Wireless signal recognition (WSR) is crucial in modern and future wireless communication networks since it aims to identify properties of the received signal. Although many deep learning-based WSR models have been developed, they still rely on a large amount of labeled training data. Thus, they cannot tackle the few-sample problem in the practically and dynamically changing wireless communication environment. To overcome this challenge, a novel SSwsrNet framework is proposed by using the deep residual shrinkage network (DRSN) and semi-supervised learning. The DRSN can learn discriminative features from noisy signals. Moreover, a modular semi-supervised learning method that combines labeled and unlabeled data using MixMatch is exploited to further improve the classification performance under few-sample conditions. Extensive simulation results on automatic modulation classification (AMC) and wireless technology classification (WTC) demonstrate that our proposed WSR scheme can achieve better performance than the benchmark schemes in terms of classification accuracy. This novel method enables more robust and adaptive signal recognition for next-generation wireless networks.
Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy
Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；Hong Kong University of Science and Technology（香港科技大学）；Yonsei University（延世大学）；MediaTek（联发科）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Memorial University of Newfoundland（纽芬兰纪念大学）；Imperial College（帝国理工学院）
Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Pengcheng Laboratory（鹏城实验室）；Shanghai Academy of AI for Science（上海人工智能科学研究院）；Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化院）
Rydberg Atomic Quantum Receivers for Classical Wireless Communications and Sensing: Their Models and Performance
Nanyang Technological University（南洋理工大学）；DSO National Laboratories（DSO国家实验室）；Khalifa University of Science and Technology（哈利法科学技术大学）；University of Southampton（南安普顿大学）
Wireless Power Control Based on Large Language Models
School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；Department of Electrical and Computer Engineering, University of California, Santa Cruz（加州大学圣克鲁兹分校电气与计算机工程系）
Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model
Department of Electronic and Electrical Engineering, the University of Sheffield（电子与电气工程系，谢菲尔德大学）；Department of Computer Science and Technology, University of Cambridge（计算机科学与技术系，剑桥大学）；Brunel University London（伦敦布鲁内尔大学）；School of Information and Electronic Engineering, Zhejiang Gongshang University（信息与电子工程学院，浙江工商大学）；Design & Health Lab, Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano（设计与健康实验室，建筑、环境与土木工程系，米兰理工学院）；Department of Computer Science, Brunel University London（计算机科学系，伦敦布鲁内尔大学）；The State Key Laboratory of Synthetical Automation for Process Industries and The College of Information Science and Engineering, Northeastern University（过程工业综合自动化国家重点实验室和信息科学与工程学院，东北大学）；Centre for Wireless Innovation, Queen’s University of Belfast（无线创新中心，贝尔法斯特女王大学）
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）
A Graph Foundation Model for Large-Scale MIMO Detection
National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信重点实验室）；Department of Electrical and Computer Engineering, University of California, Santa Cruz（加州大学圣克鲁兹分校电气与计算机工程系）；Institute of Communications Engineering, National Sun Yat-sen University（国立中山大学通信工程研究所）；School of Electrical Engineering and Telecommunications, University of New South Wales（新南威尔士大学电气工程与电信学院）
An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier
Berkeley Wireless Research Center, EECS Department, University of California, Berkeley（加州大学伯克利分校电气工程与计算机科学系伯克利无线研究中心）；Integrated System Laboratory, ETH Zurich（苏黎世联邦理工学院集成系统实验室）；DEI, University of Bologna（博洛尼亚大学DEI（可能是Department of Electrical Engineering and Information Technology的缩写，常见译为电气工程与信息技术系））
Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach
National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究中心）；Purple Mountain Laboratories（紫金山实验室）；State Key Laboratory of CNS/ATM, Beijing Institute of Technology（北京理工大学 CNS/ATM 国家重点实验室）；Beijing Institute of Technology（北京理工大学）；MIT Key Laboratory of Complex-Field Intelligent Sensing, Beijing Institute of Technology（MIT 复杂场智能感知实验室，北京理工大学）；Advanced Technology Research Institute, Beijing Institute of Technology（北京理工大学先进技术研究院）；Yangtze Delta Region Academy, Beijing Institute of Technology（长江三角洲地区研究院，北京理工大学）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子与电气工程系）；Department of Electrical and Computer Engineering, University of Delaware（德雷塞尔大学电子与计算机工程系）
