Fabrication of quencher-free liquid scintillator-based, high-activity 222^{222}Rn calibration sources for the Borexino detector

A reliable and consistently reproducible technique to fabricate $^{222}$Rn-loaded radioactive sources ($\sim$0.5-1 kBq just after fabrication) based on liquid scintillator (LS), with negligible amounts of LS quencher contaminants, was implemented. This work demonstrates the process that will be used during the Borexino detector's upcoming calibration campaign, with one or several $\sim$100 Bq such sources will be deployed at different positions in its fiducial volume, currently showing unprecedented levels of radiopurity. These sources need to fulfill stringent requirements of $^{222}$Rn activity, transparency to the radiations of interest and complete removability from the detector to ensure their impact on Borexino's radiopurity is negligible. Moreover, the need for a clean, undistorted spectral signal for the calibrations imposes a tight requirement to minimize quenching agents ("quenchers") to null or extremely low levels

Enhanced content update dissemination through D2D in 5G cellular networks

Opportunistic traffic offloading has been proposed to tackle overload problems in cellular networks. However, existing proposals only address device-to-device-based offloading techniques with deadline-based data propagation, and neglect content injection procedures. In contrast, we tackle the offloading issue from another perspective: the base station interference coordination problem during content injection. In particular, we focus on dissemination of contents, and aim at the minimization of the total transmission time spent by base stations to inject the contents into the network. We leverage the almost blank subframe technique to keep under control the intercell interference in such a process. We formulate an optimization problem, prove that it is NP-hard and NP-complete, and propose a near-optimal heuristic to solve it. Our algorithm substantially outperforms classical intercell interference approaches, as we evaluate through the simulation of LTE-A networks.This work has been initially supported by the FP7 CROWD Project under Grant 318115 and later on followed up by the H2020 5G NORMA Project under Grant 671584. The work of V. Mancuso was supported by the Ramon y Cajal Grant from the Spanish Ministry of Economy and Competitiveness under Grant RYC-2014-01335

A multi-traffic inter-cell interference coordination scheme in dense cellular networks

This paper proposes a novel semi-distributed and practical ICIC scheme based on the Almost Blank Sub-Frame (ABSF) approach specified by 3GPP. We define two mathematical programming problems for the cases of guaranteed and best-effort traffic, and use game theory to study the properties of the derived ICIC distributed schemes, which are compared in detail against unaffordable centralized schemes. Based on the analysis of the proposed models, we define Distributed Multi-traffic Scheduling (DMS), a unified distributed framework for adaptive interference-aware scheduling of base stations in future cellular networks, which accounts for both guaranteed and best-effort traffic. DMS follows a two-tier approach, consisting of local ABSF schedulers, which perform the resource distribution between the guaranteed and best effort traffic, and a light-weight local supervisor, which coordinates ABSF local decisions. As a result of such a two-tier design, DMS requires very light signaling to drive the local schedulers to globally efficient operating points. As shown by means of numerical results, DMS allows to: (i) maximize radio resources resue; (ii) provide requested quality for guaranteed traffic; (iii) minimize the time dedicated to guaranteed traffic to leave room for best-effort traffic; and (iv) maximize resource utilization efficiency for the best-effort traffic.The work of A. Banchs was supported by the H2020 5GMoNArch project (Grant Agreement No. 761445) and the 5GCity project of the Spanish Ministry of Economy and Competitiveness (TEC2016-76795-C6-3-R). The work of V. Mancuso has been supported by a Ramon y Cajal grant (ref: RYC-2014-16285) in part by the Spanish Ministry of Science, Innovation and Universities under grant TIN2017-88749-R and by the Madrid Regional Government through the TIGRE5-CM program (S2013/ICE-2919)

RL-NSB: Reinforcement Learning-based 5G Network Slice Broker

Network slicing is considered one of the main pillars of the upcoming 5G networks. Indeed, the ability to slice a mobile network and tailor each slice to the needs of the corresponding tenant is envisioned as a key enabler for the design of future networks. However, this novel paradigm opens up to new challenges, such as isolation between network slices, the allocation of resources across them, and the admission of resource requests by network slice tenants. In this paper, we address this problem by designing the following building blocks for supporting network slicing: i) traffic and user mobility analysis, ii) a learning and forecasting scheme per slice, iii) optimal admission control decisions based on spatial and traffic information, and iv) a reinforcement process to drive the system towards optimal states. In our framework, namely RL-NSB, infrastructure providers perform admission control considering the service level agreements (SLA) of the different tenants as well as their traffic usage and user distribution, and enhance the overall process by the means of learning and the reinforcement techniques that consider heterogeneous mobility and traffic models among diverse slices. Our results show that by relying on appropriately tuned forecasting schemes, our approach provides very substantial potential gains in terms of system utilization while meeting the tenants' SLAs.The work of V. Sciancalepore and X. Costa-Perez was supported by the European Union H-2020 Project 5G-TRANSFORMER under Grant Agreement 761536. The work of A. Banchs was supported in part by the 5GCity project of the Spanish Ministry of Economy and Competitiveness (TEC2016-76795-C6-3-R

Study of 222−220Rn Measurement Systems Based on Electrostatic Collection by Using Geant4+COMSOL Simulation

Using Monte Carlo (with Geant4) and COMSOL simulations, the authors have defined a useful tool to reproduce the alpha spectroscopy of 222Rn, 220Rn and their ionized daughters by measurement systems based on electrostatic collection on a silicon detector, inside a metallic chamber. Several applications have been performed: (i) simulating commercial devices worldwide used, and comparing them with experimental theoretical results; (ii) studying of realization of new measurement systems through investigation of the detection efficiency versus different chamber geometries. New considerations and steps forward have been drawn. The present work is a novelty in the literature concerning this research framework

Offloading cellular traffic through opportunistic communications: analysis and optimization

Offloading traffic through opportunistic communications has been recently proposed as a way to relieve the current overload of cellular networks. Opportunistic communication can occur when mobile device users are (temporarily) in each other's proximity, such that the devices can establish a local peer-to-peer connection (e.g., via WLAN or Bluetooth). Since opportunistic communication is based on the spontaneous mobility of the participants, it is inherently unreliable. This poses a serious challenge to the design of any cellular offloading solutions, that must meet the applications' requirements. In this paper, we address this challenge from an optimization analysis perspective, in contrast to the existing heuristic solutions. We first model the dissemination of content (injected through the cellular interface) in an opportunistic network with heterogeneous node mobility. Then, based on this model, we derive the optimal content injection strategy, which minimizes the load of the cellular network while meeting the applications' constraints. Finally, we propose an adaptive algorithm based on control theory that implements this optimal strategy without requiring any data on the mobility patterns or the mobile nodes' contact rates. The proposed approach is extensively evaluated with both a heterogeneous mobility model as well as real-world contact traces, showing that it substantially outperforms previous approaches proposed in the literature.This work has been sponsored by the HyCloud project, supported by Microsoft Innovation Cluster for Embedded Software (ICES), and by the EU H2020-ICT-2014-2 Flex5Gware project, no. 671563

Resource allocation for network slicing in mobile networks

This paper provides a survey of resource allocation for network slicing. We focus on two classes of existing solutions: (i) reservation-based approaches, which allocate resources on a reservation basis, and (ii) share-based approaches, which allocate resources based on static overall shares associated to individual slices. We identify the requirements that a slice-based resource allocation mechanism should satisfy, and evaluate the performance of both approaches against these requirements. Our analysis reveals that reservation-based approaches provide a better level of isolation as well as stricter guarantees, by enabling tenants to explicitly reserve resources, but one must pay a price in terms of efficiency unless reservations can be updated very dynamically; in particular, efficiency falls below 50\% when reservations are performed over long timescales. We provide further comparisons in terms of customizability, complexity, privacy and cost predictability, and discuss which approach might be more suitable depending on the network slices' characteristics. We also describe the additional mechanisms required to implement the desired resource allocations while meeting the latency and reliability requirements of the different slice types, and outline some issues for future work.The work of Albert Banchs was supported in part by the H2020 5G-TOURS European project under Grant 856950, and in part by the Spanish State Research Agency (TRUE5G project) under Grant PID2019-108713RB-C52/AEI/10.13039/501100011033. The work of Gustavo de Veciana was supported by NSF Grant CNS-1910112

A machine learning approach to 5G infrastructure market optimization

It is now commonly agreed that future 5G Networks will build upon the network slicing concept. The ability to provide virtual, logically independent "slices" of the network will also have an impact on the models that will sustain the business ecosystem. Network slicing will open the door to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, how to correctly handle resource allocation among tenants and how to maximize the monetization of the infrastructure become fundamental problems that need to be solved. In this paper, we address this issue by designing a network slice admission control algorithm that (i) autonomously learns the best acceptance policy while (ii) it ensures that the service guarantees provided to tenants are always satisfied. The contributions of this paper include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modeled as a Semi-Markov Decision Process) and the optimization of the infrastructure providers revenue, and (iii) the design of a machine learning algorithm that can be deployed in practical settings and achieves close to optimal performance.The work of University Carlos III of Madrid was supported by the H2020 5G-MoNArch project (Grant Agreement No. 761445) and the 5GCity project of the Spanish Ministry of Economy and Competitiveness (TEC2016-76795-C6-3-R). The work of NEC Laboratories Europe was supported by the 5G-Transformer project (Grant Agreement No. 761536)

Validation of the Electromagnetic Physical Processes with Software SPENVIS

The Space Radiation represents a serious risk for astronauts during space missions. The risk related to the space radiation exposure could involve acute and/or late effects. The Solar Cosmic Radiation that consists of protons (≈98%) with a very wide spectrum in energy (up to several GeV), is the major source of exposure for the crew. In this paper we present the results of the validation of the electromagnetic physical processes with the aim to contribute to the study of radiation protection for astronauts, in particular against the radiation due to the Solar Particle Events (SPE). The simulation was performed using MULASSIS, a module to the software SPENVIS, with protons as source, in the energy range from 800 MeV to 1.2 GeV, on a slab of aluminum of mass thickness of 20 g/cm2. The results obtained by the simulation were compared with PSTAR database of the NIST. Finally, a comparison between SPENVIS and Geant4-9.6p2 was performed

Analysis of the performance in the linear field of Equivalent-Frame Models for regular and irregular masonry walls

The Equivalent-Frame Method (EFM), a simplified procedure for structural modelling of masonry constructions, is having a great success for the good balance that it allows between the accuracy of the geometrical description and the simplicity of the mechanical calibration. Despite the widespread use of EFM in scientific and professional field, some uncertainties affect its application to the specific problem of the existing unreinforced masonry (URM) buildings. For these structures, in fact, irregular geometries, the presence of deformable diaphragms and the interaction with other structures in aggregate configurations represent hard-to-model features that limit the accuracy of EFM. The paper presents a comparative study in the linear field between EFM and the more accurate Finite Element Method (FEM), assumed as reference. The comparative analysis involves a wide set of geometrical schemes, characterized by both regular and irregular configurations, and it is aimed at providing a measure of the EFM modelling accuracy as a function of the geometry of the wall. Non-dimensional parameters allow exploring the limits of applicability of EFM for both regular and irregular walls. Based on the parametric analyses, some recommendations are given for improving the effectiveness of the method and preserving the simplicity of application that makes EFM models so popular and widely used.Peer ReviewedPostprint (author's final draft