Scalable Learning Paradigms for Data-Driven Wireless Communication
Beijing University of Posts and Telecommunications（北京邮电大学）；National Chiao Tung University（国立交通大学）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy. However, the ever exploding data volume and model complexity will limit centralized solutions to learn and respond within a reasonable time. Therefore, scalability becomes a critical issue to be solved. In this article, we aim to provide a systematic discussion on the building blocks of scalable data-driven wireless networks. On one hand, we discuss the forward-looking architecture and computing framework of scalable data-driven systems from a global perspective. On the other hand, we discuss the learning algorithms and model training strategies performed at each individual node from a local perspective. We also highlight several promising research directions in the context of scalable data-driven wireless communications to inspire future research.
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification
Beijing University of Posts and Telecommunications（北京邮电大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of California, Davis（加州大学戴维斯分校）
Towards Scalable Wireless Federated Learning: Challenges and Solutions
ShanghaiTech University（上海科技大学）；Nanjing University（南京大学）；Beihang University（北京航空航天大学）；Xiameng University（厦门大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G
School of Telecommunications Engineering, Xidian University（西安电子科技大学电信工程学院）；School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子与信息工程学院）；Department of Electronics Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）
CLEAN-MI: A Scalable and Efficient Pipeline for Constructing High-Quality Neurodata in Motor Imagery Paradigm
Ministry of Education Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（教育部长图像处理与智能控制重点实验室，人工智能与自动化学院，华中科技大学）；Zhongguancun Academy（中关村学院）
SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation
Shanghai AI Laboratory（上海人工智能实验室）；Zhejiang University（浙江大学）；University of Maryland, College Park（马里兰大学 College Park 分校）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）
本文提出 SDAR 范式，将训练好的 AR 模型低成本转换为分块离散扩散模型，兼顾全局连贯与块内并行解码，并在密集及 MoE 模型上实现无损加速与推理提升。
