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When Bayesian Tensor Completion Meets Multioutput Gaussian Processes: Functional Universality and Rank Learning
Siyuan Li, Shikai Fang, Lei Cheng +4
Functional tensor decomposition can analyze multi-dimensional data with real-valued indices, paving the path for applications in machine learning and signal processing. A limitatio…
Balancing Latency and Model Accuracy for Fluid Antenna-Assisted LM-Embedded MIMO Network
Yichen Jin, Zongze Li, Zeyi Ren +2
This paper addresses the challenge of large model (LM)-embedded wireless network for handling the trade-off problem of model accuracy and network latency. To guarantee a high-quali…
Adverse Weather-Independent Framework Towards Autonomous Driving Perception through Temporal Correlation and Unfolded Regularization
Wei-Bin Kou, Guangxu Zhu, Rongguang Ye +5
Various adverse weather conditions such as fog and rain pose a significant challenge to autonomous driving (AD) perception tasks like semantic segmentation, object detection, etc.…
FieldFormer: Self-supervised Reconstruction of Physical Fields via Tensor Attention Prior
Panqi Chen, Siyuan Li, Lei Cheng +3
Reconstructing physical field tensors from \textit{in situ} observations, such as radio maps and ocean sound speed fields, is crucial for enabling environment-aware decision making…
Distributed Activity Detection for Cell-Free Hybrid Near-Far Field Communications
Jingreng Lei, Yang Li, Zeyi Ren +4
A great amount of endeavor has recently been devoted to activity detection for massive machine-type communications in cell-free massive MIMO. However, in practice, as the number of…
Deep Unfolding with Kernel-based Quantization in MIMO Detection
Zeyi Ren, Jingreng Lei, Yichen Jin +5
The development of edge computing places critical demands on energy-efficient model deployment for multiple-input multiple-output (MIMO) detection tasks. Deploying deep unfolding m…
iMacHSR: Intermediate Multi-Access Heterogeneous Supervision and Regularization Scheme Toward Architecture-Agnostic Training
Wei-Bin Kou, Guangxu Zhu, Yichen Jin +4
While deep supervision is a powerful training strategy by supervising intermediate layers with auxiliary losses, it faces three underexplored problems: (I) Existing deep supervisio…
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving
Wei-Bin Kou, Guangxu Zhu, Bingyang Cheng +3
Street Scene Semantic Understanding (denoted as S3U) is a crucial but complex task for autonomous driving (AD) vehicles. Their inference models typically face poor generalization d…
A General Optimization Framework for Tackling Distance Constraints in Movable Antenna-Aided Systems
Yichen Jin, Qingfeng Lin, Yang Li +4
The recently emerged movable antenna (MA) shows great promise in leveraging spatial degrees of freedom to enhance the performance of wireless systems. However, resource allocation…
Channel Estimation for Rydberg Atomic Receivers
Bokai Xu, Jiayi Zhang, Zhongtao Chen +4
The rapid development of the quantum technology presents huge opportunities for 6G communications. Leveraging the quantum properties of highly excited Rydberg atoms, Rydberg atom-b…
Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator
Wei-Bin Kou, Guangxu Zhu, Rongguang Ye +3
Learning-based street scene semantic understanding in autonomous driving (AD) has advanced significantly recently, but the performance of the AD model is heavily dependent on the q…
Mixture of Experts-augmented Deep Unfolding for Activity Detection in IRS-aided Systems
Zeyi Ren, Qingfeng Lin, Jingreng Lei +2
In the realm of activity detection for massive machine-type communications, intelligent reflecting surfaces (IRS) have shown significant potential in enhancing coverage for devices…
Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory
Wei-Bin Kou, Qingfeng Lin, Ming Tang +5
To improve the generalization of the autonomous driving (AD) perception model, vehicles need to update the model over time based on the continuously collected data. As time progres…