A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications

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.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys & Tutorials (IEEE COMST

ML-based Approaches for Wireless NLOS Localization: Input Representations and Uncertainty Estimation

The challenging problem of non-line-of-sight (NLOS) localization is critical for many wireless networking applications. The lack of available datasets has made NLOS localization difficult to tackle with ML-driven methods, but recent developments in synthetic dataset generation have provided new opportunities for research. This paper explores three different input representations: (i) single wireless radio path features, (ii) wireless radio link features (multi-path), and (iii) image-based representations. Inspired by the two latter new representations, we design two convolutional neural networks (CNNs) and we demonstrate that, although not significantly improving the NLOS localization performance, they are able to support richer prediction outputs, thus allowing deeper analysis of the predictions. In particular, the richer outputs enable reliable identification of non-trustworthy predictions and support the prediction of the top-K candidate locations for a given instance. We also measure how the availability of various features (such as angles of signal departure and arrival) affects the model's performance, providing insights about the types of data that should be collected for enhanced NLOS localization. Our insights motivate future work on building more efficient neural architectures and input representations for improved NLOS localization performance, along with additional useful application features.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Work partly supported by the RA Science Committee grant No. 22rl-052 (DISTAL) and the EU under Italian National Recovery and Resilience Plan of NextGenerationEU on "Telecommunications of the Future" (PE00000001 - program "RESTART"

Exploiting the spatio-temporal channel properties of multiple antenna systems

The spatio-temporal channel properties of multiple antenna systems are exploited to obtain new approaches to localization and channel prediction. It is shown that a mobile station can be localized in multipath environments under the explicit consideration of scatterers. Thus, unlike conventional localization systems, the scatterers are used as an aid in localization. Moreover, it is shown that channel prediction in multiple antenna systems can be performed using linear prediction filters. This result is used to propose optimal and computationally inexpensive suboptimal channel predictors

Reconfigurable Intelligent Surfaces: A signal processing perspective with wireless applications

Antenna array technology enables the directional transmission and reception of wireless signals for communication, localization, and sensing purposes. The signal processing algorithms that underpin it began to be developed several decades ago [1], but it was with the deployment of 5G wireless mobile networks that the technology became mainstream [2]. The number of antenna elements in the arrays of 5G base stations (BSs) and user devices can be measured on the order of hundreds and tens, respectively. As networks shift toward using higher-frequency bands, more antennas fit into a given aperture. For communication purposes, the arrays are harnessed to form beams in desired directions to improve the signal-to-noise ratio (SNR) and multiplex data signals in the spatial domain (to one or multiple devices) and to suppress interference by spatial filtering [2]. For localization purposes, these arrays are employed to maintain the SNR when operating across wider bandwidths, for angle-of-arrival estimation, and to separate multiple sources and scatterers [3]. The practical use of these features requires that each antenna array is equipped with well-designed signal processing algorithms

Beyond 5G RIS mmWave Systems: Where Communication and Localization Meet

Upcoming beyond fifth generation (5G) communications systems aim at further enhancing key performance indicators and fully supporting brand-new use cases by embracing emerging techniques, e.g., reconfigurable intelligent surface (RIS), integrated communication, localization, and sensing, and mmWave/THz communications. The wireless intelligence empowered by state-of-the-art artificial intelligence techniques has been widely considered at the transceivers, and now the paradigm is deemed to be shifted to the smart control of radio propagation environment by virtue of RISs. In this paper, we argue that to harness the full potential of RISs, localization and communication must be tightly coupled. This is in sharp contrast to 5G and earlier generations, where localization was a minor additional service. To support this, we first introduce the fundamentals of RIS mmWave channel modeling, followed by RIS channel state information acquisition and link establishment. Then, we deal with the connection between localization and communications, from a separate and joint perspective

Towards joint communication and sensing (Chapter 4)

Localization of user equipment (UE) in mobile communication networks has been supported from the early stages of 3rd generation partnership project (3GPP). With 5th Generation (5G) and its target use cases, localization is increasingly gaining importance. Integrated sensing and localization in 6th Generation (6G) networks promise the introduction of more efficient networks and compelling applications to be developed

On geometry-base statistical channel models for MIMO wireles communications

El uso de sistemas de comunicación de banda ancha de múltiple entradamúltiple salida (Multiple Input Multiple Output MIMO) es actualmente objeto de un interés considerable. Una razón para esto es el reciente desarrollo de sistemas de comunicación móvil de tercera generación (3G) y superiores, tales como la tecnología de banda ancha Wideband Code Division Multiple Access (WCDMA, por sus siglas en inglés), la cual proporciona canales de radio de 5 MHz de ancho de banda. Para el diseño y la simulación de estos sistemas de radio móviles que usan propagación inalámbrica MIMO (como Wideband-CDMA por ejemplo), necesitamos modelos de canal que provean la requerida información espacial y temporal necesaria para el estudio de tales sistemas, esto es, los parámetros básicos de modelado en los dominios del espacio y el tiempo. Como ejemplo podemos mencionar, el valor cuadrático medio de la dispersión del retardo (Delay spread DS) el cual está directamente relacionado a la capacidad de un sistema de comunicación específico y nos da una idea aproximada de la complejidad del receptor. En esta tesis, se propone un modelo basado en geometría con enfoque en grupos (clusters) y es utilizado para el análisis en los dominios del espacio y el tiempo para condiciones estacionarias, y para representar los perfiles de potencia-angulo-retardo (Power Delay Angle Profiles PDAPs) de los componentes multi-trayectoria en ambientes urbanos. Además, se han derivado soluciones en formas cerradas para las expresiones en el dominio del ángulo (espacial) y del tiempo. La investigación previa sobre el modelado de canales cubre una amplia variedad de aspectos en varios niveles de detalle, incluyendo análisis para condiciones no estacionarias. Sin embargo el trabajo presentado en la literatura no incluye las relaciones entre los grupos (cluster) físicos y los PDAPs. El modelo propuesto basado en grupos (clusters) puede ser usado para mejorar aún más el desempeño en condiciones estacionarias de los sistemas de comunicaciones móviles actuales y futuros tales como los sistemas de comunicación MIMO de banda ancha. En la tesis también se presenta un análisis en el dominio del ángulo (espacial) y del tiempo respectivamente, a través de las funciones densidad de probabilidad (PDF) de la dirección de llegada (Direction of Arrival DOA) y el tiempo de llegada (Time of Arrival TOA) para el modelo basado en grupos. A fin de evaluar las funciones de probabilidad teóricas derivadas, éstas han sido comparadas con resultados experimentales publicados en la literatura. La comparación con estos resultados experimentales muestran una buena concordancia, no obstante la técnica de modelado presentada en esta tesis se encuentra limitada a condiciones estacionarias del canal. La condición de no estacionariedad se ubica más allá del alcance de esta tesis, es decir, el modelo propuesto no incorpora el efecto Doppler en los análisis