The development of integrated sensing and communication (ISAC) systems has recently gained interest for its ability to offer a variety of services including resources sharing and new applications, for example, localization, tracking, and health care related. While the sensing capabilities are offered through many technologies, rending to their wide deployments and the high frequency spectrum they provide and high range resolution, its accessibility through the Wi-Fi networks IEEE 802.11ad and 802.11ay has been getting the interest of research and industry. Even though there is a dedicated standardization body, namely the 802.11bf task group, working on enhancing the Wi-Fi sensing performance, investigations are needed to evaluate the effectiveness of various sensing techniques. In this project, we, in addition to surveying related literature, we evaluate the sensing performance of the millimeter wave (mmWave) Wi-Fi systems by simulating a scenario of a human target using Matlab simulation tools. In this analysis, we processed channel estimation data using the short time Fourier transform (STFT). Furthermore, using a channel variation threshold method, we evaluated the performance while reducing feedback. Our findings indicate that using STFT window overlap can provide good tracking results, and that the reduction in feedback measurements using 0.05 and 0.1 threshold levels reduces feedback measurements by 48% and 77%, respectively, without significantly degrading performance.
Experimental Performance of Bidirectional Phase Coherent Transmission and Sensing for mmWave Cell-free Massive MIMO Systems with Reciprocity Calibration
National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信重点实验室）；Pervasive Communication Research Center, Purple Mountain Laboratories（紫金山实验室泛在通信研究中心）；China Mobile Research Institute（中国移动研究院）
mmWave Sensing for Detecting Movement Through Thermoplastic Masks During Radiation Therapy Treatment
Aalto University School of Electrical Engineering（阿尔托大学电气工程学院）；Viterbi School of Engineering, University of Southern California（南加州大学维特比工程学院）；Keck School of Medicine, University of Southern California（南加州大学凯克医学院）
LoS sensing-based superimposed CSI feedback for UAV-Assisted mmWave systems
在无人机辅助毫米波系统中，信道状态信息（CSI）反馈对于调制方案的选择、资源管理、波束形成等至关重要。然而，传统的CSI反馈方法会导致UAV发射机的显著反馈开销和能量消耗，从而缩短系统的运行时间。为了解决这些问题，受叠加反馈和通感一体化（ISAC）的启发，提出了一种基于视距（LoS）传感的叠加CSI反馈方案。具体地，在UAV发射器侧，地面到UAV（G2 U）CSI叠加在UAV到地面（U2 G）数据上以反馈到地面基站（gBS）。在gBS处，专用LoS感测网络（LoSSenNet）被设计为在LoS和NLoS场景中感测U2 G CSI。利用LoS-SenNet的感测结果，从初始特征提取中确定的G2 U CSI将用作先验信息以指导后续操作。具体地，对于NLoS中的G2 U CSI，开发CSI恢复网络（CSI-RecNet）和叠加干扰消除以恢复G2 U CSI和U2 G数据。对于LoS场景，在CSI-RecNet和叠加干扰消除块之前嵌入专用LoS辅助网络（LoS-AidNet），以突出G2 U CSI的特征。仿真结果表明，与其他叠加CSI反馈方法相比，该反馈方案有效地提高了G2 U CSI和U2 G数据的恢复精度。此外，对参数变化，所提出的反馈方案显示其鲁棒性。
Toward Distributed User Scheduling and Coordinated Beamforming in Multi-Cell mmWave Networks: A Sensing-Assisted Framework
The 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）；School of Artificial Intelligence, CUHK-Shenzhen（香港中文大学（深圳）人工智能学院）；Networking and User Experience Laboratory, Huawei Technologies（华为技术有限公司网络与用户体验实验室）
Physics-Inspired Target Shape Detection and Reconstruction in mmWave Communication Systems
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；Zhejiang Provincial Key Laboratory of Info. Proc., Commun. & Netw. (IPCAN)（浙江省信息处理、通信与网络重点实验室）；Zhejiang Lab（之江实验室）
Knowledge Distillation for Lightweight Multimodal Sensing-Aided mmWave Beam Tracking
Centre for Wireless Communications (CWC), University of Oulu（奥卢大学无线通信中心）；School of Electrical, Computer, and Energy Engineering, Arizona State University（亚利桑那州立大学电气、计算机与能源工程学院）；School of Electrical Engineering & Computer Science, University of California, Irvine（加州大学欧文分校电气与计算机工程学院）
Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Centre for Wireless Communications, University of Oulu（奥卢大学无线通信中心）；Ben-Gurion University of the Negev（内盖夫本古里安大学）；University of California, Irvine（加州大学尔湾分校）；Weizmann Institute of Science（魏茨曼科学研究所）
Multi-Modal Sensing Residual-Corrected GNN for mmWave Path Loss Prediction via Synesthesia of Machines
Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University（山东大学中德人工智能联合研究中心）；School of Software, Shandong University（山东大学软件学院）；State Key Laboratory of Photonics and Communications, School of Electronics, Peking University（北京大学电子工程学院光通信与光子学国家重点实验室）
