We target the planning of a 5G cellular network under 5G service and ElectroMagnetic Fields (EMFs) constraints. We initially model the problem with a mixed integer linear programming (MILP) formulation. The pursued objective is a weighed function of next-generation Node-B (gNB) installation costs and 5G service coverage level from a massive multiple input multiple output (MIMO) system. In addition, we precisely model restrictive EMF constraints and we integrate scaling parameters to estimate the power radiated by 5G gNBs. Since the considered planning problem is NP-Hard, and therefore very challenging to be solved even for small problem instances, we design an efficient heuristic, called PLATEA , to practically solve it. Results, obtained over a realistic scenario that includes EMF exposure from pre-5G technologies (e.g., 2G, 3G, 4G), prove that the cellular planning selected by PLATEA ensures 5G service and restrictive EMF constraints. However, we demonstrate that the results are strongly affected by: i) the relative weight between gNB installation costs and 5G service coverage level; ii) the scaling parameters to estimate the exposure generated by 5G gNBs; iii) the amount of exposure from pre-5G technologies; and iv) the adopted frequency reuse scheme.

While GPS has traditionally been the primary positioning technology, 3GPP has more recently begun to include positioning services as native, built-in features of future-generation cellular networks. With Release 16 of the 3GPP, finalized in 2021, a significant standardization effort has taken place for positioning in 5G networks, especially in terms of physical layer signals, measurements, schemes, and architecture to meet the requirements of a wide range of regulatory, commercial and industrial use cases. However, experimentally-driven research aiming to assess the real-world performance of 5G positioning is still lagging behind, root causes being i) the slow integration of positioning technologies in open-source 5G frameworks, ii) the complexity in setting up and properly configuring a 5G positioning testbed and iii) the cost of a multi-BS deployment. This paper sheds some light on all such aspects. After a brief overview of state of the art in 5G positioning and its support in open-source platforms based on software-defined radios, we provide advice on how to set-up positioning testbeds, and we demonstrate, via a set of real-world measurements, how to assess aspects such as reference signal configurations, localization algorithms, and network deployments, even with a cost-constrained limited-size testbed.

A very popular theory circulating among non-scientific communities claims that the massive deployment of Base Stations (BSs) over the territory, a.k.a. cellular network densification, always triggers an uncontrolled and exponential increase of human exposure to Radio Frequency “Pollution” (RFP). To face such concern in a way that can be understood by the layman, in this work we develop a very simple model to compute the RFP, based on a set of worst-case and conservative assumptions. We then provide closed-form expressions to evaluate the RFP variation in a pair of candidate 5G deployments, subject to different densification levels. Results, obtained over a wide set of representative 5G scenarios, dispel the myth: cellular network densification triggers an RFP decrease (up to three orders of magnitude) when the radiated power from the BS is adjusted to ensure a minimum sensitivity at the cell edge. Eventually, we analyze the conditions under which the RFP may increase when the network is densified (e.g., when the radiated power does not scale with the cell size), proving that the amount of RFP is always controlled. Finally, the results obtained by simulation confirm the outcomes of the RFP model.

Small Cells (SCs) mounted on top of Unmanned Aerial Vehicles (UAVs) can be used to boost the radio capacity in hotspot zones.However, UAV-SCs are subject to tight battery constraints, resulting in frequent recharges operated at the ground sites.To meet the UAV-SCs energy demanded to the ground sites, the operator leverages a set of Solar Panels (SPs) and grid connection.In this work, we demonstrate that both i) the level of throughput provided to a set of areas and ii) the amount of energy that is exchanged with the grid by the ground sites play a critical role in such UAV-aided cellular network.We then formulate the J-MATE model to jointly optimize the energy and throughput through revenue and cost components.In addition, we design the BBSR algorithm, which is able to retrieve a solution even for large problem instances.We evaluate J-MATE and BBSR over a realistic scenario composed of dozens of areas and multiple ground sites, showing that: i) both J-MATE and BBSR outperform previous approaches targeting either the throughput maximization or the energy minimization, and ii) the computation time and the memory occupation of BBSR are reduced up to five orders of magnitude compared to J-MATE.

Location based services are expected to play a major role in future generation cellular networks, starting from the incoming 5G systems. At the same time, localization technologies may be severely affected by attackers capable to deploy low cost fake base stations and use them to alter localization signals. In this paper, we concretely focus on two classes of threats: noise-like jammers, whose objective is to reduce the signal-to-noise ratio, and spoofing/meaconing attacks, whose objective is to inject false or erroneous information into the receiver. Then, we formulate the detection problems as binary hypothesis tests and solve them resorting to the generalized likelihood ratio test design procedure as well as the Latent Variable Models, which involves the expectation-maximization algorithm to estimate the unknown data distribution parameters. The proposed techniques can be applied to a large class of location data regardless the subsumed network architecture. The performance analysis is conducted over simulated data generated by using measurement models from the literature and highlights the effectiveness of the proposed approaches in detecting the aforementioned classes of attacks.

We target the problem of performing a large set of measurements over the territory to characterize the exposure from a 5G deployment. Since using a single Spectrum Analyzer (SA) is not practically feasible (due to the limited battery duration), in this work we adopt an integrated approach, based on the massive measurement of 5G metrics with a 5G smartphone, followed by a detailed analysis done with the SA and an ElectroMagnetic Field (EMF) meter in selected locations. Results, obtained over a real territory covered by 5G signal, reveal that 5G exposure is overall very limited for most of measurement locations, both in terms of field strength (up to 0.7 [V/m]) and as share w.r.t. other wireless technologies (typically lower than 15%). Moreover, our approach allows easily spotting measurement outliers, e.g., due to the exploitation of Dynamic Spectrum Sharing (DSS) techniques between 4G and 5G. In addition, the exposure metrics collected with the smartphone are overall a good proxy of the total exposure measured over the whole 5G channel. Moreover, the sight conditions and the distance from 5G base station play a great role in determining the level of exposure. Finally, a maximum of 130 [W] of power radiated by a 5G base station is estimated in the scenario under consideration.

Software Defined Wide Area Network (SD-WAN) was originally proposed as an alternative solution to redesign the architecture of the WAN. Like its technology precursor Software Defined Networking, SD-WAN was aiming at simplifying the management and operation of the networks (with a particular focus on WAN scenarios) by decoupling the networking hardware from its control programs and using software and open APIs to abstract the infrastructure and manage the connectivity and the services. The SD-WAN architecture leverages SDN principles to securely build interconnections between users and the applications hosted in the clouds or in remote branches, by leveraging any combination of transport services With this paper we shed some light on the SD-WAN scenario and describe an open-source implementation which can be taken as reference. We call this architecture EveryWAn.It has been designed with SDN and NFV principles in mind, and leverages Cloud best practices to deliver to the WAN customers and the MSP the same benefits and the agility of the Cloud service providers. Moreover, we strongly believe in the openness of the SDN/NFV paradigms which can ease the development of new services and can foster the innovation in the SD-WAN deployments.

This article presents a shared vision among stakeholders across the value chain on the use of radio positioning and sensing for road safety in the 5G ecosystem. The key enabling technologies and architectural functionalities are explored, focusing on the extremely stringent localization and communication requirements. A case study for joint radar and communication using experimental data showcases the potential of the new enablers that are paving the way toward enhanced road safety in beyond 5G scenarios.

A very popular theory circulating among non-scientific communities claims that the massive deployment of 5G base stations over the territory, a.k.a. 5G densification, always triggers an uncontrolled and exponential increase of human exposure to Radio Frequency Pollution (RFP). To face such concern in a way that can be understood by the layman, in this work we develop a very simple model to compute the RFP, based on a set of worst-case and conservative assumptions. We then provide closed-form expressions to evaluate the RFP variation in a pair of candidate 5G deployments, subject to different densification levels. Results, obtained over a wide set of representative 5G scenarios, dispel the myth: 5G densification triggers an RFP decrease when the radiated power from the 5G base stations is adjusted to ensure a minimum sensitivity at the cell edge. Eventually, we analyze the conditions under which the RFP may increase when the network is densified (e.g., when the radiated power does not scale with the cell size), proving that the amount of RFP is always controlled. Finally, the results obtained by simulation confirm the outcomes of the RFP model.

Information Centric Networking (ICN) is a new paradigm where the network provides users with named content, instead of communication channels between hosts. This document outlines some research directions for Information Centric Networking with respect to applying ICN approaches for coping with natural or human-generated, large-scale disasters. This document is a product of the Information-Centric Networking Research Group (ICNRG).

A common concern among the population is that installing new 5G Base Stations (BSs) over a given geographic region may result in an uncontrollable increase of Radio-Frequency “Pollution” (RFP). To face this dispute in a way that can be understood by the layman, we develop a very simple model, which evaluates the RFP at selected distances between the user and the 5G BS locations. We then obtain closed-form expressions to quantify the RFP increase/decrease when comparing a pair of alternative 5G deployments. Results show that a dense 5G deployment is beneficial to the users living in proximity to the 5G BSs, with an abrupt decrease of RFP (up to three orders of magnitude) compared to a sparse deployment. We also analyze scenarios where the user equipment minimum detectable signal threshold is increased, showing that in such cases a (slight) increase of RFP may be experienced.

We focus on the ElectroMagnetic Field (EMF) exposure safety for people living in the vicinity of cellular towers. To this aim, we analyze a large dataset of long-term EMF measurements collected over almost 20 years in more than 2000 measurement points spread over an Italian region. We evaluate the relationship between EMF exposure and the following factors: (i) distance from the closest installation(s), (ii) type of EMF sources in the vicinity, (iii) Base Station (BS) technology, and (iv) EMF regulation updates. Overall, the exposure levels from BSs in the vicinity are below the Italian EMF limits, thus ensuring safety for the population. Moreover, BSs represent the lowest exposure compared to Radio/TV repeaters and other EMF sources. However, the BS EMF exposure in proximity to users exhibits an increasing trend over the last years, which is likely due to the pervasive deployment of multiple technologies and to the EMF regulation updates. As a side consideration, if the EMF levels continue to increase with the current trends, the EMF exposure in proximity to BSs will saturate to the maximum EMF limit by the next 20 years at a distance of 30 meters from the closest BS.

The goal of this paper is to assess how different User Terminals react to IMSI-catching attacks, namely location privacy attacks aiming at gathering the user's International Mobile Subscriber Identity (IMSI). After having implemented two different attack techniques over two different Software-Defined-Radio (SDR) platforms (OpenAirInterface and srsLTE), we have tested these attacks over different versions of the mobile phone brands, for a total of 19 different radio modems tested. We show that while the majority of devices surrender almost immediately, iPhones seem to implement some cleverness that resembles proper countermeasures. We also bring about evidence that the two chosen SDR platforms implement different signaling procedures that differentiate their ability as IMSI-catchers. We finally analyse IMSI-catchers' behaviors against subscribers of different operators, showing that successfulness of the attack depends only on the chipset and the SDR tool. We believe that our analysis may be useful either to practitioners that need to experiment with mobile security, as well as engineers for improving the design of mobile modems.

We face the problem of designing a 5G network composed of Virtual Network Function (VNF)-based entities, called Reusable Functional Blocks (RFBs). RFBs provide a high level of flexibility and scalability, which are recognized as core functions for the deployment of the forthcoming 5G technology. Moreover, the RFBs can be run on different HardWare (HW) and SoftWare (SW) execution environments located in 5G nodes, in line with the current trend of network softwarization. After overviewing the considered RFB-based 5G network architecture, we formulate the problem of minimizing the total costs of a 5G network composed of RFBs and physical 5G nodes. Since the presented problem is NP-Hard, we derive two algorithms, called SFDA and 5G-PCDA, to tackle it. We then consider a set of scenarios located in the city of San Francisco, where the positions of the users and the set of candidate sites to host 5G nodes have been derived from the WeFi app. Our results clearly show the trade-offs that emerge between (i) the total costs incurred by the installation of the 5G equipment, (ii) the percentage of users that are served, and (iii) the minimum downlink traffic provided to the users.

The expected dramatic growth of connected things raises the issue of how to efficiently organize them, in order to monitor and manage functions and interactions. Information centric networking (ICN) is a communication paradigm that provides content-oriented functionality in the network and at the network level, including content routing, caching, multicast, mobility, data-centric security, and a flexible namespace. Thus, it is a viable solution for supporting Internet of Things (IoT) services without requiring any centralized entity. In this paper, we introduce the lightweight named object solution: a convenient way to represent physical IoT objects in a derived name space, exploiting ICN. We show that this abstraction can: 1) increase the programming simplicity; 2) offer extended functionality, such as augmentation and upgrading, to cope with the "software erosion," and 3) implement a common interaction logic involving mutual function invocation. We present some proof-of-concept implementations of the proposed abstraction dealing with challenging IoT test cases; we also carry out a performance evaluation in a simulated network scenario.

LoRaWAN (Long Range Wide Area Network) is an interesting network technology for building ultra low-power instances of the Internet of Things (IoT) and motivated a significant interest in the recent literature. The contribution of this paper is twofold. First, we devise a model to evaluate the performance of algorithms used for assigning the best "resource patterns" to transmit packets on the wireless interface of LoRa; to this end, we adopt a Spatial Point Process to model the distribution of nodes in the system and we apply such a model to derive, in a compact way, the performance of a Spreading Factor allocation mechanism proposed in the literature. A second contribution of the paper consists in the definition of a new metric to estimate the network performance and of a new protocol to dynamically improve the above assignment algorithm. Both the metric and the algorithm are based on a re-transmission mechanism.

The forthcoming 5G technology foresees the exploitation of solutions able to increase both the flexibility and the scalability of the network. In line with the current trend of softwarization, in this work we face the problem of designing a 5G network from the outcome of the Horizon 2020 project Super-fluidity. The core of the project is the definition of a 5G converged architecture based on virtual entities, called Reusable Functional Blocks (RFBs), which can be run on different HardWare (HW) and SoftWare (SW) execution environments. The exploitation of RFBs allows to achieve the required level of flexibility required by 5G. After optimally formulating the problem of minimizing the total installation costs of a SuperFluid network composed of RFBs and physical 5G nodes, we propose a new algorithm, called SFDA, to practically tackle the problem. Our results, obtained over a representative case study, show that SFDA is able to solve the problem in a reasonable amount of time, returning solutions very close to the optimum. In addition, we clearly show the trade offs that emerge between the need of providing a service level to users (in terms of downlink traffic or coverage) and the total costs incurred to install the elements of the network.

We target the problem of providing 5G network connectivity in rural zones by means of Base Stations (BSs) carried by Unmanned Aerial Vehicles (UAVs). Our goal is to schedule the UAVs missions to: i) limit the amount of energy consumed by each UAV, ii) ensure the coverage of selected zones over the territory, ii) decide where and when each UAV has to be recharged in a ground site, iii) deal with the amount of energy provided by Solar Panels (SPs) and batteries installed in each ground site. We then formulate the RURALPLAN optimization problem, a variant of the unsplittable multicommodity flow problem defined on a multiperiod graph. After detailing the objective function and the constraints, we solve RURALPLAN in a realistic scenario. Results show that RURALPLAN is able to outperform a solution ensuring coverage but not considering the energy management of the UAVs.

Nowadays, at least two billion people are experiencing a complete lack of cellular coverage. Since the lack of cellular connectivity is mostly experienced in rural zones, it is of mandatory importance to design solutions to manage cellular architectures tailored to such zones. To this aim, we consider a new cellular 5G architecture, where the Base Stations (BSs) are carried by Unmanned Aerial Vehicles (UAVs). Specifically, we focus on the problem of planning the missions of the UAV-based BSs over the territory, with the goal of minimizing the energy consumed for moving the UAVs. After introducing the considered framework, which is based on a multi-period graph defined over a set of places and a set of Time Slots (TSs), we derive a simple algorithm, called GAUP, to solve the considered problem in a reasonable amount of time. Our results, obtained over a simple - yet representative - scenario, reveals that GAUP is able to efficiently manage the energy for moving the UAVs, while guaranteeing relatively low computation times.

The installation of base station (BS) sites is regulated by a variety of laws at international, national, and local levels. While international regulations are already severe, the national and local laws applied in many countries and regions follow precautionary principles and enforce electromagnetic field (EMF) constraints that are even more restrictive. This legal environment results in substantial constraints affecting the planning of cellular networks, as requests for new BS site installation are easily denied by national or local authorities. In this paper, we consider the problem of cellular planning under restrictive EMF limits from the user equipment (UE) viewpoint. We focus on outdoor urban areas and first evaluate the impact of the current, non-optimal network planning at the UE side through a quantitative measurement-driven analysis of the quality of service (QoS) observed by users in heterogeneous, large-scale urban scenarios. We then perform a qualitative assessment of the perceived QoS and generated EMF levels at one UE transferring data from/to a BS based on its position with respect to the serving BS. Finally, we run a what-if analysis by comparing the existing planning with the one where new BS sites can be installed, thanks to a relaxation of the restrictive EMF constraints. Our results clearly show that a cellular planning driven by restrictive EMF constraints forces UE to experience large distances from the serving BS, frequent non-line-of-sight conditions, and poor received signal. In turn, this entails a very negative combination of high electric field activity (EFA) levels generated by the UE and low QoS perceived by the user. We show that, by relaxing the restrictive EMF constraints, the problem could be sensibly mitigated with a positive impact on the UE channel conditions and consequently on the perceived QoS and the UE EFA.