Device Activity Detection and Channel Estimation Using Score-Based Generative Models in Massive MIMO  

作　　者：TANG Chenyue LI Zeshen CHEN Zihan Howard H.YANG 

机构地区：[1]ZJU-UIUC Institute,Zhejiang University,Haining 314400,China [2]Singapore University of Technology and Design,Singapore 487372,Singapore

出　　处：《ZTE Communications》2025年第1期53-62,共10页中兴通讯技术(英文版)

摘　　要：The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and robust approach is joint device activity detection and channel estimation.In this paper,we present an approach utilizing score-based generative models to address the underdetermined nature of channel estimation,which is data-driven and well-suited for the complex and dynamic environment of massive MIMO systems.Our experimental results,based on a comprehensive dataset generated through Monte-Carlo sampling,demonstrate the high precision of our channel estimation approach,with errors reduced to as low as-45 d B,and exceptional accuracy in detecting active devices.

关 键 词：activity detection channel estimation inverse problem score-based generative model massive MIMO 

分 类 号：TN9[电子电信—信息与通信工程]