RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing

In this work we present RAPID, a joint communication and radar (JCR) system based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz band. In contrast to most existing approaches for human sensing at millimeter-waves, which employ special-purpose radars to retrieve the small-scale Doppler effect (micro-Doppler) caused by human motion, RAPID achieves radar-level sensing accuracy by retrofitting IEEE 802.11ay access points. For this, it leverages the IEEE 802.11ay beam training mechanism to accurately localize and track multiple individuals, while the in-packet beam tracking fields are exploited to extract the desired micro-Doppler signatures from the time-varying phase of the channel impulse response (CIR). The proposed approach enables activity recognition and person identification with IEEE 802.11ay wireless networks without requiring modifications to the packet structure specified by the standard. RAPID is implemented on an IEEE 802.11ay-compatible FPGA platform with phased antenna arrays, which estimates the CIR from the reflections of transmitted packets. The proposed system is evaluated on a large dataset of CIR measurements, proving robustness across different environments and subjects, and outperforming state-of-the-art sub-6 GHz WiFi sensing techniques. Using two access points, RAPID reliably tracks multiple subjects, reaching activity recognition and person identification accuracies of 94% and 90%, respectively.Comment: 16 pages, 18 figures, 4 table

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

Sfruttare le radio mmWave per il rilevamento ambientale interno

Il concetto di rilevamento senza contatto dell'attività umana ha il potenziale di rivoluzionare la nostra interazione con la tecnologia e l'ambiente circostante, consentendo sistemi di monitoraggio remoto che sono non invasivi. I segnali radio riflessi Mm-Wave hanno attirato l'attenzione significativa da parte di accademici e industrie grazie alla loro capacità di rilevare, tracciare e analizzare il movimento umano e altri oggetti utilizzando il principio del radar. L'alta sensibilità e affidabilità delle frequenze Mm-Wave nel rilevare piccoli movimenti del corpo umano, unita alla non invasività di questi segnali, li rendono una tecnologia promettente per le applicazioni in cui la privacy è una preoccupazione. Tuttavia, ci sono sfide nella pratica di questa tecnologia, come la complessità delle riflessioni in ambienti reali e il fov limitato dei radar, che richiedono un'indagine approfondita. Questa tesi approfondisce i campi di localizzazione e rilevamento Mm-Wave, esplorando varie applicazioni e sviluppando piattaforme integrate di rilevamento e comunicazione. La ricerca inizia con il tracciamento di persone basato su radar utilizzando algoritmi di Deep Learning supervisionati e non supervisionati, che vengono confrontati con l'algoritmo fondamentale della letteratura, il filtro di Kalman. Viene quindi costruita una piattaforma di rilevamento e comunicazione integrata utilizzando Access Point IEEE 802.11ay per il riconoscimento dell'attività umana, estraendo le firme micro-Doppler dalle unità di allenamento incorporate nei pacchetti dati per il tracciamento del raggio. Infine, viene implementato un banco di prova di una rete di radar per applicazioni di rilevamento con obiettivi multipli, sfruttando i punti di forza di più radar e il campo di vista condiviso per compiti distribuiti e in tempo reale. La tesi fornisce una prospettiva completa sulla rilevazione dell'ambiente Mm-Wave, utilizzando vari hardware e combinando tecniche standard di elaborazione del segnale e tecniche di apprendimento automatico basate sui dati per sviluppare algoritmi. I risultati sono supportati da un'ampia sperimentazione con i banchi di prova del radar Mm-Wave e di rilevamento e comunicazione integrati all'avanguardia.The concept of contactless detection of human activity has the potential to revolutionize our interaction with technology and surroundings, enabling remote monitoring systems that are unobtrusive. Mm-Wave reflected radio signals have drawn significant attention from academia and industry due to their ability to detect, track, and analyze human movement and other objects using the radar principle. The high sensitivity and reliability of Mm-Wave frequencies in detecting small-scale movements of the human body, coupled with the non-intrusiveness of these signals, make them a promising technology for applications where privacy is a concern. However, there exist challenges in practical application of this technology, such as the complexity of reflections in real-life environments and limited fov of the radars, requiring tedious investigation. This thesis delves into the Mm-Wave localization and sensing fields, exploring various applications and developing integrated sensing and communication platforms. The research begins with radar-based person tracking using supervised and unsupervised Deep Learning algorithms, that are compared to the literature’s cornerstone algorithm, Kalman filter. An integrated sensing and communication platform using IEEE 802.11ay Access Points is then built for Human Activity Recognition, extracting the micro-Doppler signatures from the training units embedded in the data packets for beam tracking. Finally, a radar network testbed is implemented for sensing applications with multiple objectives, exploiting the strengths of multiple radars and shared field-of-view for distributed and real-time tasks. The thesis provides a comprehensive perspective on Mm-Wave environment sensing, utilizing various hardware and combining standard signal processing techniques and data-driven machine learning techniques to develop algorithms. The results are supported by extensive experimentation with state-of-the-art Mm-Wave radar and Integrated Sensing and Communication testbeds

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

The commercial availability of low-cost millimeterwave (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 fifthgeneration (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 standardcompliant 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: improved 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 largescale 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