Millimetre wave (mmWave) beam tracking is a challenging task because tracking algorithms are required to provide consistent high accuracy with low probability of loss of track and minimal overhead. To meet these requirements, we propose in this paper a new analog beam tracking framework namely Adaptive Tracking with Stochastic Control (ATSC). Under this framework, beam direction updates are made using a novel mechanism based on measurements taken from only two beam directions perturbed from the current data beam. To achieve high tracking accuracy and reliability, we provide a systematic approach to jointly optimise the algorithm parameters. The complete framework includes a method for adapting the tracking rate together with a criterion for realignment (perceived loss of track). ATSC adapts the amount of tracking overhead that matches well to the mobility level, without incurring frequent loss of track, as verified by an extensive set of experiments under both representative statistical channel models as well as realistic urban scenarios simulated by ray-tracing software. In particular, numerical results show that ATSC can track dominant channel directions with high accuracy for vehicles moving at 72 km/hour in complicated urban scenarios, with an overhead of less than 1\%.
Integrated Sensing, Communication, and Control for UAV-Assisted Mobile Target Tracking
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 Multi-Modal Communication Networks and Intelligent Information Processing（浙江省多模通信网络与智能信息处理重点实验室）
CKM-Based Predictive Beamforming for Energy-Efficient Mobile Tracking in ISCAP System
School of Electrical Engineering, the Iran University of Science and Technology (IUST)（伊朗科技大学电气工程学院）；Department of Electrical Engineering Technical University of Darmstadt（达姆施塔特工业大学电气工程系）
Fast Reconstruction of Motion-Corrupted Data with Mobile-GRAPPA: Motion and dB0 Inhomogeneity Correction Leveraging Efficient GRAPPA
Department of Electrical Engineering, Stanford University（斯坦福大学电气工程系）；Department of Radiology, Stanford University（斯坦福大学放射学系）；Siemens Medical Solutions USA, Inc.（西门子医疗解决方案美国公司）
DoDTrack: Indoor Mobile Devices Tracking via Difference-of-Doppler
College of Semiconductors, Southern University of Science and Technology（南方科技大学半导体学院）；Department of Electronic and Electrical Engineering, Southern University of Science and Technology（南方科技大学电子电气工程系）；The Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）未来网络智能研究院）
Neural Tracking of Sustained Attention, Attention Switching, and Natural Conversation in Audiovisual Environments using Mobile EEG
Dept. of Electrical Engineering, Linköping University（电子工程系，利厄普堡大学）；Centre for Mathematical Sciences, Lund University（数学科学中心，卢德大学）；Eriksholm Research Centre, Oticon A/S（埃里克霍姆研究中心，Oticon A/S）
