Wireless Localization Systems: Statistical Modeling and Algorithm Design

Wireless localization systems are essential for emerging applications that rely on context-awareness, especially in civil, logistic, and security sectors. Accurate localization in indoor environments is still a challenge and triggers a fervent research activity worldwide. The performance of such systems relies on the quality of range measurements gathered by processing wireless signals within the sensors composing the localization system. Such range estimates serve as observations for the target position inference. The quality of range estimates depends on the network intrinsic properties and signal processing techniques. Therefore, the system design and analysis call for the statistical modeling of range information and the algorithm design for ranging, localization and tracking. The main objectives of this thesis are: (i) the derivation of statistical models and (ii) the design of algorithms for different wire- less localization systems, with particular regard to passive and semi-passive systems (i.e., active radar systems, passive radar systems, and radio frequency identification systems). Statistical models for the range information are derived, low-complexity algorithms with soft-decision and hard-decision are proposed, and several wideband localization systems have been analyzed. The research activity has been conducted also within the framework of different projects in collaboration with companies and other universities, and within a one-year-long research period at Massachusetts Institute of Technology, Cambridge, MA, USA. The analysis of system performance, the derived models, and the proposed algorithms are validated considering different case studies in realistic scenarios and also using the results obtained under the aforementioned projects

Adaptive Repetitions Strategies in IEEE 802.11bd

A new backward compatible WiFi amendment is under development by the IEEE bd Task Group towards the so-called IEEE 802.11bd, which includes the possibility to transmit up to three repetitions of the same packet. This feature increases time diversity and enables the use of maximum ratio combining (MRC) at the receiver to improve the probability of correct decoding. In this work, we first investigate the packet repetition feature and analyze how it looses its efficacy increasing the traffic as an higher number of transmissions may augment the channel load and collision probability. Then, we propose two strategies for adaptively selecting the number of transmissions leveraging on an adapted version of the channel busy ratio (CBR), which is measured at the transmitter and is an indicator of the channel load. The proposed strategies are validated through network-level simulations that account for both the acquisition and decoding processes. Results show that the proposed strategies ensure that devices use optimal settings under variable traffic conditions

Dominance of Smartphone Exposure in 5G Mobile Networks

The deployment of 5G networks is sometimes questioned due to the impact of ElectroMagnetic Field (EMF) generated by Radio Base Station (RBS) on users. The goal of this work is to analyze such issue from a novel perspective, by comparing RBS EMF against exposure generated by 5G smartphones in commercial deployments. The measurement of exposure from 5G is hampered by several implementation aspects, such as dual connectivity between 4G and 5G, spectrum fragmentation, and carrier aggregation. To face such issues, we deploy a novel framework, called 5G-EA, tailored to the assessment of smartphone and RBS exposure through an innovative measurement algorithm, able to remotely control a programmable spectrum analyzer. Results, obtained in both outdoor and indoor locations, reveal that smartphone exposure (upon generation of uplink traffic) dominates over the RBS one. Moreover, Line-of-Sight locations experience a reduction of around one order of magnitude on the overall exposure compared to Non-Line-of-Sight ones. In addition, 5G exposure always represents a small share (up to 28%) compared to 4G EMF

A Physical Layer Model for the Performance Evaluation of V2X Communication

Recent advancements in V2X communications have greatly increased the flexibility of the physical and MAC layers. The performance evaluation of V2X communication systems should take such flexibility into account through a cross-layer approach, which often leads to complex evaluation processes. Indeed, many performance evaluations presented in the literature rely on simple models to abstract the physical layer of the supported technologies. However, such models are usually not general, i.e., their applications are limited to specific scenarios or technologies, thus failing to reflect the flexibility of current V2X communications. Alternative solutions require computationally intensive simulations at the link level or non-trivial parameter tuning. The goal of this paper is to develop a new approach for modeling V2X communications at the physical layer. The approach is general for different technologies and has been validated through experimental data and system simulations with both IEEE 802.11p and sidelink LTE-V2X technologies

Measuring EMF and Throughput Before and After 5G Service Activation in a Residential Area

The deployment of 5G networks is approaching a mature phase in many countries across the world. However, little efforts have been done so far to scientifically compare ElectroMagnetic Field (EMF) exposure and traffic levels before and after the activation of 5G service over the territory. The goal of this work is to provide a sound comparative assessment of exposure and traffic, by performing repeated measurements before and after 5G provisioning service. Our solution is based on an EMF meter and a spectrum analyzer that is remotely controlled by a measurement algorithm. In this way, we dissect the contribution of each pre-5G and 5G band radiating over the territory. In addition, we employ a traffic chain to precisely characterize the achieved throughput levels. Results, derived from a set of measurements performed on a commercial deployment, reveal that the provisioning of 5G service over mid-band frequencies has a limited impact on the exposure. In parallel, the measured traffic is more than doubled when 5G is activated over mid-bands, reaching levels above 200 [Mbps]. On the other hand, the provisioning of 5G over sub-GHz bands does not introduce a substantial increase in the traffic levels. Eventually, we demonstrate that EMF exposure is impacted by the raw-land reconfiguration to host the 5G panels, which introduces changes in the sight conditions and in the power received from the main lobes

Expert clinical pharmacological advice may make an antimicrobial TDM program for emerging candidates more clinically useful in tailoring therapy of critically ill patients

Background Therapeutic drug monitoring (TDM) may represent an invaluable tool for optimizing antimicrobial therapy in septic patients, but extensive use is burdened by barriers. The aim of this study was to assess the impact of a newly established expert clinical pharmacological advice (ECPA) program in improving the clinical usefulness of an already existing TDM program for emerging candidates in tailoring antimicrobial therapy among critically ill patients. Methods This retrospective observational study included an organizational phase (OP) and an assessment phase (AP). During the OP (January-June 2021), specific actions were organized by MD clinical pharmacologists together with bioanalytical experts, clinical engineers, and ICU clinicians. During the AP (July-December 2021), the impact of these actions in optimizing antimicrobial treatment of the critically ill patients was assessed. Four indicators of performance of the TDM-guided real-time ECPA program were identified [total TDM-guided ECPAs July-December 2021/total TDM results July-December 2020; total ECPA dosing adjustments/total delivered ECPAs both at first assessment and overall; and turnaround time (TAT) of ECPAs, defined as optimal (< 12 h), quasi-optimal (12-24 h), acceptable (24-48 h), suboptimal (> 48 h)]. Results The OP allowed to implement new organizational procedures, to create a dedicated pathway in the intranet system, to offer educational webinars on clinical pharmacology of antimicrobials, and to establish a multidisciplinary team at the morning bedside ICU meeting. In the AP, a total of 640 ECPAs were provided for optimizing 261 courses of antimicrobial therapy in 166 critically ill patients. ECPAs concerned mainly piperacillin-tazobactam (41.8%) and meropenem (24.9%), and also other antimicrobials had >= 10 ECPAs (ceftazidime, ciprofloxacin, fluconazole, ganciclovir, levofloxacin, and linezolid). Overall, the pre-post-increase in TDM activity was of 13.3-fold. TDM-guided dosing adjustments were recommended at first assessment in 61.7% of ECPAs (10.7% increases and 51.0% decreases), and overall in 45.0% of ECPAs (10.0% increases and 35.0% decreases). The overall median TAT was optimal (7.7 h) and that of each single agent was always optimal or quasi-optimal. Conclusions Multidisciplinary approach and timely expert interpretation of TDM results by MD Clinical Pharmacologists could represent cornerstones in improving the cost-effectiveness of an antimicrobial TDM program for emerging TDM candidates

Predictive model for bacterial co-infection in patients hospitalized for COVID-19: a multicenter observational cohort study

Objective: The aim of our study was to build a predictive model able to stratify the risk of bacterial co-infection at hospitalization in patients with COVID-19. Methods: Multicenter observational study of adult patients hospitalized from February to December 2020 with confirmed COVID-19 diagnosis. Endpoint was microbiologically documented bacterial co-infection diagnosed within 72 h from hospitalization. The cohort was randomly split into derivation and validation cohort. To investigate risk factors for co-infection univariable and multivariable logistic regression analyses were performed. Predictive risk score was obtained assigning a point value corresponding to β-coefficients to the variables in the multivariable model. ROC analysis in the validation cohort was used to estimate prediction accuracy. Results: Overall, 1733 patients were analyzed: 61.4% males, median age 69 years (IQR 57-80), median Charlson 3 (IQR 2-6). Co-infection was diagnosed in 110 (6.3%) patients. Empirical antibiotics were started in 64.2 and 59.5% of patients with and without co-infection (p = 0.35). At multivariable analysis in the derivation cohort: WBC ≥ 7.7/mm3, PCT ≥ 0.2 ng/mL, and Charlson index ≥ 5 were risk factors for bacterial co-infection. A point was assigned to each variable obtaining a predictive score ranging from 0 to 5. In the validation cohort, ROC analysis showed AUC of 0.83 (95%CI 0.75-0.90). The optimal cut-point was ≥2 with sensitivity 70.0%, specificity 75.9%, positive predictive value 16.0% and negative predictive value 97.5%. According to individual risk score, patients were classified at low (point 0), intermediate (point 1), and high risk (point ≥ 2). CURB-65 ≥ 2 was further proposed to identify patients at intermediate risk who would benefit from early antibiotic coverage. Conclusions: Our score may be useful in stratifying bacterial co-infection risk in COVID-19 hospitalized patients, optimizing diagnostic testing and antibiotic use

Bioresorbable insertion aids for brain implantable flexible probes: a comparative study on silk fibroin, alginate, and disaccharides

Miniaturized, flexible, and biocompatible neural probes have the potential to circumvent the brain's foreign body response, but the problem of surgical implantation remains. Herein, a probe intended for implantation in the rat hippocampus is coated in four bioresorbable stiffeners to determine which is most effective in aiding insertion. The stiffeners (sucrose, maltose, silk fibroin, and alginate) are evaluated through mechanical, chemical, and dissolution tests. After coating with silk fibroin, the buckling force of the neural probe increases from 0.31 to 75.99 mN. This goes in accordance with subsequent successful insertion tests. Fourier transform infrared spectroscopy results demonstrate the increase in β-sheet content of silk fibroin samples after treatment (e.g., water annealing) and show relevant changes due to dehydration of the alginate hydrogel. Both qualitative and quantitative dissolution studies in artificial cerebrospinal fluid illustrate that alginate and silk fibroin outlasts the disaccharide stiffeners. In this work, a variety of multidisciplinary analyses are carried out to find the best bioresorbable stiffener for deep brain implantable devices with the highest buckling force, longest dissolution time, and the most tunable structure. For the first time, an alginate hydrogel is used as a stiffener to aid insertion, expanding its usefulness beyond neural tissue engineering