Optimizing large-scale wireless networks, including optimal resource management, power allocation, and throughput maximization, is inherently challenging due to their non-observable system dynamics and heterogeneous and complex nature. Herein, a novel ensemble Q-learning algorithm that addresses the performance and complexity challenges of the traditional Q-learning algorithm for optimizing wireless networks is presented. Ensemble learning with synthetic Markov Decision Processes is tailored to wireless networks via new models for approximating large state-space observable wireless networks. In particular, digital cousins are proposed as an extension of the traditional digital twin concept wherein multiple Q-learning algorithms on multiple synthetic Markovian environments are run in parallel and their outputs are fused into a single Q-function. Convergence analyses of key statistics and Q-functions and derivations of upper bounds on the estimation bias and variance are provided. Numerical results across a variety of real-world wireless networks show that the proposed algorithm can achieve up to 50% less average policy error with up to 40% less runtime complexity than the state-of-the-art reinforcement learning algorithms. It is also shown that theoretical results properly predict trends in the experimental results.
Large Vision Model-Enhanced Digital Twin with Deep Reinforcement Learning for User Association and Load Balancing in Dynamic Wireless Networks
National Mobile Communications Research Lab, Southeast University, Nanjing 210096, China（南京东南大学国家移动通信研究中心）；Pervasive Communication Research Center, Purple Mountain Laboratories, Nanjing 211111, China（南京紫金山实验室 pervasive 通信研究中心）
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Institute for the Wireless Internet of Things, Northeastern University（东北大学无线互联网研究所）
Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（Simula都市数字工程中心）
Tri-Band Channel Measurement-Enabled Multi-Layer Digital Twin for Terahertz Wireless Data Centers
Terahertz Wireless Communications (TWC) Interdisciplinary Research Center, Shanghai Jiao Tong University（上海交通大学太赫兹无线通信（TWC）跨学科研究中心）；Huawei Technologies Company Ltd.（华为技术有限公司）；School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（上海交通大学信息科学与工程学院）
Metasurfaces-Integrated Wireless Neural Networks for Lightweight Over-The-Air Edge Inference
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens（信息与电信系，雅典国家与卡波迪斯蒂亚大学）；National Inter-University Consortium for Telecommunications (CNIT)（电信国家大学联合体（CNIT））；DIAG Department, Sapienza University of Rome（罗马萨皮恩扎大学DIAG系）；DIET Department, Sapienza University of Rome（罗马萨皮恩扎大学DIET系）
Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）；Department of Computing, Hong Kong Polytechnic University（香港理工大学计算机系）；Data61, CSIRO（数据61，CSIRO）；Department of Electronics Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究中心）
