In this paper, we consider the downlink of large OFDMA-based networks and study their performance bounds as a function of the number of - transmitters $B$, users $K$, and resource-blocks $N$. Here, a resource block is a collection of subcarriers such that all such collections, that are disjoint have associated independently fading channels. In particular, we analyze the expected achievable sum-rate as a function of above variables and derive novel upper and lower bounds for a general spatial geometry of transmitters, a truncated path-loss model, and a variety of fading models. We establish the associated scaling laws for dense and extended networks, and propose design guidelines for the regulators to guarantee various QoS constraints and, at the same time, maximize revenue for the service providers. Thereafter, we develop a distributed resource allocation scheme that achieves the same sum-rate scaling as that of the proposed upper bound for a wide range of $K, B, N$. Based on it, we compare low-powered peer-to-peer networks to high-powered single-transmitter networks and give an additional design principle. Finally, we also show how our results can be extended to the scenario where each of the $B$ transmitters have $M (>1)$ co-located antennas.
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
Princeton University（普林斯顿大学）；Imperial College London（帝国理工学院）；The University of Hong Kong（香港大学）；Virginia Tech（弗吉尼亚理工大学）；University of Oulu（奥卢大学）；Ericsson Research（爱立信研究院）
Optimizations with Intelligent Reflecting Surfaces (IRSs) in 6G Wireless Networks: Power Control, Quality of Service, Max-Min Fair Beamforming for Unicast, Broadcast, and Multicast with Multi-antenna Mobile Users and Multiple IRSs
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
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））；Princeton University（普林斯顿大学）；The University of Edinburgh（爱丁堡大学）；Virginia Tech（弗吉尼亚理工大学）；Huawei France R & D（华为法国研发中心）
On Privacy, Security, and Trustworthiness in Distributed Wireless Large AI Models (WLAM)
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Key Laboratory of Information Processing, Communication and Networking of Zhejiang Province (IPCAN)（浙江省信息处理、通信与网络重点实验室）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信重点实验室）；School of Information and Communication Engineering, Beijing University of Posts and Communications（北京邮电大学信息与通信工程学院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Center for 6G Technology, Khalifa University of Science and Technology（哈利法科技大学6G技术中心）
