11 papers
Low-Overhead Receiver Design for Data-Dependent Superimposed Training via Deep Learning
Xinjie Li, Xingyu Zhou, Jing Zhang +3
Superimposed pilot (SIP) transmission improves spectral efficiency by eliminating the dedicated pilot overhead required in orthogonal pilot (OP)-based schemes. However, SIP suffers…
NF-TrackLLM: Joint Prediction of UAV Trajectory and Near-Field Beam for LAE XL-MIMO Systems
Qianfan Lu, Mengyuan Li, Jiachen Tian +3
User localization and beam management are tightly linked in extremely large-scale multiple-input multiple-output (XL-MIMO) systems, especially in dense low-altitude economy (LAE) s…
Beam Prediction Based on Multimodal Large Language Models
Tianhao Mao, Le Liang, Jie Yang +3
Accurate beam prediction is a key enabler for next-generation wireless communication systems. In this paper, we propose a multimodal large language model (LLM)-based beam predictio…
Improving Channel Estimation via Multimodal Diffusion Models with Flow Matching
Xiaotian Fan, Xingyu Zhou, Le Liang +2
Deep generative models offer a powerful alternative to conventional channel estimation by learning complex channel distributions. By integrating the rich environmental information…
Timely Parameter Updating in Over-the-Air Federated Learning
Jiaqi Zhu, Zhongyuan Zhao, Xiao Li +3
Incorporating over-the-air computations (OAC) into the model training process of federated learning (FL) is an effective approach to alleviating the communication bottleneck in FL…
Planar Diffractive Neural Networks Empowered Communications: A Spatial Modulation Scheme
Xiaokun Teng, Yanqing Ren, Weicong Chen +3
Diffractive neural networks, where signal processing is embedded into wave propagation, promise light-speed and energy-efficient computation. However, existing three-dimensional st…