The commercial availability of low-cost millimeter-wave (mmWave) communication and radar devices is starting to improve the adoption of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We overview key concepts about mmWave signal propagation and system design, detailing approaches, algorithms and applications for mmWave localization and sensing. Several dimensions are considered, including the main objectives, techniques, and performance of each work, whether they reached an implementation stage, and which hardware platforms or software tools were used. We analyze theoretical (including signal processing and machine learning), technological, and implementation (hardware and prototyping) aspects, exposing under-performing or missing techniques and items towards enabling a highly effective sensing of human parameters, such as position, movement, activity and vital signs. Among many interesting findings, we observe that device-based localization systems would greatly benefit from commercial-grade hardware that exposes channel state information, as well as from a better integration between standard-compliant mmWave initial access and localization algorithms, especially with multiple access points (APs). Moreover, more advanced algorithms requiring zero-initial knowledge of the environment would greatly help improve the adoption of mmWave simultaneous localization and mapping (SLAM). Machine learning (ML)-based algorithms are gaining momentum, but still require the collection of extensive training datasets, and do not yet generalize to any indoor environment, limiting their applicability. Device-free (i.e., radar-based) sensing systems still have to be improved in terms of higher accuracy in the detection of vital signs (respiration and heart rate), and enhanced robustness/generalization capabilities across different environments. Moreover, improved support is needed for the tracking of multiple users, and for the automatic creation of radar networks to enable large-scale sensing applications. Finally, integrated systems performing joint communications and sensing are still in their infancy: theoretical and practical advancements are required to add sensing functionalities to mmWave-based channel access protocols based on orthogonal frequency-division multiplexing (OFDM) and multi-antenna technologies.

中文翻译：

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基于毫米波器件的定位和无器件传感技术及应用综述


低成本毫米波 (mmWave) 通信和雷达设备的商业化正在开始改善此类技术在消费市场的采用，为第五代 (5G) 和 - 的大规模和密集部署铺平道路。超越以及 6G 网络。与此同时，普遍的毫米波接入将以前所未有的精度实现设备定位和无设备传感，特别是对于低于 6 GHz 的商业级设备。本文调查了使用毫米波通信和雷达设备进行基于设备的定位和无设备传感的最新技术，重点关注室内部署。我们概述了毫米波信号传播和系统设计的关键概念，详细介绍了毫米波定位和传感的方法、算法和应用。考虑几个维度，包括每项工作的主要目标、技术和性能，是否达到实施阶段，以及使用了哪些硬件平台或软件工具。我们分析理论（包括信号处理和机器学习）、技术和实施（硬件和原型设计）方面，揭示表现不佳或缺失的技术和项目，以实现对人体参数的高效感知，例如位置、运动、活动和生命体征。在许多有趣的发现中，我们观察到基于设备的定位系统将极大地受益于公开通道状态信息的商业级硬件，以及符合标准的毫米波初始访问和定位算法之间的更好集成，尤其是在多个接入点的情况下（美联社）。 此外，需要对环境进行零初始了解的更先进的算法将极大地有助于提高毫米波同步定位和地图（SLAM）的采用。基于机器学习（ML）的算法正在蓬勃发展，但仍然需要收集大量的训练数据集，并且尚未推广到任何室内环境，限制了它们的适用性。无设备（即基于雷达）的传感系统仍然需要在生命体征（呼吸和心率）检测的更高准确度以及跨不同环境的鲁棒性/泛化能力方面进行改进。此外，需要改进对多个用户的跟踪以及自动创建雷达网络以实现大规模传感应用的支持。最后，执行联合通信和传感的集成系统仍处于起步阶段：需要理论和实践的进步，才能将传感功能添加到基于正交频分复用（OFDM）和多天线技术的毫米波信道接入协议中。