The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration 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 first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research reached some degree of implementation, and which hardware platforms were used for this purpose. We conclude by discussing that better algorithms for consumer-grade devices, data fusion methods for dense deployments, as well as an educated application of machine learning methods are promising, relevant and timely research directions.
翻译:低成本千兆瓦通信和雷达装置的商业可得性正在开始改善此类技术在消费市场的渗透,为在第五代(5G)和后代(5G)和6G网络中大规模密集部署大型和密集部署铺平了道路。与此同时,普通千兆瓦接入将使得设备本地化和无装置感测成为前所未有的准确度,特别是六千兆赫商业级装置。本文调查了使用毫米瓦夫通信和雷达装置进行基于装置的本地化和无装置感测的先进程度,重点是室内部署。我们首先概述了关于毫米瓦夫信号传播和系统设计的关键概念。然后,我们详细介绍了由毫米瓦夫促成的本地化和感测的方法和算法。我们考虑了我们分析中的若干层面,包括每项研究的主要目标、技术和绩效,是否达到某种执行程度,以及为此目的使用了哪些硬件平台。我们的结论是,关于消费者级装置的更好算法、密集部署的数据聚合方法,以及受过教育的机器学习方法的及时、有前途的研究方向。