Millimeter-wave backscattering measurements with transmitarrays for personal radar applications

The concept of personal radar has recently emerged as an interesting solution for next 5G applications. In fact the high portability of massive antenna arrays at millimeter-waves enables the integration of a radar system in pocket-size devices (i.e. tablets or smartphones) and enhances the possibility to map the surrounding environment by guaranteeing accurate localization together with high-speed communication capabilities. In this paper we investigate for the first time the capability of such personal radar solution using real measured data collected at millimeter-waves as input for the mapping algorithm

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

Occupancy Grid Mapping for Personal Radar Applications

Next fifth generation (5G) of mobile wireless communication foresees the use of mm-wave technology to boost communication at an unprecedented scale, thanks to the large available bandwidth [1] . In addition, the move-up in the frequency spectrum allows to include a large number of antennas into a small area, thus enabling their integration into portable devices [2 , 3] . In this way, such a technological perspective can be exploited to add new functionalities in addition to communication. For example, the laser-like beamsteering allowed by massive arrays at mm-wave can be used to automatically scan and reconstruct the topology of the surrounding environment. Such an idea, namely personal radar, has been recently proposed in theory and its feasibility assessed by experiments [4 \u2013 [5]6] . In these works, the performance has been investigated through the adoption of a grid-based mapping approach relying on an extended Kalman-Filter (EKF): the environment has been discretized in a grid of cells whose root-radar cross section (RCS) values constitute the state vector to be estimated starting from the backscattered radar response [7 , 8] . To simplify the analysis, the state of the system has been modeled as a Gaussian random vector whose mean vector and covariance matrix are updated during the mapping process as soon as new measurements are collected [5 , 9] . The main limitation of this model is that the Gaussian assumption does not capture the underlaying bimodal nature of the phenomenon, i.e., each cell is empty or occupied. In laser-based mapping systems, occupancy grid (OG) methods are usually considered to model this bi-modality by exploiting the basic assumption that laser beam illuminates only one cell per time [10] . This is not the case in radio-based radars where the shape of the radiation pattern is such to illuminate an area composed of several cells, thus making existing OG methods not appropriate due to the inherent cross-correlation between cells that is not zero [11]

Indoor environment-adaptive mapping with beamsteering massive arrays

open6siThis work was supported in part by the European Union’s Horizon 2020 research and innovation programme under the XCYCLE project under Grant 635975, in part by the Marie Sklodowska-Curie project MAPS under Grant 659067, and in part by the Marie Sklodowska-Curie project AirSens under Grant 793581.Beamsteering massive arrays have been recently proposed for indoor environment mapping in next 5G scenarios, thanks to their capability to better penetrate materials with respect to current laser or vision-based systems. From the perspective of integrating radars in small portable devices, architectures based on non-coherent processing of raw measurements represent a viable solution to overcome the limitations of current indoor radio mapping techniques, which entail a too high processing or receiver complexity. In this correspondence, we investigate the capability of low-complexity mobile radars, equipped with mm-wave massive arrays, to adapt to the environment in order to reconstruct it, by adjusting a threshold with respect to the collected data and the radiation pattern. Results, corroborated by means of a measurement campaign, show the effectiveness of the proposed approach.embargoed_20190106Guidi, Francesco; Mariani, Andrea; Guerra, Anna; Dardari, Davide; Clemente, Antonio; D'Errico, RaffaeleGuidi, Francesco; Mariani, Andrea; Guerra, Anna; Dardari, Davide; Clemente, Antonio; D'Errico, Raffael

Application of transmitarray antennas for indoor mapping at millimeter-waves

Millimeter-waves are expected to play a key role in next 5G scenario due to the availability of a large clean unlicensed bandwidth at 60 GHz and the possibility to realize packed antenna arrays, with a consequent increase of the communication capacity and the introduction of new functionalities, such as high-definition localization and personal radar for automatic environment mapping. In this paper we propose the adoption of millimeter-wave transmitarrays for personal radar applications and we investigate the impact of the radiation pattern characteristics on the map reconstruction accuracy, by analysing how the number of array elements, of quantization bits and the focal distance affect the environment reconstruction performance

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Crowd-based cognitive perception of the physical world: Towards the internet of senses

This paper introduces a possible architecture and discusses the research directions for the realization of the Cognitive Perceptual Internet (CPI), which is enabled by the convergence of wired and wireless communications, traditional sensor networks, mobile crowd-sensing, and machine learning techniques. The CPI concept stems from the fact that mobile devices, such as smartphones and wearables, are becoming an outstanding mean for zero-effort world-sensing and digitalization thanks to their pervasive diffusion and the increasing number of embedded sensors. Data collected by such devices provide unprecedented insights into the physical world that can be inferred through cognitive processes, thus originating a digital sixth sense. In this paper, we describe how the Internet can behave like a sensing brain, thus evolving into the Internet of Senses, with network-based cognitive perception and action capabilities built upon mobile crowd-sensing mechanisms. The new concept of hyper-map is envisioned as an efficient geo-referenced repository of knowledge about the physical world. Such knowledge is acquired and augmented through heterogeneous sensors, multi-user cooperation and distributed learning mechanisms. Furthermore, we indicate the possibility to accommodate proactive sensors, in addition to common reactive sensors such as cameras, antennas, thermometers and inertial measurement units, by exploiting massive antenna arrays at millimeter-waves to enhance mobile terminals perception capabilities as well as the range of new applications. Finally, we distillate some insights about the challenges arising in the realization of the CPI, corroborated by preliminary results, and we depict a futuristic scenario where the proposed Internet of Senses becomes true

AOA estimation with EM lens-embedded massive arrays

Recently, EM lens-embedded massive array antennas have been proposed for next 5G mobile wireless communications, as the adoption of a lens allows to discriminate the AOA of signals in the analog domain, with the possibility to preserve the processing complexity lower with respect to traditional massive arrays. In fact, in such a way, complex ADC chains can be avoided and the number of required antennas can be decreased. By exploiting these advantages, in this paper we study the possibility to use a single EM lens massive array at mm-wave for the AOA estimation of the received signal. In this perspective, ML estimator and practical approaches, tailored for the considered scenario, are derived. Results, obtained for different number of antennas, confirm the possibility to achieve interesting AOA estimation performance with an extremely compact architecture