Robust DNN Partitioning and Resource Allocation Under Uncertain Inference Time
Zhaojun Nan, Yunchu Han, Sheng Zhou +1
In edge intelligence systems, deep neural network (DNN) partitioning and data offloading can provide real-time task inference for resource-constrained mobile devices. However, the…
DVFS-Aware DNN Inference on GPUs: Latency Modeling and Performance Analysis
Yunchu Han, Zhaojun Nan, Sheng Zhou +1
The rapid development of deep neural networks (DNNs) is inherently accompanied by the problem of high computational costs. To tackle this challenge, dynamic voltage frequency scali…
SMDP-Based Dynamic Batching for Improving Responsiveness and Energy Efficiency of Batch Services
Yaodan Xu, Sheng Zhou, Zhisheng Niu
For servers incorporating parallel computing resources, batching is a pivotal technique for providing efficient and economical services at scale. Parallel computing resources exhib…
AEPHORA: AI/ML-Based Energy-Efficient Proactive Handover and Resource Allocation
Bowen Xie, Sheng Zhou, Zhisheng Niu +2
Future Vehicle-to-Everything (V2X) scenarios require high-speed, low-latency, and ultra-reliable communication services, particularly for applications such as autonomous driving an…
DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model
Ruiqing Mao, Haotian Wu, Yukuan Jia +5
Collaborative perception (CP) is emerging as a promising solution to the inherent limitations of stand-alone intelligence. However, current wireless communication systems are unabl…
C-MASS: Combinatorial Mobility-Aware Sensor Scheduling for Collaborative Perception with Second-Order Topology Approximation
Yukuan Jia, Yuxuan Sun, Ruiqing Mao +3
Collaborative Perception (CP) has been a promising solution to address occlusions in the traffic environment by sharing sensor data among collaborative vehicles (CoV) via vehicle-t…
Task-Oriented Wireless Communications for Collaborative Perception in Intelligent Unmanned Systems
Sheng Zhou, Yukuan Jia, Ruiqing Mao +3
Collaborative Perception (CP) has shown great potential to achieve more holistic and reliable environmental perception in intelligent unmanned systems (IUSs). However, implementing…
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning
Jintao Yan, Tan Chen, Yuxuan Sun +3
Leveraging the computing and sensing capabilities of vehicles, vehicular federated learning (VFL) has been applied to edge training for connected vehicles. The dynamic and intercon…