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Large language models have significantly transformed multiple fields with their exceptional performance in natural language tasks, but their deployment in resource-constrained environments like edge networks presents an ongoing challenge. Decentralized techniques for inference have emerged, distributing the model blocks among multiple devices to improve flexibility and cost effectiveness. However, energy limitations remain a significant concern for edge devices. We propose a sustainable model for collaborative inference on interconnected, battery-powered edge devices with energy harvesting. A semi-Markov model is developed to describe the states of the devices, considering processing parameters and average green energy arrivals. This informs the design of scheduling algorithms that aim to minimize device downtimes and maximize network throughput. Through empirical evaluations and simulated runs, we validate the effectiveness of our approach, paving the way for energy-efficient decentralized inference over edge networks.

Integrated Sensing And Communication (ISAC ) systems are expected to perform accurate radar sensing while having minimal impact on communication. Ideally, sensing should only reuse communication resources, especially for spectrum which is contended by many applications. However, this poses a great challenge in that communication systems often operate on narrow subbands with low sensing resolution. Combining contiguous subbands has shown significant resolution gain in active localization. However, multiband ISAC remains unexplored due to communication subbands being highly sparse (non-contiguous) and affected by phase offsets that prevent their aggregation (incoherent). To tackle these problems, we design HiSAC, the first multiband ISAC system that combines diverse subbands across a wide frequency range to achieve super-resolved passive ranging. To solve the non-contiguity and incoherence of subbands, HiSAC combines them progressively, exploiting an anchor propagation path between transmitter and receiver in an optimization problem to achieve phase coherence. HiSAC fully reuses pilot signals in communication systems, it applies to different frequencies and can combine diverse technologies, e.g., 5G-NR and WiGig. We implement HiSAC on an experimental platform in the millimeter-wave unlicensed band and test it on objects and humans. Our results show it enhances the sensing resolution by up to 20 times compared to single-band processing while occupying the same spectrum., Comment: 15 pages, 15 figures

In this letter, we present for the first time a method to estimate the bistatic Doppler frequency of a target with clock asynchronous and mobile Integrated Sensing And Communication (ISAC) devices. Existing approaches have separately tackled the presence of phase offsets due to clock asynchrony or the additional Doppler shift due to device movement. However, in real ISAC scenarios, these two sources of phase nuisance are concurrently present, making the estimation of the target's Doppler frequency particularly challenging. Our method solves the problem using the sole wireless signal at the receiver, exploiting the invariance of phase offsets across multipath components and the bistatic geometry in an original way. The proposed method is validated via simulation, exploring the impact of different system parameters. Numerical results show that our approach is a viable way of estimating Doppler frequency in bistatic asynchronous ISAC scenarios with mobile devices.

Integrated Sensing and Communication (ISAC) has been identified as a pillar usage scenario for the impending 6G era. Bi-static sensing, a major type of sensing in ISAC, is promising to expedite ISAC in the near future, as it requires minimal changes to the existing network infrastructure. However, a critical challenge for bi-static sensing is clock asynchronism due to the use of different clocks at far-separated transmitters and receivers. This causes the received signal to be affected by time-varying random phase offsets, severely degrading, or even failing, direct sensing. Hence, to effectively enable ISAC, considerable research has been directed toward addressing the clock asynchronism issue in bi-static sensing. This paper provides an overview of the issue and existing techniques developed in an ISAC background. Based on the review and comparison, we also draw insights into the future research directions and open problems, aiming to nurture the maturation of bi-static sensing in ISAC., Comment: 20 pages, 6 figures, 1 table

Radio Frequency (RF) sensing holds the potential for enabling pervasive monitoring applications. However, modern sensing algorithms imply complex operations, which clash with the energy-constrained nature of edge sensing devices. This calls for the development of new processing and learning techniques that can strike a suitable balance between performance and energy efficiency. Spiking Neural Networks (SNNs) have recently emerged as an energy-efficient alternative to conventional neural networks for edge computing applications. They process information in the form of sparse binary spike trains, thus potentially reducing energy consumption by several orders of magnitude. Their fruitful use for RF signal processing critically depends on the representation of RF signals in the form of spike signals. We underline that existing spike encoding algorithms to do so generally produce inaccurate signal representations and dense (i.e., inefficient) spike trains. In this work, we propose a lightweight neural architecture that learns a tailored spike encoding representations of RF channel responses by jointly reconstructing the input and its spectral content. By leveraging a tunable regularization term, our approach enables fine-grained control over the performance-energy trade-off of the system. Our numerical results show that the proposed method outperforms existing encoding algorithms in terms of reconstruction error and sparsity of the obtained spike encodings., Comment: 6 pages, 5figures, 2 tables

Assisted and autonomous driving are rapidly gaining momentum and will soon become a reality. Artificial intelligence and machine learning are regarded as key enablers thanks to the massive amount of data that smart vehicles will collect from onboard sensors. Federated learning is one of the most promising techniques for training global machine learning models while preserving data privacy of vehicles and optimizing communications resource usage. In this article, we propose vehicular radio environment map federated learning (VREM-FL), a computation-scheduling co-design for vehicular federated learning that combines mobility of vehicles with 5G radio environment maps. VREM-FL jointly optimizes learning performance of the global model and wisely allocates communication and computation resources. This is achieved by orchestrating local computations at the vehicles in conjunction with transmission of their local models in an adaptive and predictive fashion, by exploiting radio channel maps. The proposed algorithm can be tuned to trade training time for radio resource usage. Experimental results demonstrate that VREM-FL outperforms literature benchmarks for both a linear regression model (learning time reduced by 28%) and a deep neural network for semantic image segmentation (doubling the number of model updates within the same time window)., Comment: Copyright (c) 2024 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org

We prototype and validate a multistatic mmWave ISAC system based on IEEE802.11ay. Compensation of the clock asynchrony between each TX and RX pair is performed using the sole LoS wireless signal propagation. As a result, our system provides concurrent target tracking and micro-Doppler estimation from multiple points of view, paving the way for practical multistatic data fusion. Our results on human movement sensing, complemented with precise, quantitative GT data, demonstrate the enhanced sensing capabilities of multistatic ISAC, due to the spatial diversity of the receiver nodes.

The reconstruction of micro-Doppler signatures of human movements is a key enabler for fine-grained activity recognition wireless sensing. In Joint Communication and Sensing (JCS) systems, unlike in dedicated radar sensing systems, a suitable trade-off between sensing accuracy and communication overhead has to be attained. It follows that the micro-Doppler has to be reconstructed from incomplete windows of channel estimates obtained from communication packets. Existing approaches exploit compressed sensing, but produce very poor reconstructions when only a few channel measurements are available, which is often the case with real communication patterns. In addition, the large number of iterations they need to converge hinders their use in real-time systems. In this work, we propose and validate STAR, a neural network that reconstructs micro-Doppler sequences of human movement even from highly incomplete channel measurements. STAR is based upon a new architectural design that combines a single unrolled iterative hard-thresholding layer with an attention mechanism, used at its output. This results in an interpretable and lightweight architecture that reaps the benefits of both model-based and data driven solutions. STAR is evaluated on a public JCS dataset of 60 GHz channel measurements of human activity traces. Experimental results show that it substantially outperforms state-of-the-art techniques in terms of the reconstructed micro-Doppler quality. Remarkably, STAR enables human activity recognition with satisfactory accuracy even with 90% of missing channel measurements, for which existing techniques fail., Comment: 16 pages, 10 figures, 6 tables

Power efficiency is a crucial consideration for embedded systems design, particularly in the field of edge computing and IoT devices. This study aims to calibrate the power measurements obtained from the built-in sensors of NVIDIA Jetson devices, facilitating the collection of reliable and precise power consumption data in real-time. To achieve this goal, accurate power readings are obtained using external hardware, and a regression model is proposed to map the sensor measurements to the true power values. Our results provide insights into the accuracy and reliability of the built-in power sensors for various Jetson edge boards and highlight the importance of calibrating their internal power readings. In detail, internal sensors underestimate the actual power by up to 50% in most cases, but this calibration reduces the error to within 3%. By making the internal sensor data usable for precise online assessment of power and energy figures, the regression models presented in this paper have practical applications, for both practitioners and researchers, in accurately designing energy-efficient and autonomous edge services., Comment: 5 pages, 5 figures, IEEE Edge 2023 Conference

In this paper we present DISC, a dataset of millimeter-wave channel impulse response measurements for integrated human activity sensing and communication. This is the first dataset collected with a software-defined radio testbed that transmits 60 GHz IEEE 802-11ay-compliant packets and estimates the channel response including reflections of the signal on the moving body parts of subjects moving in an indoor environment. The provided data contain the contribution of 7 subjects performing 4 different activities. Differently from available radar-based millimeter-wave sensing datasets, our measurements are collected using both uniform packet transmission times and sparse traffic patterns from real Wi-Fi deployments. Thanks to these unique characteristics, DISC serves as a multi-purpose benchmarking tool for machine learning-based human activity recognition, radio frequency gait analysis, and sparse sensing algorithms for next-generation integrated sensing and communication.

Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms. These are, in fact, desirable qualities for DO frameworks, such as federated edge learning techniques, in the presence of data and system heterogeneity, and in scenarios where internode communication is the main bottleneck. Although computationally demanding, Newton-type (NT) methods have been recently advocated as enablers of robust convergence rates in challenging DO problems where edge devices have sufficient computational power. Along these lines, in this work we propose Q-SHED, an original NT algorithm for DO featuring a novel bit-allocation scheme based on incremental Hessian eigenvectors quantization. The proposed technique is integrated with the recent SHED algorithm, from which it inherits appealing features like the small number of required Hessian computations, while being bandwidth-versatile at a bit-resolution level. Our empirical evaluation against competing approaches shows that Q-SHED can reduce by up to 60% the number of communication rounds required for convergence.

In the last years, several machine learning-based techniques have been proposed to monitor human movements from Wi-Fi channel readings. However, the development of domain-adaptive algorithms that robustly work across different environments is still an open problem, whose solution requires large datasets characterized by strong domain diversity, in terms of environments, persons and Wi-Fi hardware. To date, the few public datasets available are mostly obsolete - as obtained via Wi-Fi devices operating on 20 or 40 MHz bands - and contain little or no domain diversity, thus dramatically limiting the advancements in the design of sensing algorithms. The present contribution aims to fill this gap by providing a dataset of IEEE 802.11ac channel measurements over an 80 MHz bandwidth channel featuring notable domain diversity, through measurement campaigns that involved thirteen subjects across different environments, days, and with different hardware. Novel experimental data is provided by blocking the direct path between the transmitter and the monitor, and collecting measurements in a semi-anechoic chamber (no multi-path fading). Overall, the dataset - available on IEEE DataPort [1] - contains more than thirteen hours of channel state information readings (23.6 GB), allowing researchers to test activity/identity recognition and people counting algorithms.

Wideband millimeter-wave communication systems can be extended to provide radar-like sensing capabilities on top of data communication, in a cost-effective manner. However, the development of joint communication and sensing technology is hindered by practical challenges, such as occlusions to the line-of-sight path and clock asynchrony between devices. The latter introduces time-varying timing and frequency offsets that prevent the estimation of sensing parameters and, in turn, the use of standard signal processing solutions. Existing approaches cannot be applied to commonly used phased-array receivers, as they build on stringent assumptions about the multipath environment, and are computationally complex. We present JUMP, the first system enabling practical bistatic and asynchronous joint communication and sensing, while achieving accurate target tracking and micro-Doppler extraction in realistic conditions. Our system compensates for the timing offset by exploiting the channel correlation across subsequent packets. Further, it tracks multipath reflections and eliminates frequency offsets by observing the phase of a dynamically-selected static reference path. JUMP has been implemented on a 60 GHz experimental platform, performing extensive evaluations of human motion sensing, including non-line-of-sight scenarios. In our results, JUMP attains comparable tracking performance to a full-duplex monostatic system and similar micro-Doppler quality with respect to a phase-locked bistatic receiver., Comment: 17 pages, 18 figures

The present paper is aimed to discussing three kinds of problems: (1) producing some ``mirror theorem'' for the recent mirror symmetric construction, called \emph{framed} duality ($f$-duality), described in \cite{R-fTV} and \cite{R-fpCI}: this is performed from the point of view proposed by Homological Mirror Symmetry (HMS), by studying \emph{derived equivalence} ($D$-equivalence) of multiple mirror models produced by means of a, so-called, \emph{uncalibrated $f$-process}; (2) proposing a general construction giving a big number of multiple mirror models to, in principle, any projective complete intersection of non-negative Kodaira dimension: these multiple mirrors turn out to be each other connected by means of uncalibrated $f$-processes and then, after (1), $D$-equivalent or $K$-equivalent, in the sense of Kawamata \cite{Kawamata}; (3) presenting a number of evidences for the Bondal-Orlov-Kawamata conjecture that $D$-equivalence is $K$-equivalence, and viceversa., Comment: 1 figure, 3 appendices; 71 pages. Comments are welcome. v3: An inaccuracy in proving Thm. 1.3 and Thm. 4.7 has been fixed. Some further minor changes resulting. References updated

The increasing demand for edge computing is leading to a rise in energy consumption from edge devices, which can have significant environmental and financial implications. To address this, in this paper we present a novel method to enhance the energy efficiency while speeding up computations by distributing the workload among multiple containers in an edge device. Experiments are conducted on two Nvidia Jetson edge boards, the TX2 and the AGX Orin, exploring how using a different number of containers can affect the energy consumption and the computational time for an inference task. To demonstrate the effectiveness of our splitting approach, a video object detection task is conducted using an embedded version of the state-of-the-art YOLO algorithm, quantifying the energy and the time savings achieved compared to doing the computations on a single container. The proposed method can help mitigate the environmental and economic consequences of high energy consumption in edge computing, by providing a more sustainable approach to managing the workload of edge devices.

Recently, there has been a trend of shifting the execution of deep learning inference tasks toward the edge of the network, closer to the user, to reduce latency and preserve data privacy. At the same time, growing interest is being devoted to the energetic sustainability of machine learning. At the intersection of these trends, we hence find the energetic characterization of machine learning at the edge, which is attracting increasing attention. Unfortunately, calculating the energy consumption of a given neural network during inference is complicated by the heterogeneity of the possible underlying hardware implementation. In this work, we hence aim at profiling the energetic consumption of inference tasks for some modern edge nodes and deriving simple but realistic models. To this end, we performed a large number of experiments to collect the energy consumption of convolutional and fully connected layers on two well-known edge boards by NVIDIA, namely Jetson TX2 and Xavier. From the measurements, we have then distilled a simple, practical model that can provide an estimate of the energy consumption of a certain inference task on the considered boards. We believe that this model can be used in many contexts as, for instance, to guide the search for efficient architectures in Neural Architecture Search, as a heuristic in Neural Network pruning, or to find energy-efficient offloading strategies in a Split computing context, or simply to evaluate the energetic performance of Deep Neural Network architectures.

Mobile networks are becoming energy hungry, and this trend is expected to continue due to a surge in communication and computation demand. Multi-access Edge Computing (MEC), will entail energy-consuming services and applications, with non-negligible impact in terms of ecological sustainability. In this paper, we provide a comprehensive review of existing approaches to make edge computing networks greener, including but not limited to the exploitation of renewable energy resources, and context-awareness. We hence provide an updated account of recent developments on MEC from an energetic sustainability perspective, addressing the initial deployment of computing resources, their dynamic (re)allocation, as well as distributed and federated learning designs. In doing so, we highlight the energy aspects of these algorithms, advocating the need for energy-sustainable edge computing systems that are aligned with Sustainable Development Goals (SDGs) and the Paris agreement. To the best of our knowledge, this is the first work providing a systematic literature review on the efficient deployment and management of energy harvesting MEC, with special focus on the deployment, provisioning, and scheduling of computing tasks, including federated learning for distributed edge intelligence, toward making edge networks greener and more sustainable. At the end of the paper, open research avenues and challenges are identified for all the surveyed topics., Comment: 31 pages, submitted to ACM Computing Surveys

Millimeter wave (mmWave) radar sensors are emerging as valid alternatives to cameras for the pervasive contactless monitoring of people in indoor spaces. However, commercial mmWave radars feature a limited range (up to $6$-$8$ m) and are subject to occlusion, which may constitute a significant drawback in large, crowded rooms characterized by a challenging multipath environment. Thus, covering large indoor spaces requires multiple radars with known relative position and orientation and algorithms to combine their outputs. In this work, we present ORACLE, an autonomous system that (i) integrates automatic relative position and orientation estimation from multiple radar devices by exploiting the trajectories of people moving freely in the radars' common fields of view, and (ii) fuses the tracking information from multiple radars to obtain a unified tracking among all sensors. Our implementation and experimental evaluation of ORACLE results in median errors of $0.12$ m and $0.03^\circ$ for radars location and orientation estimates, respectively. Fused tracking improves the mean target tracking accuracy by $27\%$, and the mean tracking error is $23$ cm in the most challenging case of $3$ moving targets. Finally, ORACLE does not show significant performance reduction when the fusion rate is reduced to up to 1/5 of the frame rate of the single radar sensors, thus being amenable to a lightweight implementation on a resource-constrained fusion center.

mmWave radars have recently gathered significant attention as a means to track human movement within indoor environments. Widely adopted Kalman filter tracking methods experience performance degradation when the underlying movement is highly non-linear or presents long-term temporal dependencies. As a solution, in this article we design a convolutional-recurrent Neural Network (NN) that learns to accurately estimate the position and the velocity of the monitored subjects from high dimensional radar data. The NN is trained as a probabilistic model, utilizing a Gaussian negative log-likelihood loss function, obtaining explicit uncertainty estimates at its output, in the form of time-varying error covariance matrices. A thorough experimental assessment is conducted using a 77 GHz FMCW radar. The proposed architecture, besides allowing one to gauge the uncertainty in the tracking process, also leads to greatly improved performance against the best approaches from the literature, i.e., Kalman filtering, lowering the average error against the ground truth from 32.8 to 7.59 cm and from 56.8 to 14 cm/s in terms of position and velocity tracking, respectively., Comment: 5 pages, 4 figures, 1 table

A well established method to detect and classify human movements using Millimeter-Wave ( mmWave) devices is the time-frequency analysis of the small-scale Doppler effect (termed micro-Doppler) of the different body parts, which requires a regularly spaced and dense sampling of the Channel Impulse Response ( CIR). This is currently done in the literature either using special-purpose radar sensors, or interrupting communications to transmit dedicated sensing waveforms, entailing high overhead and channel utilization. In this work we present SPARCS, an integrated human sensing and communication solution for mmWave systems. SPARCS is the first method that reconstructs high quality signatures of human movement from irregular and sparse CIR samples, such as the ones obtained during communication traffic patterns. To accomplish this, we formulate the micro-Doppler extraction as a sparse recovery problem, which is critical to enable a smooth integration between communication and sensing. Moreover, if needed, our system can seamlessly inject short CIR estimation fields into the channel whenever communication traffic is absent or insufficient for the micro-Doppler extraction. SPARCS effectively leverages the intrinsic sparsity of the mmWave channel, thus drastically reducing the sensing overhead with respect to available approaches. We implemented SPARCS on an IEEE 802.11ay Software Defined Radio (SDR) platform working in the 60 GHz band, collecting standard-compliant CIR traces matching the traffic patterns of real WiFi access points. Our results show that the micro-Doppler signatures obtained by SPARCS enable a typical downstream application such as human activity recognition with more than 7 times lower overhead with respect to existing methods, while achieving better recognition performance., Comment: 13 pages, 9 figures, 2 tables

We present DeepCSI, a novel approach to Wi-Fi radio fingerprinting (RFP) which leverages standard-compliant beamforming feedback matrices to authenticate MU-MIMO Wi-Fi devices on the move. By capturing unique imperfections in off-the-shelf radio circuitry, RFP techniques can identify wireless devices directly at the physical layer, allowing low-latency low-energy cryptography-free authentication. However, existing Wi-Fi RFP techniques are based on software-defined radio (SDRs), which may ultimately prevent their widespread adoption. Moreover, it is unclear whether existing strategies can work in the presence of MU-MIMO transmitters - a key technology in modern Wi-Fi standards. Conversely from prior work, DeepCSI does not require SDR technologies and can be run on any low-cost Wi-Fi device to authenticate MU-MIMO transmitters. Our key intuition is that imperfections in the transmitter's radio circuitry percolate onto the beamforming feedback matrix, and thus RFP can be performed without explicit channel state information (CSI) computation. DeepCSI is robust to inter-stream and inter-user interference being the beamforming feedback not affected by those phenomena. We extensively evaluate the performance of DeepCSI through a massive data collection campaign performed in the wild with off-the-shelf equipment, where 10 MU-MIMO Wi-Fi radios emit signals in different positions. Experimental results indicate that DeepCSI correctly identifies the transmitter with an accuracy of up to 98%. The identification accuracy remains above 82% when the device moves within the environment. To allow replicability and provide a performance benchmark, we pledge to share the 800 GB datasets - collected in static and, for the first time, dynamic conditions - and the code database with the community., Comment: To be presented at the 42nd IEEE International Conference on Distributed Computing Systems (ICDCS), Bologna, Italy, July 10-13, 2022

There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL). In particular, much attention is being turned to FL scenarios where the network is strongly heterogeneous in terms of communication resources (e.g., bandwidth) and data distribution. In these cases, communication between local machines (agents) and the central server (Master) is a main consideration. In this work, we present SHED, an original communication-constrained Newton-type (NT) algorithm designed to accelerate FL in such heterogeneous scenarios. SHED is by design robust to non i.i.d. data distributions, handles heterogeneity of agents' communication resources (CRs), only requires sporadic Hessian computations, and achieves super-linear convergence. This is possible thanks to an incremental strategy, based on eigendecomposition of the local Hessian matrices, which exploits (possibly) outdated second-order information. The proposed solution is thoroughly validated on real datasets by assessing (i) the number of communication rounds required for convergence, (ii) the overall amount of data transmitted and (iii) the number of local Hessian computations. For all these metrics, the proposed approach shows superior performance against state-of-the art techniques like GIANT and FedNL., Comment: The name of the algorithm has been changed to SHED (Sharing Hessian Eigenvectors for Distributed learning). Section 3 has been enriched with an example Figure. In Sec. 5, the main theorems have been updated and improved, explicit sufficient conditions for the beginning of the linear convergence phase have been provided and conditions for super linear convergence have been made more general

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)

The energy sustainability of multi-access edge computing (MEC) platforms is here addressed by developing Energy-Aware job Scheduling at the Edge (EASE), a computing resource scheduler for edge servers co-powered by renewable energy resources and the power grid. The scenario under study involves the optimal allocation and migration of time-sensitive computing tasks in a resource-constrained internet of vehicles (IoV) context. This is achieved by tackling, as the main objective, the minimization of the carbon footprint of the edge network, whilst delivering adequate quality of service (QoS) to the end users (e.g., meeting task execution deadlines). EASE integrates i) a centralized optimization step, solved through model predictive control (MPC), to manage the renewable energy that is locally collected at the edge servers and their local computing resources, estimating their future availability, and ii) a distributed consensus step, solved via dual ascent in closed form, to reach agreement on service migrations. EASE is compared with four existing migration strategies. Quantitative results demonstrate its greater energy efficiency, which often gets close to complete carbon neutrality, while also improving the QoS., 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

Social distancing and temperature screening have been widely employed to counteract the COVID-19 pandemic, sparking great interest from academia, industry and public administrations worldwide. While most solutions have dealt with these aspects separately, their combination would greatly benefit the continuous monitoring of public spaces and help trigger effective countermeasures. This work presents milliTRACE-IR, a joint mmWave radar and infrared imaging sensing system performing unobtrusive and privacy preserving human body temperature screening and contact tracing in indoor spaces. milliTRACE-IR combines, via a robust sensor fusion approach, mmWave radars and infrared thermal cameras. It achieves fully automated measurement of distancing and body temperature, by jointly tracking the subjects's faces in the thermal camera image plane and the human motion in the radar reference system. Moreover, milliTRACE-IR performs contact tracing: a person with high body temperature is reliably detected by the thermal camera sensor and subsequently traced across a large indoor area in a non-invasive way by the radars. When entering a new room, a subject is re-identified among several other individuals by computing gait-related features from the radar reflections through a deep neural network and using a weighted extreme learning machine as the final re-identification tool. Experimental results, obtained from a real implementation of milliTRACE-IR, demonstrate decimeter-level accuracy in distance/trajectory estimation, inter-personal distance estimation (effective for subjects getting as close as 0.2 m), and accurate temperature monitoring (max. errors of 0.5{\deg}C). Furthermore, milliTRACE-IR provides contact tracing through highly accurate (95%) person re-identification, in less than 20 seconds., Comment: 16 pages, 18 figures, 7 tables

In this work we present RAPID, the first joint communication and radar system based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz band. Unlike existing approaches for human sensing at millimeter-wave frequencies, which rely on special-purpose radars, RAPID achieves radar-level sensing accuracy with IEEE 802.11ay access points, thus avoiding the burden of installing ad-hoc sensors. RAPID enables contactless human sensing applications, such as people tracking, Human Activity Recognition (HAR), and person identification without requiring modifications to the standard packet structure. Specifically, we leverage IEEE 802.11ay beam training to accurately localize and track multiple individuals within the same environment. Then, we propose a new way of using beam tracking to extract micro-Doppler signatures from the time-varying Channel Impulse Response (CIR) estimated from reflected packets. Such signatures are fed to a deep learning classifier to perform HAR and person identification. RAPID is implemented on a cutting-edge IEEE 802.11ay-compatible FPGA platform with phased antenna arrays, and evaluated on a large dataset of CIR measurements. It is robust across different environments and subjects, and outperforms state-of-the-art sub-6 GHz WiFi sensing techniques. Using two access points, RAPID reliably tracks multiple subjects, reaching HAR and person identification accuracies of 94% and 90%, respectively., Comment: 18 pages, 21 figures, 5 tables

Mm-wave radars have recently gathered significant attention as a means to track human movement and identify subjects from their gait characteristics. A widely adopted method to perform the identification is the extraction of the micro-Doppler signature of the targets, which is computationally demanding in case of co-existing multiple targets within the monitored physical space. Such computational complexity is the main problem of state-of-the-art approaches, and makes them inapt for real-time use. In this work, we present an end-to-end, low-complexity but highly accurate method to track and identify multiple subjects in real-time using the sparse point-cloud sequences obtained from a low-cost mm-wave radar. Our proposed system features an extended object tracking Kalman filter, used to estimate the position, shape and extension of the subjects, which is integrated with a novel deep learning classifier, specifically tailored for effective feature extraction and fast inference on radar point-clouds. The proposed method is thoroughly evaluated on an edge-computing platform from NVIDIA (Jetson series), obtaining greatly reduced execution times (reduced complexity) against the best approaches from the literature. Specifically, it achieves accuracies as high as 91.62%, operating at 15 frames per seconds, in identifying three subjects that concurrently and freely move in an unseen indoor environment, among a group of eight., Comment: 17 pages, 17 figures, 6 tables

In this article we present SHARP, an original approach for obtaining human activity recognition (HAR) through the use of commercial IEEE 802.11 (Wi-Fi) devices. SHARP grants the possibility to discern the activities of different persons, across different time-spans and environments. To achieve this, we devise a new technique to clean and process the channel frequency response (CFR) phase of the Wi-Fi channel, obtaining an estimate of the Doppler shift at a radio monitor device. The Doppler shift reveals the presence of moving scatterers in the environment, while not being affected by (environment-specific) static objects. SHARP is trained on data collected as a person performs seven different activities in a single environment. It is then tested on different setups, to assess its performance as the person, the day and/or the environment change with respect to those considered at training time. In the worst-case scenario, it reaches an average accuracy higher than 95%, validating the effectiveness of the extracted Doppler information, used in conjunction with a learning algorithm based on a neural network, in recognizing human activities in a subject and environment independent way. The collected CFR dataset and the code are publicly available for replicability and benchmarking purposes., Comment: This work has been published in IEEE Transactions on Mobile Computing (Open Access)

This paper is devoted to systematically extend $f$-mirror symmetry between families of hypersurfaces in complete toric varieties, as introduced in \cite{R-fTV}, to families of complete intersections subvarieties. Namely, $f$-mirror symmetry is induced by framed duality of framed toric varieties extending Batyrev-Borisov polar duality between Fano toric varieties. Framed duality has been defined and essentially well described for families of hypersurfaces in toric varieties in the previous \cite{R-fTV}. Here it is developed for families of complete intersections, allowing us to strengthening some previous results on hypersurfaces. In particular, the class of projective complete intersections and their mirror partners are studied in detail. Moreover, a (generalized) Landau-Ginzburg/Complete-Intersection correspondence is discussed, extending to the complete intersection setup the LG/CY correspondence firstly studied Chiodo-Ruan and Krawitz., Comment: v4: Abstract and Introduction modified and some other minor modifications, according with the Referee's report. Final version to be published in the Journal of Geometry and Physics; 57 pages

We present two algorithms determining all the complete and simplicial fans admitting a fixed non-degenerate set of vectors $V$ as generators of their 1-skeleton. The interplay of the two algorithms allows us to discerning if the associated toric varieties admit a projective embedding, in principle for any values of dimension and Picard number. The first algorithm is slower than the second one, but it computes all complete and simplicial fans supported by $V$ and lead us to formulate a topological-combinatoric conjecture about the definition of a fan. On the other hand, we adapt the Sturmfels' arguments on the Gr\"obner fan of toric ideals to our complete case; we give a characterization of the Gr\"obner region and show an explicit correspondence between Gr\"obner cones and chambers of the secondary fan. A homogenization procedure of the toric ideal associated to $V$ allows us to employing GFAN and related software in producing our second algorithm. The latter turns out to be much faster than the former, although it can compute only the projective fans supported by $V$. We provide examples and a list of open problems. In particular we give examples of rationally parametrized families of $\Q$-factorial complete toric varieties behaving in opposite way with respect to the dimensional jump of the nef cone over a special fibre., Comment: 18 pages, 2 figures. Final version accepted for publication in the special issue of the Journal AAAECC, concerning "Algebraic Geometry from an Algorithmic point of View"

The present paper is dedicated to illustrating an extension of polar duality between Fano toric varieties to a more general duality, called \emph{framed} duality, so giving rise to a powerful and unified method of producing mirror partners of hypersurfaces and complete intersections in toric varieties, of any Kodaira dimension. In particular, the class of projective hypersurfaces and their mirror partners are studied in detail. Moreover, many connections with known Landau-Ginzburg mirror models, Homological Mirror Symmetry and Intrinsic Mirror Symmetry, are discussed., Comment: Major improvements suggested by the Referee, to whom all my thanks go. Final version to apper in Advances of Theoretical and Mathematical Physics

In this work, we investigate the use of backscattered mm-wave radio signals for the joint tracking and recognition of identities of humans as they move within indoor environments. We build a system that effectively works with multiple persons concurrently sharing and freely moving within the same indoor space. This leads to a complicated setting, which requires one to deal with the randomness and complexity of the resulting (composite) backscattered signal. The proposed system combines several processing steps: at first, the signal is filtered to remove artifacts, reflections and random noise that do not originate from humans. Hence, a density-based classification algorithm is executed to separate the Doppler signatures of different users. The final blocks are trajectory tracking and user identification, respectively based on Kalman filters and deep neural networks. Our results demonstrate that the integration of the last-mentioned processing stages is critical towards achieving robustness and accuracy in multi-user settings. Our technique is tested both on a single-target public dataset, for which it outperforms state-of-the-art methods, and on our own measurements, obtained with a 77 GHz radar on multiple subjects simultaneously moving in two different indoor environments. The system works in an online fashion, permitting the continuous identification of multiple subjects with accuracies up to 98%, e.g., with four subjects sharing the same physical space, and with a small accuracy reduction when tested with unseen data from a challenging real-life scenario that was not part of the model learning phase., Comment: 16 pages, 12 figures, 5 tables

Mobility may degrade the performance of next-generation vehicular networks operating at the millimeter-wave spectrum: frequent loss of alignment and blockages require repeated beam training and handover, thus incurring huge overhead. In this paper, an adaptive and joint design of beam training, data transmission and handover is proposed, that exploits the mobility process of mobile users and the dynamics of blockages to optimally trade-off throughput and power consumption. At each time slot, the serving base station decides to perform either beam training, data communication, or handover when blockage is detected. The problem is cast as a partially observable Markov decision process, and solved via an approximate dynamic programming algorithm based on PERSEUS [2]. Numerical results show that the PERSEUS-based policy performs near-optimally, and achieves a 55% gain in spectral efficiency compared to a baseline scheme with periodic beam training. Inspired by its structure, an adaptive heuristic policy is proposed with low computational complexity and small performance degradation., Comment: To appear in IEEE ICC 2020. arXiv admin note: substantial text overlap with arXiv:2002.11210

Mobility may degrade the performance of next-generation vehicular networks operating at the millimeter-wave spectrum: frequent mis-alignment and blockages require repeated beam training and handover, and incur enormous overhead. Nevertheless, mobility induces temporal correlations in the communication beams and in blockage events. In this paper, an adaptive design of beam training, data transmission and handover is proposed, that learns and exploits these temporal correlations to reduce the beam training overhead and optimally trade-off throughput and power consumption. At each time-slot, the serving base station (BS) decides to perform either beam training, data communication, or handover when blockage is detected, under uncertainty in the system state. The decision problem is cast as a partially observable Markov decision process, and the goal is to maximize the throughput delivered to the UE, under an average power constraint. To address the high dimensional optimization, an approximate constrained point-based value iteration (C-PBVI) method is developed, which simultaneously optimizes the primal and dual functions to meet the power constraint. Numerical results demonstrate a good match between the analysis and a simulation based on 2D mobility and 3D analog beamforming via uniform planar arrays at both BSs and UE, and reveal that C-PBVI performs near-optimally, and outperforms a baseline scheme with periodic beam training by 38% in spectral efficiency. Motivated by the structure of the C-PBVI policy, two heuristics are proposed, that trade complexity with sub-optimality, and achieve only 4% and 15% loss in spectral efficiency., Comment: To appear in IEEE Transaction on Vehicular Technology (TVT), 2020

Smartphones and wearable devices are fast growing technologies that, in conjunction with advances in wireless sensor hardware, are enabling ubiquitous sensing applications. Wearables are suitable for indoor and outdoor scenarios, can be placed on many parts of the human body and can integrate a large number of sensors capable of gathering physiological and behavioral biometric information. Here, we are concerned with gait analysis systems that extract meaningful information from a user's movements to identify anomalies and changes in their walking style. The solution that is put forward is subject-specific, as the designed feature extraction and classification tools are trained on the subject under observation. A smartphone mounted on an ad-hoc made chest support is utilized to gather inertial data and video signals from its built-in sensors and rear-facing camera. The collected video and inertial data are preprocessed, combined and then classified by means of a Recurrent Neural Network (RNN) based Sequence-to-Sequence (Seq2Seq) model, which is used as a feature extractor, and a following Convolutional Neural Network (CNN) classifier. This architecture provides excellent results, being able to correctly assess anomalies in 100% of the cases, for the considered tests, surpassing the performance of support vector machine classifiers.

The automatic classification of applications and services is an invaluable feature for new generation mobile networks. Here, we propose and validate algorithms to perform this task, at runtime, from the raw physical channel of an operative mobile network, without having to decode and/or decrypt the transmitted flows. Towards this, we decode Downlink Control Information (DCI) messages carried within the LTE Physical Downlink Control CHannel (PDCCH). DCI messages are sent by the radio cell in clear text and, in this paper, are utilized to classify the applications and services executed at the connected mobile terminals. Two datasets are collected through a large measurement campaign: one labeled, used to train the classification algorithms, and one unlabeled, collected from four radio cells in the metropolitan area of Barcelona, in Spain. Among other approaches, our Convolutional Neural Network (CNN) classifier provides the highest classification accuracy of 99%. The CNN classifier is then augmented with the capability of rejecting sessions whose patterns do not conform to those learned during the training phase, and is subsequently utilized to attain a fine grained decomposition of the traffic for the four monitored radio cells, in an online and unsupervised fashion.