CLIMATE, MDT AND MACHINE LEARNING THE

CLIMA VARIABLES IN THE CITY

Davide Micheli, Giuliano Muratore, Aldo Vannelli

The privileged view offered by the MDT mobile radio networks measurements represents an added value also for the study of the environmental impacts and of the related effects on social behavior. This research focuses on the correlation between rainfall weather events and variations both in the distribution of people in the territory and in the radio measurements performed by mobile terminals.The research has been carried out analyzing the MDT 2019 measurements of the region Emilia Romagna in 2019 and of the region Veneto in 2018, by comparing climatically different days and moments. This document describes a way to measure both types of climate-induced variations, the behavioral variation of users, through the measurement of positional entropy, and the variation of physical parameters, through the measurement of the RSRP (4G LTE Reference Signal Received Power). The document introduces also the opportunity, offered by the MDT innovation, that these types of analysis provide both for the study of the effects of climate changes in our cities and for the evolution of the mobile operators' business.

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Introduction

The 3GPP MDT (Minimization of Drive Test) standard [1-4] offers the possibility to collect radio meas-urements that the mobile terminal performs both when it is using a connection (Connected Mode) and when it is in waiting state (Logged Mode), all linked to the GPS coor-dinates, if available, of the position in which the single measurement takes place.The distribution across the terri-tory of mobile phones and the high number of the anonymized MDT measurements allows to build a database with a sufficient statisti-cal validity, which forms a relevant aspect for the study of the influence of atmospheric rainfall on the traf-fic distribution and on the signal strength level received from mobile terminals. Typically, the effect of rainfall on mobile communications is considered negligible, since the attenuation of rain it is very low for the usual frequencies between 800 MHz and 2.7 GHz, as reported in the ITU recommendations [5-8]. How-ever, compared to point-to-point connections, the mobile telephony, especially within cities, is charac-terized by the presence of multiple paths of radio signals [9].This affects the propagation of electromagnetic waves and, when the environment becomes wet, the characteristics of the dielectric sur-face of the soil, walls, roof buildings, foliage, cars and in general of all surfaces are altered. Other authors

have made considerations on the effect of rain. For example, Rogers et al. [10], highlight how the dielec-tric properties of the trees vary from season to season also depending on the salinity of the water further to the atmospheric precipitations. Li et al. [11] show how the equiva-lent electrical permittivity of rain depends on both the working fre-quency and the rainfall rate.S. Helhel et al. [12] report a study with measurements at 900 MHz and 1800 MHz showing how the sur-rounding wet environment formed by soil and trees reduces globally the value of the radio signal re-ceived by a few dB.The novelty of the research present-ed in this article is the use of a large amount of measurements generat-ed by mobile terminals. The mobile terminals are in fact instruments of measurement of the electromag-netic field and the MDT technology makes available a high number of geolocalized measurements, ena-bling the application of statistical analysis models to isolate the single hypothesized effects and identify the searched feedback, in real traf-fic scenarios.The article shows that the weather conditions can already affect the carrier frequencies currently used in 2G, 3G and 4G mobile networks (800-1800-2600 MHz), but some of the effects highlighted in this study will be further observable on 5G networks, that is, when higher car-rier frequencies (millimeter waves) will be used, frequencies more in-

fluenced by the presence of water. Therefore, the evolution towards 5G cannot ignore the development of SON (Self-Organizing Network) solutions, to make the infrastruc-tures able to continuously measure, and therefore dynamically adapt to, the variations due to meteorologi-cal events, to maintain the quality of the radio network at the desired level.The article is divided into four main sections. In the first section we ana-lyze weather data of the Emilia Ro-magna (Bologna), in relation to the issue of positional entropy. In the second section we analyze weather data of the region Veneto (Padua) in relation to the topic [9] of the re-ceived signal level. The third section discusses the potential that MDT-based studies also project for ana-lyzes on the life and evolution of the city. The MDT data covered in this article have been collected with the GeoSynthesis system developed by Nokia.The fourth section reflects on how the technical evolution of the net-works and the analysis tools avail-able today end up influencing the role itself of the mobile operators.

Emilia Weather and Positional Entropy

The month of May 2019 was a cli-matically exceptional period for Italy, with several episodes of bad

weather all over the peninsula, of-ten during the weekend. And it is precisely a weekend that is exam-ined here, in particular Sunday 12th May 2019, with very intense weath-er phenomena that affected the region Emilia Romagna, as can be seen also from the frame of Figure 1 (processing of a radar image of the Civil Protection), in a meteorologi-cal context that has presented rapid succession of rainy moments and pauses all over this region.

1Frame taken from radar images published by the Civil Protection. Shortly before 18:00 (16:50 UTC + 2H, to obtain the Italian time) a strong thunderstorm core (the red spot, highlighted by the arrow) was formed roughly at the intersection across the provinces of Bologna, Ferrara and Ravenna. That strong stormy core then quickly moved towards the west, before losing intensity.

In the same days a measurement campaign was also activeMDT 4G of the cluster (set of 4G LTE Cells) of Bologna, for which it was possible to study the effects of that stormy core on the distribution of telephone traffic in the area. The importance of the study of these variations depends on the fact that the radioelectric coverage of a ter-ritory is only ideally considered uni-form in all positions, but in the real world the radioelectric signal has

specific emission points (the anten-nas) and is also subjected to attenu-ations everywhere, reflections and refractions that alter the signal level in the single points of a territory. The quality of the services therefore also varies depending on where the ser-vices are used, and, in turn, it can be influenced by the environment and by its variations.It is understandable that in situa-tions of hot and stable weather a public space can be occupied in a

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different way compared to a rainy occasion, especially if it is very in-tense, during which the occupation of individual places more sheltered from the weather can suddenly be-come a position used more than other ones.As the possible distributions of posi-tions along a territory are theoreti-cally many, it becomes relevant to identify a mode of measurement of the distributions which is objective, rapidly obtainable from the radio data and still indicative of the dif-ferent (overall) scenarios of the ser-vices’ usage.It meets this summary need for complex phenomena the concept of entropy, a concept that emerged in physics to summarize the state of greater or lesser order of a system composed of many components, that in our case will not be the phys-ical particles but the swarm of ter-minals that access and use mobile radio services.We will therefore talk about posi-tional entropy, a simple numerical representation of how long the us-ers of the mobile radio services tend

to be "chaotically dispersed" (in all possible positions) or, on the con-trary, tend to be "neatly collected" around some specific locations.Moving to an illustrative example of the Emilia area in question, a rectan-gle (with a diagonal of 5 km) of the territory surrounding the Meraville shopping center in Bologna (Viale Tito Carnacini) is analyzed. In fact, this type of area represents a place that is sometimes enough frequent-ed (during opening hours), in order to be able to have a statistically sig-nificant measurements basis, but also a very accessible place with a few positional constraints, as it is a shopping center typically equipped with large parking lots and various access and runoff roads.The rectangle of the territory above was therefore ideally divided into 48 identical tiles, each of which can be associated to the number of radio measurements generated therein, within a specific time interval (as e.g. 5 minutes).Thus the distribution of radio meas-urements in the area can be trans-formed (see Figure 2) into a se-

quence of "pictures" that represent the various tiles, each more or less populated, in a specific time.The subdivision (rasterization) of presences within the tiles can be fi-nally converted into a vector (a list of numbers), where each position of the vector represents one of the various tiles, and in each tile (posi-tion of the vector) shows the count of radio measurements in that spe-cific tile (at that time).In this example, only the radio measurements that can be associ-ated with low-speed movements (less than 5 km/h) have been count-ed, with the objective to isolate pedestrian-type movements (which

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2Example of rasterization of measurements in the area of the Meraville Shopping Center (Bologna). Each box represents a time interval of 5 minutes, and the different colors the density of measurements in a tile (the darker color indicates a greater density, the lighter one indicates a lower density).

are free to occupy any portion of territory) than road ones (more con-strained by the roads and to the forced directions).It is then possible to measure (us-ing the tools of Information Theo-ry) exactly how much information each vector (which represents the tiles within a single time interval) contains, for example, how many informational bits would be needed to describe its specific composition. Clearly the case of a single popu-lated tile would be a situation that would intuitively require fewer bits, to be described, than in the case of many populated tiles, and all popu-lated differently between them. In terms of entropy we would say that in the first case the system is quite orderly (low entropy), while in the second case we would affirm the exact opposite, that is that the system is quite disjointed (high en-

tropy). In Figure 3 an example of dis-tribution of MDT samples in the area is reported.We can therefore compare two dif-ferent days, i.e. Saturday 11th May, which did not present particular meteorological criticalities, with the following day, Sunday 12th May, which towards the late afternoon showed a rapid increase in rainfall.What is expected is that, when mo-ments of intense rainfall begin, the area hit by those phenomena tends to decrease its positional entropy, because the positions of use of the services will tend to decrease as many more users will tend to group

themselves in some specific areas (the more sheltered ones) and few users will remain scattered in the positions that before (when there was no rain) were accessible with-out problems. Figure 4 confirms this hypothesis showing that when the period of intense rainfall begins (af-ter 18:00) the trend of the measured entropy tends to be reduced, while in the same period of the previous day (less rainy) this phenomenon does not occur.Generalizing the single example de-scribed here we can guess the po-tential deriving from the use of MDT as a tool to monitor places, times and situations in urban contexts, leveraging the potential that the concept of positional entropy makes available to us.Numerous studies [15-18] based on the analysis of a considerable num-ber of space-time traces generated 3

Graphic representation of an example of geographical distribution of samples in the area under investigation

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by mobile devices have outlined the possibility of identifying different levels of "order" (e.g. spatial order, social order, spontaneous self-or-ganized order), in the apparently casual distribution of citizens in the urban area.

4Variations of Entropy (positional) in the vicinity of the Shopping Center Meraville (Bologna) on May 11th (less rainy weather, in the area) and May 12th (rainy afternoon phenomena, sharp from late afternoon as evidenced by the rain symbol). To allow the comparison between the two days in the graph, the mean value ("Mean") of entropy is reported, referring to the two compared days.

Veneto Weather and Radio Signal

If the study of meteorological phe-nomena through MDT brings out dif-ferent behaviors for the use of mobile radio services, the same meteorolog-

ical phenomena can also modify the characteristics of the mobile radio service. The signal strength received from an antenna is one of the main parameters that our phones con-stantly monitor and communicate to the cellular network infrastructure so that, from time to time, we can use the antenna (the Cell) that can best serve, in that moment, in that posi-tion, in that situation.While in the case of positional en-tropy the cause (the weather) and

5Relationship between rainfall intensity and the specific attenuation at different frequencies [13]. Please note that all the main frequency bands of networks in 2G, 3G and 4G technology, falling within 4 GHz, present low specific attenuations (dB / km) even for very intense rainfall intensity (mm/h).

the effect (the change in the use scenario) are directly correlated, in the case of the variations of the ra-dio characteristics this happens in a particularly complex way, due to the multiple causes that contribute to the received signal level (e.g. the fact that people hold the phone in our hands or that we put it in a pocket or bag).The mobile radio frequencies used in the 2G, 3G and 4G networks have physical characteristics that make them very robust from a time point of view (see Figure 5), however, the signal level received from mobile phones also depends on reflections (e.g. from soil), refractions (e.g. from

buildings) and absorptions (e.g. walls or trees), as well as how direct sig-nals and reflected signals are recom-bined (interference phenomenon).The climate therefore, modifying the entire territory hit by humidity and rain, affects the mobile radio sce-nario by introducing a further level of complexity (see Figure 6).To better understand this complex-ity, Figure 6 shows the result of the simulation of electromagnetic prop-agation through a 5 mm thick flat glass in the absence and in the pres-ence of a thin layer of 0.3 mm thick overlapped water.This scenario tries to represent a car glass or a window in a simplified

way in the event of heavy rain on the glass.The simulations were carried out considering the dielectric character-istics of the glass indicated by the ITU recommendations (ITU-R P.2040-1) [19] and water [20], considering a model of reflection and transmis-sion of a plain wave through a mul-tilayer structure as shown in Figure 7. The mathematical model of elec-tromagnetic propagation through a structure of multilayer materials was implemented in Matlab for the cal-culation of the reflection coefficients (RC) in dB, of the transmission coef-ficient (TC) in dB and of the factor of loss (LF) in percentage [21].From the graphs of Figure 6, it is ob-served that the presence of water on the surfaces favors the reflec-tion of the incident electromagnetic

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CLASSIFICATION OF MOBILITY BY SURVIVAL MODELS AND ENTROPY OF INFORMATION

The development of ICT technologies has had a great impact in the study of the dynamic and statistical prop-erties of mobility in modern cities thanks to the possi-bility of collecting large georeferenced databases that contain information on individual mobility.The main consequence of this development was the change of point of view in the analysis of phenomena related to mobility. Before the scientific community used a Eulerian point of view in which the study of mo-bility was carried out through observations located in

single roads and averaged over time to measure the av-erage flow and density of vehicles and deduce a funda-mental diagram. Now ICT-based databases provide in-formation on the trajectories carried out by a sample of

Armando Bazzaniarmando.bazzani@unibo.it

individuals using different means of transport and it is necessary to develop analysis techniques that adapt to a Lagrangian viewpoint for the study of mobility. In the case of urban mobility, it is also necessary to develop statistical observables to understand when we are in the presence of phenomena that reflect a macroscopic change in the state of mobility, or a phase transition in the physical sense of the term.This approach opens new interesting perspectives for understanding traffic problems and for developing new governance policies that allow an improvement in the quality of life in cities. Let's take as an example the problem of measuring the degree of congestion of a city: the observation that in a given street, or cross-roads, a queue has been generated could be used as a measure of the degree of congestion of the urban network only if we assume we know the mobility de-mand of different classes of citizens and the existence of a Wardrop balance for the state of mobility (ie each individual behaves in a rational way, achieving optimal mobility with respect to his knowledge of the behavior

of other users). Empirical evidence has shown that it is extremely difficult to justify such assumptions, which can also be wrong in many cases. Taking the Lagran-gian point of view the question becomes: what is the effect of the degree of congestion of a city on the dy-namic trajectories achieved by individuals and on the behavior of individuals themselves. Currently we are not able to give a satisfactory answer to this question if not that the traveling speed in the urban road net-work decreases as the traffic load in the network itself increases, in a non-linear way. The fundamental point is the development of new models that allow an interpretation of statistical laws from a Lagrangian point of view to measure quanti-ties in relation to the behavior of individuals. This prob-lem has had recent contributions from the Physics of Complex Systems based on survival models and on the concept of information entropy (Lempel-Ziv entropy). The survival models have been applied to understand the distribution of mobility times related to different means of transport. In a mechanical statistical ap-

Continua

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Segue

proach to the analysis of individual mobility, time can be considered an amount corresponding to energy [1]. Let P (t) be the probability that an observed path has a duration greater than a given time t, we propose to introduce the following model to classify the empirical distribution of travel durations [2]:

where the function τ(t) defines the so-called hazard function. The logistic form of this function is typical of decision models as it represents mathematically a threshold effect. This model depends on three pa-rameters that take on a precise meaning and allow to classify the observed mobility: tc is a characteristic time that measures a duration considered convenient for the chosen means of transport, measures the typi-cal temporal distance from a goal that α-1 measures the typical temporal distance from a goal that an indi-

dPdt

β1 + exp(-α(t-tc))

(t) = -τ (t)P(t) τ (t) =

vidual reaches and β-1 is a time scale that represents the distance characteristic of the destinations in the considered urban context. We note that for t»tc the hazard function tends to the constant β and we have P(t)~exp(-βt) which coincides with the Maxwell-Boltz-mann distribution with β-1 which plays the role of tem-perature. In Figure A we show how this model interpo-lates both the empirical distribution of travel times for journeys by car and the distribution of times for bicycle journeys recorded in the city of Bologna.The model parameters are different in the two cases considered: in particular for cars tc it is equal to 3 min-utes while for bicycles it is 8 minutes while α-1 is esti-mated 1.5 minutes for cars and 2.5 minutes for bicycles. Taking into account the speed ratio between the two types of transport, these results suggest that in Bolo-gna cars and bicycles respond to the same demand for small-scale mobility. Finally, if we consider that the pa-rameter β-1 has a value of 30 minutes for cars and 13 minutes for bicycles, this reflects the fact that the car is still used for routes of longer duration than the bicycle even if the presence of a queue larger than expected

A(left) distribution of journey times of car routes in the city of Bologna (histogram) and interpolation with the model (continuous line) (journeys reconstructed using GPS data in May 2011 - Octotelematics database); (right) distribution of journey times of bicycle routes in the city of Bologna (histogram) and interpolation with the model (continuous line) (journeys reconstructed using GPS data - database Bellamossa 2018 Municipality of Bologna).

B(left) distribution of the Lempel-Ziv entropy calculated on all trajectories of cyclists longer than 15 minutes on a 200-meter spatial scale with a 10-second time step; (right) the same restricting the analysis to the trajectories that start from within the historic center.

in the distribution of cycling routes could suggest the presence of a small fraction of individuals who use the bicycle as the main transport tool. We can therefore classify the typology of the paths us-ing the concept of entropy of information of Lempel-Ziv that measures the compressibility of the symbolic coding of a trajectory [3]. In other words, if we divide the city into different sectors (for example a partition in squares of 200 meters on each side) and we associate a symbol to each sector, it is possible to encode a tra-jectory by associating the symbol corresponding to the square in which the trajectory is located in each given time interval, the Lempel-Ziv entropy corresponds to the ratio between the length of the compressed signal with the Lempel-Ziv algorithm and the original signal length and is measured in bits per character. Figure B shows the analysis carried out on the tra-jectories of cyclists in the city of Bologna using the

trajectories longer than 15 minutes with a sampling of 10 seconds per trajectory. The results show the ex-istence of two types of mobility: a low entropy mobil-ity most likely of origin-destination type and a high entropy mobility that can be associated to a random component in the trajectories. The analysis restricted to the trajectories carried out in the historic center of Bologna shows that the origin-destination compo-nent is actually more present in the historic center than in the whole city.This fact can be interpreted with the fact that the bi-cycle is a transport tool that satisfies the demand for mobility in the center, while it is used for local move-ments in the periphery preferring the car or a public transport for longer journeys. The information entropy is therefore a good indicator to distinguish the charac-teristics of the demand for mobility at the base of the trajectories observed through ICT technologies ■

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field. Furthermore, it follows that the electromagnetic field transmitted through the glass + water structure is reduced by a few dB and even if not shown in the figure it increases its overall dissipation.The parameters of the structure influencing these effects are the dielectric characteristics of the ma-terials and thicknesses of each ma-terial.This simple example suggests that in the real world the scenario of real

6Simulation of the Coefficients of reflection (RC), transmission (TC), of a layer of glass 5 mm thick in the absence and in the presence of a superimposed layer of water of 0.3 mm thickness towards which the electromagnetic field affects for angles of incidence between 0 and 80 °. In the case of oblique incidence of the plane wave of the electromagnetic field on the surface of the materials, the cases of electric transversal (TE) and transverse magnetic (TM) are distinguished, depending on whether the tangent component of the electromagnetic field incident to the multilayer air interface is the electric field or the magnetic field. Only the graphs for the TE type incidence (Electric Transversal) are reported here for simplicity.

electromagnetic propagation can be modified in the event of rain even for the radio frequencies used today. Therefore, that the presence of water on walls, soil, roads and trees modi-fies overall electromagnetic propa-gation in the environment producing in some cases greater reflection and in others a greater attenuation of electromagnetic fields.In literature there are other studies that confirm this direction as already highlighted in the introduction.

To isolate the specific effects of the weather on the signal level available for the devices it is therefore neces-sary to operate in a very selective manner on the MDT measurements to be analyzed, both in geographi-cal terms, appropriately cutting out the area to be placed from time to time, both in temporal terms, to be able to correlate the variations of the weather parameters with the varia-tions of the mobile radio parameters measured by the devices. Only with

7Electromagnetic propagation model through a multilayer sandwich of materials.

this approach is it possible to isolate, study, and therefore measure, those specific effects that the climate in-duces in individual areas [note 1].The magnificent piazza of Padua Prato della Valle, one of the largest squares in Europe, is a good example of an urban area suitable for study-ing the effects of rain on the mobile radio signal. First, the size and con-tinuous attendance of the square provide a good statistical basis for the analyzes. The meteorological survey station is then in proximity of the square, therefore the hourly rain-fall data collected by that station are directly usable, without the need to interpolate data from several weath-er stations (this is possible but would lower the reliability of the weather data in the area in analysis). Finally the position of the main cell serving that area is placed at an optimal dis-

tance from the square (about 1 km) for an urban study, a study in which we want to analyze both effects on the direct lighting component but also possible effects due to reflec-tions and signal refractions.The results obtained (see Figure 8) show an effective reduction, on av-erage, of the power level (RSRP) re-ceived from the devices that were in Prato Della Valle during the rainy time slot (RSRP -87.5 dBm between 16.00 and 18.00 ), while in the ab-sence of rainfall in the morning and early afternoon the RSRP averages were respectively -84.4 dBm and -82 dBm. Similar received power level, -84.5 dBm, was then recorded in the evening period, with the weather having meanwhile returned clear.It was therefore possible to meas-ure a case of influence of the mete-orological context on the provision of

mobile radio services that was also little influenced by behavioral rea-sons of the users, so as to be able to isolate this specific physical effect on the signal received from the other possible contributing factors.The signal power level received by the devices in a specific area is one of the most significant parameters for the quality of the services offered in that area.

Measure cities with MDT

It is known that climate influences the way people live their own terri-tory. And it is also now part of the common feeling that the climatic variations, which have always been able to generate changes to the livability of entire areas of the planet, could still have a profound effect on it due to global warming.It is therefore always necessary to have a double view of the climatic phenomena we undergo, a physi-cal view and a behavioral, human view, that is linked to the reflec-tions on our lives of the physical

Incidentelectromagnetic field

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8Main positions (maps from www.OpenStreetMap.org elaborated with R Study [14]) from which Power MDT measurements of the LTE Reference Signal 4G were carried out in Prato Della Valle (Padua) on 7/6/2018, in four different time slots (9-12, 12-15, 15-18, 18-21), of which the afternoon session (155-18) coincided with a rainfall sequence (about 17 mm of rain in 3 h). The four frames are quite homogeneous between them, thus making it possible to compare the specific variations in power occurring within each time slot, reducing the possible distortions due to other effects (eg if there were extreme rainfall phenomena on 7/6) that area, this would have involved measures not homogeneously distributed, or even completely absent in the bundles.

changes of the ecosystem in which we are immersed.To this end the technological inno-vation brought by MDT, being as-sociated with very frequent meas-urements (radio), to guarantee full continuity of operation to the mo-bile radio system (moreover used in an increasingly intense way), and being MDT also combined with the main instrument of our

times, that is, the mobile phone that we constantly keep with us, is increasingly showing itself capa-ble of forging instruments capable of offering accurate views of the scenarios in which we live, offer-ing us methods of investigation capable of objectively measuring the emerging trends, capable of comparing similar situations in dif-ferent scenarios, capable of moni-

toring the effects of interventions aimed at improving a specific situ-ation, capable of evaluating the effort made to improve, for exam-ple, the mobility of an area, and ul-timately capable to privilege those actions that are really more effec-tive in that specific context, urban or non-urban.The possibility of inferring the be-havioral models of cities (mobil-

9Left: geographical location of the Cell whose power is measured by telephones, the survey point (Botanical Garden) of the weather data of the period under investigation (courtesy of Radarmeteo) and the magnificent square of Padua, Prato della Valle, from which power measurements are taken. On the lower side: the average power loss (RSRP) measured by mobile phones in the square during the three hours of rain, compared to the rest of the period in question, with no precipitation.

ity, entropy, density of citizens in particular social gathering points, etc.) through aggregated MDT data and Machine Learning tools, also in relation to the different lo-cal weather conditions, represents for Mobile Operators an oppor-tunity to develop new services to be positioned on the market in a distinctive manner compared to other ICT players.

The role of the Operator in the future

It is easy to think of the Operator as a mere provider of connectiv-ity, but the wealth of knowledge that houses in an operator now becomes only partly linked to this fundamental professionalism.Disciplines once distant from the daily practice of a TLC Operator,

like the Machine Learning, nowa-days become more and more a daily practice, necessary to face a technological evolution that shows increasing complexity and increas-ingly sophisticated automationThis trend joins new information (computer science, mathematics, etc.) to the consolidated knowl-edge (propagation, connections, protocols, communications mar-ket, etc.), taking place all around the use of tools, such as smart-phones, which now accompany in every moment our lives, is a trend able to free new opportunities for the Operator...With the evolution of networks to-wards 5G, the volume of data gen-erated by the network itself will grow exponentially.This will be partly due to the inno-vative features of the 5G that will enable greater flexibility thanks to the peculiarities of active anten-nas and beamforming, or slicing. It will also be due to the increasing diversity of objects connected and

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managed by mobile radio networks that will support not only tradi-tional smartphones, but a growing number of connected objects, both passive (sensors) and active (ro-botics, drones, vehicles).The increasing complexity in plan-ning and managing the topology of networks and the diversity of use cases will be possible only thanks to a greater intelligence that will be fed by data.Therefore, the network infrastruc-ture will have to be engineered to be AI-ready, and to produce and make available data that will en-able its management. This same data, as can be seen from this ar-ticle, can be analyzed and shared with other data, to produce ana-lyzes and perspectives so far unim-aginable.The potential is very high and quite interesting as well, but it will require an innovative approach that is more oriented towards in-

novation and the creation of an ecosystem.In fact, it will be necessary to im-agine the networks as platforms that, via API and Exposure, enable the provision of network data, in respect of privacy and security.

Conclusions

The analysis conducted in this ar-ticle, although susceptible to re-finement and validation on a sig-nificant number of experimental cases, illustrates the way to move from large sets of MDT measures to synthetic numerical descriptions of the places and related service usage scenarios, to varying weath-er conditions.Synthetic numerical descriptions are very useful for the application of data analysis techniques and for the development of predictive

models, whose training requires coding the aspects that the algo-rithm must learn.Automatically recognizing a us-age scenario and the context in which it is immersed then lends itself to very different applications. These applications are evident if the learning process is grafted into the SON (Self Organizing Network) evolution in networks and continu-ous quality improvement.But the field of application is not exclusively technical.It is easy to understand how this type of study can also benefit the analysis of the ways in which the city is experienced from moment to moment, and how the onset of this new value ends up influenc-ing the very way in which the Op-erators they will reach the market, increasingly expanding the role of connectivity providers ■

Note

[1] The different effects, direct and indirect, that the climate induces on the power received by the devices would re-quire to illustrate multiple types of case studies, within which they can from time to time become predominant depressive effects (absorption) on the signal level or, at On the contrary, it has a more expansive effect (reflections

on the level of the signal received. The set of cases is not adequately treatable in a single article that also focuses on indirect effects, such as behavioral, on the signal level. In this article however, a single example of a significant weather situation is reported

Bibliografia

[1] "La mobilità di breve e lungo raggio con le innovative misure radiomobili e l’Intelligenza Artificiale" - Notiziario Tecnico n.3-2018

[2] "Big Data georeferenziati MDT per servizi digitali nelle Smart Cities" - Notiziario Tecnico n.1-2018

[3] “3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Universal Terrestrial Radio Access (UTRA) and Evolved Universal Terrestrial Radio Access (E-UTRA); Radio measurement collection for Minimization of Drive Tests (MDT)”; Overall description; Stage 2 3GPP TS 37.320 V14.0.0 (2017-03).

[4] Chiara Mizzi, Alessandro Fabbri, Sandro Rambaldi, Flavio Bertini, Nico Curti, Stefano Sinigardi, Rachele Luzi, Giulia Venturi, Micheli Davide, Giuliano Muratore, Aldo Vannelli, Armando Bazzani. “Unraveling pedestrian mobility on a road network using ICTs data during great tourist events”, EPJ Data Sci. (2018) 7: 44, https://doi.org/10.1140/epjds/s13688-018-0168-2.

[5] Rec. ITU-R P.838 “Specific attenuation model for rain for use in prediction methods”

[6] Rec. ITU-R P.530 “Propagation data and prediction methods required for the design of terrestrial-line-of-sight systems”

[7] Rec. ITU-R P.618 “Propagation data and prediction methods required for the design of trans-horizon radio-relay systems”

[8] Rec. ITU-R P.676 “Attenuation by atmospheric gases”[9] Andrea Scaloni, Pasquale Cirella, Mauro Sgheiz,

Riccardo Diamanti, Davide Micheli, “Multipath and Doppler Characterization of an Electromagnetic Environment by Massive MDT Measurements From 3G and 4G Mobile Terminals”, Published in: IEEE Access ( Volume: 7 ), 21 January 2019, Page(s): 13024 – 13034, DOI: 10.1109/ACCESS.2019.2892864

[10] Rogers, N. C., A. Seville, J. Richter, D. Ndzi, N. Savage, R. F. S. Caldeirinha, A. K. Shukla, M. O. Al-Nuaimi, K. Craig, E. Vilar, and J. Austin, “A generic model of

1–60 GHz radio propagation through vegetation” Final Report, May 2002.

[11] Li, Y. and P. Yang, “The permittivity based on electromagnetic wave attenuation for rain medium and its applications” J. of Electromagnetic Waves and Appl., Vol. 20, No. 15, 2231–2238, 2006.

[12] S. Helhel, S¸. ¨ Ozen, and H. G¨oksu. “INVESTIGATION OF GSM SIGNAL VARIATION DRY AND WET EARTH EFFECTS”, Progress In Electromagnetics Research B, Vol. 1, 147–157, 2008.

[13] M.Cubaiu, L.Cerea, R.Nebuloni, G.L.Solazzi, M.Oldoni “Caratterizzazione dei segnali di link a microonde per scopi di modellizzazione e rilevamento pioggia” 05/06/2018 Progetto MOPRAM.

[14] R is a project which is attempting to provide a modern piece of statistical software for the GNU suite of software. The current R is the result of a collaborative effort with contributions from all over the world. R was initially written by Robert Gentleman and Ross Ihaka—also known as "R & R" of the Statistics Department of the University of Auckland. R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

[15] Entropy and its Application to Urban Systems - Ben Purvis et al. – MDPI, 2019

[16] Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone – Gariazzo et al. – MDPI, 2019

[17] An Entropy-Based Model to Infer Social Strength from Spatiotemporal Data – Pham et al. - Univ. of Southern California, 2013

[18] Entropy and order in urban street networks - A. Gudmundsson and N. Mohajeri - Nature Scientific Reports, vol. 3, p. Article number: 3324, 2013

[19] Recommendation P.2040-1 (07/2015). Effects of building materials and structures on radiowave propagation above about 100 MHz.

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http://www.telecomitalia.com/tit/it/notiziariotecnico/presentazione.html

Davide Micheli davide.micheli@telecomitalia.it

Holding a degree in Electronic and Telecommunications Engineering and a degree in Aerospace and Astronautics Engineering, he joined the company in 1989 where he dealt with design, construction, operation and quality in the territorial Area of Ancona until 2001. From 2002 he moved to Rome where he is still working in the field of Engineering of the Radio Access Network dealing with various issues related to the engineering of the network including those related to the study of electromagnetic propagation. In recent years, after obtaining a PhD in Aerospace Engineering, he began to examine in depth the techniques of Machine Learning in his work, in particular, on the Big Data of the electromagnetic statistical type available in the radio access network. He is also the author of numerous scientific articles in international journals

Giuliano Muratore giuliano.muratore@telecomitalia.it

was born in Rome, in 1960. He received the “Dottore Ingegnere” degree in electronics engineering from the University of Rome “La Sapienza” in 1987. He joined the Telecom Italia group in 1987, covering different responsibilities since 1990 to 2017 in technical and innovation departments. During the last years he focused big data analysis techniques to support, with his long network signalling experience, projects aiming at creating value from operator’s data

Aldo Vannelli aldo.vannelli@telecomitalia.it

Holding a degree in Physics and Information Engineering, he joined the company in 1988. After extensive experience in the field of Data Network Engineering and Innovation (Frame Relay, ATM and IP), in 2001 he moves to TIM to take care of the development and innovation of multimedia applications and services on 2.5G/3G technologies. In this context, he coordinated several projects concerning the multi-service integration of voice/video/data on mobile and the development of solutions for the Mobile Content Distribution. From 2012 he works in the Business and TOP Clients Unit where he deals with the development of initiatives and projects for companies of national relevance. For some years he has been interested in the development of initiatives aimed at the realization of Proof of Concept based on Big Data Analytics & Machine Learning techniques

[20] “Valori della costante dielettrica (relativa) e del fattore di perdita (relativo) dell’acqua a diverse frequenze e a due diverse temperature”, Proprietà dielettriche dei materiali, 2006. https://studylibit.com/doc/5634917/proprieta--dielettriche-dei-materiali.

[21] Davide Micheli et al. - “Broadband Electromagnetic Absorbers Using Carbon Nanostructure-Based Composites,” Published in: IEEE Transactions on Microwave Theory and Techniques ( Volume: 59 , Issue: 10 , Oct. 2011 ), 29 July 2011, Page(s): 2633 – 2646, DOI: 10.1109/TMTT.2011.2160198.

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