4 citations · 6 across the 12 of their papers we have counts for
12 papers
Unified Generalization for Frequency-Domain Channel Extrapolation Across Near-Field and Far-Field Scenarios
Haoyu Wang, Zhi Sun, Shuangfeng Han +2
As antenna arrays grow, near-field effects become non-negligible in large-scale MIMO, making accurate low-overhead channel acquisition crucial in both far-field and near-field regi…
Physics-Informed Path-Parametric Learning for Efficient and Lightweight CSI Feedback
Chunyu Ling, Jiajia Guo, Yiming Cui +4
Channel State Information (CSI) feedback is vital for high spectral efficiency in wireless systems, yet high-dimensional CSI introduce significant feedback overhead. Recent deep le…
Generalizable Learning for Massive MIMO CSI Feedback in Unseen Environments
Haoyu Wang, Zhi Sun, Shuangfeng Han +2
Deep learning is promising to enhance the accuracy and reduce the overhead of channel state information (CSI) feedback, which can boost the capacity of frequency division duplex (F…
TREE:Token-Responsive Energy Efficiency Framework For Green AI-Integrated 6G Networks
Tao Yu, Kaixuan Huang, Tengsheng Wang +7
As wireless networks evolve toward AI-integrated intelligence, conventional energy-efficiency metrics fail to capture the value of AI tasks. In this paper, we propose a novel EE me…
Enhancing Environment Generalizability for Deep Learning-Based CSI Feedback
Haoyu Wang, Shuangfeng Han, Xiaoyun Wang +1
Accurate and low-overhead channel state information (CSI) feedback is essential to boost the capacity of frequency division duplex (FDD) massive multiple-input multiple-output (MIM…
Generalizable Learning for Frequency-Domain Channel Extrapolation under Distribution Shift
Haoyu Wang, Zhi Sun, Shuangfeng Han +2
Frequency-domain channel extrapolation is effective in reducing pilot overhead for massive multiple-input multiple-output (MIMO) systems. Recently, Deep learning (DL) based channel…
AI-driven 6G Air Interface: Technical Usage Scenarios and Balanced Design Methodology
Xiaoyun Wang, Shuangfeng Han, Zhiming Liu +3
This paper systematically analyzes the typical application scenarios and key technical challenges of AI in 6G air interface transmission, covering important areas such as performan…
Energy Optimization of Multi-task DNN Inference in MEC-assisted XR Devices: A Lyapunov-Guided Reinforcement Learning Approach
Yanzan Sun, Jiacheng Qiu, Guangjin Pan +4
Extended reality (XR), blending virtual and real worlds, is a key application of future networks. While AI advancements enhance XR capabilities, they also impose significant comput…
Path Evolution Model for Endogenous Channel Digital Twin towards 6G Wireless Networks
Haoyu Wang, Zhi Sun, Shuangfeng Han +3
Massive Multiple Input Multiple Output (MIMO) is critical for boosting 6G wireless network capacity. Nevertheless, high dimensional Channel State Information (CSI) acquisition beco…
Low-Complexity Joint Beamforming for RIS-Assisted MU-MISO Systems Based on Model-Driven Deep Learning
Weijie Jin, Jing Zhang, Chao-Kai Wen +3
Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly wi…
Toward 6G Native-AI Network: Foundation Model based Cloud-Edge-End Collaboration Framework
Xiang Chen, Zhiheng Guo, Xijun Wang +5
Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on multi-agent…
Designing Heterogeneous GNNs with Desired Permutation Properties for Wireless Resource Allocation
Jianyu Zhao, Chenyang Yang, Tingting Liu
Graph neural networks (GNNs) have been designed for learning a variety of wireless policies, i.e., the mappings from environment parameters to decision variables, thanks to their s…