arXiv subjects

Francesco Guidi

Publications and source records attributed to Francesco Guidi.

26 records · Page 2Linked to original sources

arXiv2021

Networks of UAVs of Low-Complexity for Time-Critical Localization

Future networks of unmanned aerial vehicles (UAVs) will be tasked to carry out ever-increasing complex operations that are time-critical and that require accurate localization performance (e.g., tracking the state of a malicious user). Since there is the need to preserve low UAV complexity while tackling the challenging goals of missions in effective ways, one key aspect is the UAV intelligence (UAV-I). The UAV's intelligence includes the UAV's capability to process information and to make decisions, e.g., to decide where to sense and whether to delegate some tasks to other network entities. In this paper, we provide an overview of possible solutions for the design of UAVs of low complexity, showing some of the needs of the UAVs for running efficient localization operations, performed either as a team or individually. Further, we focus on different network configurations, which possibly include assistance with edge computing. We also discuss open problems and future perspectives for these settings.

cs.NI↗

arXiv2021

Beam Focusing for Near-Field Multi-User MIMO Communications

Large antenna arrays and high-frequency bands are two key features of future wireless communication systems. The combination of large-scale antennas with high transmission frequencies often results in the communicating devices operating in the near-field (Fresnel) region. In this paper, we study the potential of beam focusing, feasible in near-field operation, in facilitating high-rate multi-user downlink multiple-input multiple-output (MIMO) systems. As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signaling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays. We first provide a mathematical model to characterize near-field wireless channels as well as the transmission pattern for the considered antenna architectures. Then, we formulate the beam focusing problem for the goal of maximizing the achievable sum-rate in multi-user networks. We propose efficient solutions based on the sum-rate maximization task for fully-digital, (phase shifters based-) hybrid and DMA architectures. Simulation results show the feasibility of the proposed beam focusing scheme for both single- and multi-user scenarios. In particular, the designed focused beams are such that users residing at the same angular direction can communicate reliably without interfering with each other, which is not achievable using conventional far-field beam steering.

eess.SP↗

arXiv2021

Near-field Tracking with Large Antenna Arrays: Fundamental Limits and Practical Algorithms

Applications towards 6G have brought a huge interest towards arrays with a high number of antennas and operating within the millimeter and sub-THz bandwidths for joint communication and localization. With such large arrays, the plane wave approximation is often not accurate because the system may operate in the near-field propagation region (Fresnel region) where the electromagnetic field wavefront is spherical. In this case, the curvature of arrival (CoA) is a measure of the spherical wavefront that can be used to infer the source position using only a single large array. In this paper, we study a near-field tracking problem for inferring the state (i.e., the position and velocity) of a moving source with an ad-hoc observation model that accounts for the phase profile of a large receiving array. For this tracking problem, we derive the posterior Cramér-Rao Lower Bound (P-CRLB) and show the effects when the source moves inside and outside the Fresnel region. We provide insights on how the loss of positioning information outside Fresnel comes from an increase of the ranging error rather than from inaccuracies of angular estimation. Then, we investigate the performance of different Bayesian tracking algorithms in the presence of model mismatches and abrupt trajectory changes. Our results demonstrate the feasibility and high accuracy for most of the tracking approaches without the need of wideband signals and of any synchronization scheme. signals and of any synchronization scheme.

cs.IT↗

arXiv2020

Radio Positioning with EM Processing of the Spherical Wavefront

Next 5G and beyond applications have brought a tremendous interest towards array systems employing an extremely large number of antennas, so that the technology that might be in place for communication can be also exploited for positioning. In particular, in this paper we investigate the possibility to infer the position of an omnidirectional transmitter by retrieving the information from the incident spherical wavefront through its EM processing. Despite such a post-processing of the curvature wavefront has been mainly considered in the past at microwave and acoustic frequencies using extremely large antennas, it is of interest to explore the opportunities offered in the context of next 5G and beyond systems. Thus, differently from the state-of-the-art, here we first introduce a dedicated general model for different EM processing configurations, and successively we investigate the trade-off between the attainable positioning performance and the complexity offered by the different architectures, that might entail or not the use of a lens, that can be either reconfigurable or not. Indeed, we analyze also the effect of the interference, in order to evaluate the robustness of the considered system to the presence of multiple simultaneous transmitting sources. Results, obtained for different number of antennas, i.e., for different array apertures, confirm the possibility to achieve interesting positioning performance using a single antenna array with limited dimensions.

eess.SP↗

arXiv2020

Reconfigurable Intelligent Surfaces for Localization: Position and Orientation Error Bounds

Next-generation cellular networks will witness the creation of smart radio environments (SREs), where walls and objects can be coated with reconfigurable intelligent surfaces (RISs) to strengthen the communication and localization coverage by controlling the reflected multipath. In fact, RISs have been recently introduced not only to overcome communication blockages due to obstacles but also for high-precision localization of mobile users in GPS denied environments, e.g., indoors. Towards this vision, this paper presents the localization performance limits for communication scenarios where a single next-generation NodeB base station (gNB), equipped with multiple-antennas, infers the position and the orientation of the user equipment(UE) in a RIS-assisted SRE. We consider a signal model that is valid also for near-field propagation conditions, as the usually adopted far-field assumption does not always hold, especially for large RISs. For the considered scenario, we derive the Cramer-Rao lower bound (CRLB) for assessing the ultimate localization and orientation performance of synchronous and asynchronous signaling schemes. In addition, we propose a closed-form RIS phase profile that well suits joint communication and localization. We perform extensive numerical results to assess the performance of our scheme for various localization scenarios and RIS phase design. Numerical results show that the proposed scheme can achieve remarkable performance, even in asynchronous signaling and that the proposed phase design approaches the numerical optimal phase design that minimizes the CRLB.

cs.IT↗

arXiv2020

Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection

In this paper, we study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown environment. The goal is to optimize its trajectory with the purpose of maximizing the mapping accuracy and, at the same time, to avoid areas where measurements might not be sufficiently informative from the perspective of a target detection. This problem is formulated as a Markov decision process (MDP) where the UAV is an agent that runs either a state estimator for target detection and for environment mapping, and a reinforcement learning (RL) algorithm to infer its own policy of navigation (i.e., the control law). Numerical results show the feasibility of the proposed idea, highlighting the UAV's capability of autonomously exploring areas with high probability of target detection while reconstructing the surrounding environment.

cs.RO↗

arXiv2019

Automatic Mapping of the Indoor World with Personal Radars

Digital maps will revolutionize our experience of perceiving and navigating indoor environments. While today we rely only on the representation of the outdoors, the mapping of indoors is mainly a part of the traditional SLAM problem where robots discover the surrounding and perform self-localization. Nonetheless, robot deployment prevents from a large diffusion and fast mapping of indoors and, further, they are usually equipped with laser and vision technology that fail in scarce visibility conditions. To this end, a possible solution is to turn future personal devices into personal radars as a milestone towards the automatic generation of indoor maps using massive array technology at millimeter-waves, already in place for communications. In this application-oriented paper, we will describe the main achievements attained so far to develop the personal radar concept, using ad-hoc collected experimental data, and by discussing possible future directions of investigation.

eess.SP↗

arXiv2017

Single Anchor Localization and Orientation Performance Limits using Massive Arrays: MIMO vs. Beamforming

Next generation cellular networks will experience the combination of femtocells, millimeter-wave (mm-wave) communications and massive antenna arrays. Thanks to the beamforming capability as well as the high angular resolution provided by massive arrays, only one single access point (AP) acting as an anchor node could be used for localization estimation, thus avoiding over-sized infrastructures dedicated to positioning. In this context, our paper aims at investigating the localization and orientation performance limits employing massive arrays both at the AP and mobile side. Thus, we first asymptotically demonstrate the tightness of the Cramer-Rao bound (CRB) in massive array regime, and in the presence or not of multipath. Successively, we propose a comparison between MIMO and beamforming in terms of array structure, time synchronization error and multipath components. Among different array configurations, we consider also random weighting as a trade-off between the high diversity gain of MIMO and the high directivity guaranteed by phased arrays. By evaluating the CRB for the different array configurations, results show the interplay between diversity and beamforming gain as well as the benefits achievable by varying the number of array elements in terms of localization accuracy.

cs.IT↗