Probabilistic Graphical Models: an Application in Synchronization and Localization

Die Lokalisierung von mobilen Nutzern (MU) in sehr dichten Netzen erfordert häufig die Synchronisierung der Access Points (APs) untereinander. Erstens konzentriert sich diese Arbeit auf die Lösung des Problems der Zeitsynchronisation in 5G-Netzwerken, indem ein hybrider Bayesischer Ansatz für die Schätzung des Taktversatzes und des Versatzes verwendet wird. Wir untersuchen und demonstrieren den beträchtlichen Nutzen der Belief Propagation (BP), die auf factor graphs läuft, um eine präzise netzwerkweite Synchronisation zu erreichen. Darüber hinaus nutzen wir die Vorteile der Bayesischen Rekursiven Filterung (BRF), um den Zeitstempel-Fehler bei der paarweisen Synchronisierung zu verringern. Schließlich zeigen wir die Vorzüge der hybriden Synchronisation auf, indem wir ein großes Netzwerk in gemeinsame und lokale Synchronisationsdomänen unterteilen und so den am besten geeigneten Synchronisationsalgorithmus (BP- oder BRF-basiert) auf jede Domäne anwenden können. Zweitens schlagen wir einen Deep Neural Network (DNN)-gestützten Particle Filter-basierten (DePF)-Ansatz vor, um das gemeinsame MU-Sync&loc-Problem zu lösen. Insbesondere setzt DePF einen asymmetrischen Zeitstempel-Austauschmechanismus zwischen den MUs und den APs ein, der Informationen über den Taktversatz, die Zeitverschiebung der MUs, und die AP-MU Abstand liefert. Zur Schätzung des Ankunftswinkels des empfangenen Synchronisierungspakets nutzt DePF den multiple signal classification Algorithmus, der durch die Channel Impulse Response (CIR) der Synchronisierungspakete gespeist wird. Die CIR wird auch genutzt, um den Verbindungszustand zu bestimmen, d. h. Line-of-Sight (LoS) oder Non-LoS (NLoS). Schließlich nutzt DePF particle Gaussian mixtures, die eine hybride partikelbasierte und parametrische BRF-Fusion der vorgenannten Informationen ermöglichen und die Position und die Taktparameter der MUs gemeinsam schätzen.Mobile User (MU) localization in ultra dense networks often requires, on one hand, the Access Points (APs) to be synchronized among each other, and, on the other hand, the MU-AP synchronization. In this work, we firstly address the former, which eventually provides a basis for the latter, i.e., for the joint MU synchronization and localization (sync&loc). In particular, firstly, this work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Specifically, we investigate and demonstrate the substantial benefit of Belief Propagation (BP) running on Factor Graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian Recursive Filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into common and local synchronization domains, thereby being able to apply the most suitable synchronization algorithm (BP- or BRF-based) on each domain. Secondly, we propose a Deep Neural Network (DNN)-assisted Particle Filter-based (DePF) approach to address the MU joint sync&loc problem. In particular, DePF deploys an asymmetric time-stamp exchange mechanism between the MUs and the APs, which provides information about the MUs' clock offset, skew, and AP-MU distance. In addition, to estimate the Angle of Arrival (AoA) of the received synchronization packet, DePF draws on the Multiple Signal Classification (MUSIC) algorithm that is fed by the Channel Impulse Response (CIR) experienced by the sync packets. The CIR is also leveraged on to determine the link condition, i.e. Line-of-Sight (LoS) or Non-LoS (NLoS). Finally DePF capitalizes on particle Gaussian mixtures which allow for a hybrid particle-based and parametric BRF fusion of the aforementioned pieces of information and jointly estimate the position and clock parameters of the MUs

Evaluation of Frequency and Type of Severe Anemia in Patients Referred to the Baqiyatallah Hospital in Tehran in Six Months; A Descriptive Cross-Sectional Study

Purpose: To investigate the frequency and types of severe unknown anemia in patients referred to the Baqiyatallah Hospital (Tehran) for six months. Methods: In this descriptive cross-sectional study, the patients with severe unknown anemia referred to the Baqiyatallah Hospital (Tehran, Iran) were selected over six months. Following consideration of inclusion and exclusion criteria, 230 patients with severe anemia (hemoglobin (Hb) > 8gr/dl) were included. Complete medical history was obtained from the patients and additional biochemical blood analyses were applied to determine the frequency and type of anemia. SPSS (v.19) software was used to analyze the findings and the significance level was defined as a p-value <0.05. Results: In chronic disease anemia (47.5%), gastrointestinal bleeding-associated anemia (29%), bleeding malignancies anemia (21.5%), and aplastic anemia (2%). There were significant differences (p<0.05) in the frequency of different types of normocytic anemia. The highest frequency was detected in folate deficiency anemia (46%), hypothyroidism anemia (34%), and B12 deficiency anemia (20%), respectively. The hemolytic anemia represented a significant difference (p<0.05) in comparison with sickle cell anemia (95%). Also, sickle cell anemia showed a significant difference (p<0.05) between thalacemia-associated anemia (95%) and malignancy-related anemia (95%) Conclusion: Respectively, the highest frequency of anemia in patients was found in chronic diseases and gastrointestinal bleeding. It is suggested that more attention should be paid to the type of anemia of patients referred to the urgency of hospitals

Comparison between molecular methods (PCR vs LAMP) to detect Candida albicans in bronchoalveolar lavage samples of suspected tuberculosis patients

With the increase of patients suffering from immune deficiency infections also increased pulmonary fungi even in people with defective immune system can cause fatal and lethal candidiasis. The timely diagnosis of pulmonary candidiasis is one of the problems that has been detected. Polymerase chain reaction (PCR) test and Loop mediated isothermal amplification (LAMP) method optimized on the basis of α INT1 gene and then sensitivity and specificity were evaluated. LAMP is a novel nucleic acid amplification technique with high specificity and sensitivity which has been done under isothermal condition. Samples were the bronchoalveolar lavage suspected of tuberculosis (TB) reviews for TB disease negative have been reported. DNA extraction carried out by standard phenol/chloroform method on samples and PCR test and LAMP was done. PCR and LAMP testing was performed on samples and products of 441 bp were amplified and observed with agarose gel electrophoresis. At the end of the LAMP reaction, SYBR Green was used for identifying negative and positive results. Among the 60 quantities sera, only 7 cases were PCR positive but 8 cases were LAMP positive. In comparison, between LAMP and PCR, the LAMP technique in spite of its simplicity, high sensitivity and specificity, could be an appropriate replacement for PCR

5G-CLARITY: 5G-Advanced Private Networks Integrating 5GNR, WiFi, and LiFi

The future of the manufacturing industry highly depends on digital systems that transform existing production and monitoring systems into autonomous systems fulfilling stringent requirements in terms of availability, reliability, security, low latency, and positioning with high accuracy. In order to meet such requirements, private 5G networks are considered as a key enabling technology. In this article, we introduce the 5G-CLARITY system that integrates 5GNR, WiFi, and LiFi access networks, and develops novel management enablers to operate 5G-Advanced private networks. We describe three core features of 5G-CLARITY, including a multi-connectivity framework, a high-precision positioning server, and a management system to orchestrate private network slices. These features are evaluated by means of packet-level simulations and an experimental testbed demonstrating the ability of 5G-CLARITY to police access network traffic, to achieve centimeter-level positioning accuracy, and to provision private network slices in less than one minuteThis work is supported by the European Commission’s Horizon 2020 research and innovation program under grant agreement No 871428, 5G-CLARITY project

Towards joint communication and sensing (Chapter 4)

Localization of user equipment (UE) in mobile communication networks has been supported from the early stages of 3rd generation partnership project (3GPP). With 5th Generation (5G) and its target use cases, localization is increasingly gaining importance. Integrated sensing and localization in 6th Generation (6G) networks promise the introduction of more efficient networks and compelling applications to be developed

Synchronization in 5G networks: a hybrid Bayesian approach toward clock offset/skew estimation and its impact on localization

Clock synchronization has always been a major challenge when designing wireless networks. This work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Furthermore, we provide an in-depth analysis of the impact of the proposed approach on a synchronization-sensitive service, i.e., localization. Specifically, we expose the substantial benefit of belief propagation (BP) running on factor graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian recursive filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into local synchronization domains and applying the most suitable synchronization algorithm (BP- or BRF-based) on each domain. The performance of the hybrid approach is then evaluated in terms of the root mean square errors (RMSEs) of the clock offset, clock skew, and the position estimation. According to the simulations, in spite of the simplifications in the hybrid approach, RMSEs of clock offset, clock skew, and position estimation remain below 10 ns, 1 ppm, and 1.5 m, respectively

5G-CLARITY Deliverable D3.2 Design Refinements and Initial Evaluation of the Coexistence, Multi-Connectivity, Resource Management and Positioning Frameworks

This document, 5G-CLARITY D3.2, aims to provide evaluation results and refinements on the initially designed 5G-CLARITY user and control plane architecture that is introduced in 5G-CLARITY D3.1 [1]. This document is also aligned with the "network function and application stratum" that covers not only user- and controlplane but also application plane functionality as presented in 5G-CLARITY D2.2 [2]. In essence, 5G-CLARITY D3.2 provides the performance evaluations and refinements for: Multi-WAT aggregation: Including 5GNR CU/DU/RU integration, integration of Wi-Fi and LiFi networks as a single non-3GPP network, integration of 3GPP and non-3GPP wireless access technologies (WATs) and assignment of traffic flows via MPTCP; 5G-CLARITY eAT3S framework: Including operational flows, initial enhanced access traffic steering, switching and splitting (enhanced AT3S / eAT3S) algorithm design and control plane aspects of the custom MPTCP scheduler; Scheduling and resource management: Including Wi-Fi and LiFi airtime-based schedulers and utilitybased scheduler to manage different service types; Positioning: Including WAT-specific positioning scheme and its performance evaluations, as well as the fusion approach; Integrated 5G/Wi-Fi/LiFi network performance evaluation: Including possible access point (AP)/gNB deployment options, achievable communication bandwidths, technology-specific areacapacity achievements and integrated network area-capacity performance. Details for the 5G-CLARITY multi-connectivity framework evaluation are presented in Section 2. The 5GCLARITY multi-connectivity design includes, i) the multi-WAT aggregation, integrating 3GPP (5GNR) and non3GPP (Wi-Fi and LiFi) access networks, and ii) an enhancement on the AT3S scheme to improve the (multiaccess based) multi-connectivity functionalities. The details of design and validation of these features for the 5G-CLARITY user- and control-plane are provided. Section 3 delivers discussions on AP level and service level (traffic routing) resource scheduling techniques. Primarily, the corresponding telemetry and performance measurements are used to route the traffic across 3GPP/non-3GPP networks in near real-time (near-RT) using 5G-CLARITY eAT3S introduced to ensure qualityof-service (QoS), and as a following step, the AP level resource scheduling is performed by the gNB and/or Wi-Fi/LiFi AP. In this respect, a Linux-kernel based airtime management evaluation framework is discussed which can be used to segregate multi-WAT resources for a given 5G-CLARITY slice. Due to LiFi’s different channel and link reliability characteristics, the airtime scheduling for the LiFi technology is specifically discussed and slicing the attocellular network resources is researched. Section 4 is focused on 5G-CLARITY multi-WAT positioning solution. The associated technologies are 60 GHz mWave, sub-6 GHz, LiFi and Optical Camera Communications (OCC) based positioning. A localisation server obtains the position information from these WATs, and provides the position estimate, by fusing all the relevant data, to the entities requiring position services. Details of the overall architecture, each technology ranging/positioning scheme, and the fusion approach are provided. The simulation architecture to evaluate the integration and performance of 5G-CLARITY multi-WAT scheme, including the corresponding user- and control-plane functionalities are presented in Section 5. Results for a dense deployment of multi-WAT AP/gNB in contrast to the generic scenario, using both conservative (based on the available technologies) and opportunistic (assuming greedy usage of available bandwidth), are presented. The achievable system area capacity in each scenario is discussed and the limiting factors are introduced. Overall, this document, 5G-CLARITY D3.2, presents the achievable KPIs of the main components of the 5GCLARITY integrated 5G/Wi-Fi/LiFi network user- and control-plane architecture. The integration of these components and the evaluation of the overall 5G-CLARITY user- and control-plane will be reported in 5GCLARITY D3.3