Thi Thu

Nga Vu

Gilbert Teyssedre
email: gilbert.teyssedre@laplace.univ-tlse.fr

Bertrand Vissouvanadin

Christian Laurent

Charge front and negative differential mobility in HVDC model cables

Keywords: space charge, HVDC, model cable, charge injection, negative differential mobility

Space charge measurements have been carried out by means of pulsed-electro-acoustic (PEA) method on mini-cables with 1.5 mm-thick cross-linked polyethylene (XLPE) as insulation. Measurements were realized at room temperature or with a temperature gradient of 10°C through the insulation (Tin = 70°C, Text = 60°C) under DC voltage of -30kV and -55kV. Space charge results highlight the enhancement of the field at the outerelectrode when combining thermal and electrical stresses. They also reveal systematic occurrence of a negative front of charges generated at the inner electrode that moves toward the outer electrode at the beginning of the polarization step. It is observed that the transit time of the front of negative charge increases, and therefore the mobility decreases, with the applied voltage.

Further, the estimated mobility -in the range 10 -14 -10 -13 m²V -1 s -1 for the present results, increases when the temperature increases for the same condition of applied voltage. The possible origin of this charge front is discussed on the basis of transient charging current measurements realized on the same cables. The features substantiate the hypothesis of negative differential mobility used for modelling space charge packets.

INTRODUCTION

The recent impulse in demand for HVDC cable systems, owing e.g. to the need for higher transmission capacity in applications such as integration of wind power and more interconnected power networks, has reactivated researches on extruded insulations for cables, with new generations of crosslinked polyethylene grade and understanding of space charge features in such cables as main concerns. Because of the nonlinearity of conduction processes in these materials and substantial temperature dependency of conductivity the prediction of field distribution as a function of stressing conditions remains a hazardous exercise. For these reasons, space charge measurements on cable models remain an essential task. In these measurements, space charge features in polyethylene materials have revealed various forms of instability as for example 'slow' charge packets with mobility in the range 10 -14 -10 -16 m 2 V -1 s -1 observed repetitively under relatively high fields (100kV/mm or so) [START_REF] Alison | A high field pulsed electro-acoustic apparatus for space charge and external circuit current measurement within solid insulators[END_REF], or 'fast' charge packets with a much higher mobility of the order of 10 -10 m 2 V -1 s -1 reported more recently and more seldom [START_REF] Fabiani | Fast and slow charge packets in polymeric materials under DC stress[END_REF]. Slow charge packets have been reported for many years, both in thick insulations as in cables [START_REF] Hozumi | Direct observation of time-dependent space charge profiles in XLPE cable under high electric fields[END_REF], or in flat specimen [START_REF] See | Electric field criteria for charge packet formation and movement in XLPE[END_REF].

The repetitive nature of the process is one of the characteristics of charge packets and also one of the challenges when attempting to model the phenomenon [START_REF] Baudoin | Charge packets modeling in polyethylene[END_REF]. So called fast-packets tend to be transient phenomena with very small amount of charge quantity being transported. They were detected under moderate stress, pointing towards harmfulness of the process under DC service field [START_REF] Montanari | Bringing an insulation to failure[END_REF]. In this communication, we report on a specific kind of charge front with an intermediate mobility observed in HVDC miniature cables and featuring an apparent decrease of the mobility of the charge front with increasing the applied voltage.

II. EXPERIMENTAL SECTION

A. Samples

Samples used for conductivity and space charge measurements are crosslinked polyethylene-insulated -XLPEminiature cables provided by Nexans. The test samples are sections of cables of approximately 3 m in length. The insulation thickness is 1.5 mm; the other characteristic dimensions are reported in Fig. 1. Samples were outgassed prior to measurements in order to remove volatile by-products.

B. Space charge and current measurements on mini-cable samples

Space charge measurements have been carried out using the pulsed electro-acoustic (PEA) method through the configuration of Fig. 2. The PEA cable device was provided by TechImp S.p.A., Italy. The cable was fixed to the PEA cell using a mechanical holder insuring a good acoustic contact between the outer SC and aluminum electrode of the measuring device.

A pulse generator, specified with 30ns voltage pulse halfwidth, up to 5 kV amplitude and up to 10 kHz repetition rate, was used as excitation source. The pulses were injected via the outer SC of the cable. The outer SC was removed over a length of 5 cm (cf. Fig. 2) between the measuring location and the region where pulses are applied creating a decoupling capacitor between pulse and HVDC supply. For electrical stress, a negative DC voltage was applied to the cable conductor. Two voltage levels were investigated in this work: -30 kV and -55 kV, providing Laplace fields EI of ~ -30 kV/mm and -55 kV/mm at the interface with the inner SC, respectively; and -14 kV/mm and -26 kV/mm at the outer SC.

Space charge measurements were performed under two different temperature conditions: at room temperature (~22°C) and under a temperature gradient through the cable radius. To achieve a temperature gradient across the cable insulation, a current was injected into the cable core by means of a current transformer, as shown in Fig. 2, while the external surface of the semiconductor was kept in ambient. For the latter condition, the targeted temperature at the inner semiconducting screen was 70°C. Thermal modelling of the cable was achieved to set the current to be injected targeting a temperature of 70°C at the inner SC. The result was a current of 32 A, providing a surface temperature of the cable of 60°C.

The amplitude of pulses in the PEA measurement was set to 400 V and repetition frequency to 5 kHz. PEA measurements consisted in a cycle of 4h/4h of polarization/depolarization. One new cable sample was used for every stress level. Space charge profiles were recorded every 200 seconds throughout the measurement protocol. The calibration procedure used for space charge recovery is described in [START_REF] Vissouvanadin | Deconvolution techniques for space charge recovery using pulsed electroacoustic method in coaxial geometry[END_REF].

Current measurements were realized on mini-cable samples. The outer SC was removed in some section of the cable for preventing surface conduction. Guard electrodes of 1cm width were created at both ends of the active area of measurement by removing the outer SC. The measuring electrode of 20 cm length, 1 cm apart from the guard electrodes, was connected to a Keithley 617 electrometer through a protecting circuit. The cable sample was placed in an oven in air at atmospheric pressure for controlling the temperature. Measurements were performed under different temperatures varying from 30°C to +90°C by step of 10°C. For each of the temperature levels, charging/discharging current measurements were realized for 10 values of applied field ranging between 2 and 25 kV/mm under isothermal conditions. Polarization/depolarization steps last for 1h/1h. A new sample has been used for each value of temperature to avoid possible memory effects. Current values were recorded every 2 seconds all along the measurements.

III. RESULTS AND DISCUSSION

A. Space charge patterns

Fig. 3 shows typical space charge patterns obtained under the different conditions of temperature and stress that have been investigated. Two phenomena are worth mentioning here. On the one hand, negative charges appear to be dominant carriers in the patterns, and appear to be formed from injection from the cathode, i.e. the inner conductor in experiments. The second feature is a front of charges developing upon injection at the cathode and crossing the insulation. The front appears relatively narrow all along its travel through the insulation.

1) Bulk space charge due to conductivity gradient.

Let us consider first the negative charge build-up in the bulk of the insulation and consider what is expected as charge distribution due to non-homogeneous field situation using Maxwell's equation. In cylindrical geometry, in the hypothesis of a radius dependence of the electrical conductivity, it can be deduced considering the conservation of the current that the electric field distribution in the cable insulation, under steady state condition is given by [START_REF] Fabiani | HVDC cable design and space charge accumulation. Part 3: Effect of temperature gradient[END_REF]:

E(r) = E 0 r 0 σ 0 r𝜎(r) (1)
where E0 and 0 are respectively the electric field and conductivity at the reference position r0. Considering the Poisson's equation:

divE = ρ ε (2)
where is the dielectric permittivity of the material, it can readily be deduced that the space charge distribution associated to a non-uniform conductivity along the radius is of the form:

ρ(r) = -E(r) ε σ(r) dσ(r) dr (3)
The conductivity gradient may have several origins in the context of XLPE cables:

(i) a non-linear field dependence of conductivity associated with the radial dependence of the field;

(ii) a thermal gradient along the cable radius when energizing the cable, with inner interface temperature larger than outer temperature;

(iii) a gradient in the material properties, with e.g. crosslinking by-products concentration gradient as reported e.g. by Jeroense et al. [START_REF] Ghorbani | HVDC cable systems-Highlighting extruded technology[END_REF].

Except for the last instance where the nature of material changes and its impact on conductivity has to be anticipated, the sign of the space charge can be easily predicted. Field gradient induced space charge and thermal gradient normally lead to decrease of the conductivity with the radius. Therefore, as by geometry the sign of the electric field is the same as that of the polarity of the conductor, it can be deduced from Eq.3 that a negative voltage applied to the conductor leads to a negative space charge in the bulk of the insulation. These features are observed in the space charge patterns reported in Fig. 3: Increasing the voltage leads to an increase in the negative bulk space charge, owing presumably to the increase in non-linearity, and imposing a temperature gradient leads also to more space charge as variations in activity are in general more sensible in temperature than in electric field.

2) Negative charge packet.

The second feature, i.e. the front of negative charges is appealing: on the one hand, the charge front is relatively sharp; on the other the transit time of the charge front increases when increasing the stress, which appears as a rather uncommon phenomenon. The front of negative charges is somewhat less intense when increasing temperature; however, it is still clearly visible. The transit time is shorter than that at room temperature, but again, it is shorter for the highest stress. Such features could be repeatedly measured on different cable sections of the same source. The magnitude of the charge front is about 1C/m³ and its width at half-maximum about 150µm for data of Fig. 3b, representing a field variation of the order of 3.5kV/mm across the charge packet.

In order to depict in some more detail the features of the charge front we have plotted in Fig. 4 the position of the packet of charges as it crosses the insulation, considering space charge data obtained at room temperature. The decrease of the velocity of the front with increasing the stress is evident. In order to analyze the propagation of the front, we have plotted in Fig. 4b the velocity of the charge packet obtained from derivation of fitted data of Fig. 4.a. as a function of the Laplace field defined as:

E(r) = V 𝑎 r × ln(𝑟 3 /𝑟 2 ) ( 4
)
where Va is the applied voltage to the conductor. One remarkable feature is that the data in Fig. 4b at 30 kV and 55 kV overlap and provide consistent values when the field at the inner conductor at 30 kV is similar to that at the outer conductor at 55 kV. Actually, it must be stressed that the field distribution is no longer exactly the capacitive field distribution as space charge already settles in the first hour of stressing, grading the field. The above results show an apparent negative differential mobility (NDM) of carriers [START_REF] Jones | The contribution of fieldinduced morphological change to the electrical aging and breakdown of polyethylene[END_REF], i.e. a decrease of the mobility with increasing the field. Interestingly, Chen et al. [START_REF] Chen | Observation of negative differential mobility and charge packet in polyethylene[END_REF] reported such process for positive carriers when carrying out space charge measurements when applying voltage pulses under a constant DC field. The reported effect was in the same field range as applied herein. NDM is also one of the processes explaining the propagation of charge packets maintaining their shape [START_REF] Baudoin | Charge packets modeling in polyethylene[END_REF]. Therefore, the propagation of the charge front with maintaining the charge front is already an indication of a NDM. Other reports dealing with negative differential mobility for holes in polyethylene can be found; however, to our knowledge, this seems to be the first evidence for negative carriers.

B. Current transients

Figure 5 shows charging current transients obtained on minicable sample at 40°C under different fields. For almost all temperature values, the charging current increases at short time and reaches a maximum value, then it decreases with time for approaching steady state value. Moreover, the involved mechanism is clearly thermally activated since the time to peak decreases with increasing temperature, following approximately an Arrhenius law (see Fig. 5

.b).

There are at least three processes to consider for explaining such an increase of the transient current during short time:

1) Charges injection and transport through the insulationwith time-to-peak directly related to the transit time of carriers through the insulation;

2) A process linked only to the front of negative charges previously observed on space charge patterns. The time to current peak could be related to the transit of charge front;

3) An association of field dependent conductivity and divergent field (due to cylindrical geometry) with a current transient due to balancing of the field distribution. Hypothesis 3) above has been checked by obtaining a conduction equation function of field and temperature and computing the transient current. No current rise was predicted, discarding this possibility [START_REF] Vu | Transient space charge phenomena in HVDC model cables[END_REF]. In regards to Case 2), we have plotted in Fig. 5.b) the time to current peak as a function of temperature for some voltage values. With extrapolation to room temperature, consistent values of the transit time from space charge and current are obtained (about 2000s for -30kV stress). However, the extrapolation fails for the trends regarding field dependence for fields under 30kV/mm, along with the high temperature case (60-70°C) where the transit time in PEA is much larger than in current. Hence, though more data would be necessary with changing polarity for example, transient processes in space charge and current do not appear correlated in an evident way. Finally, it is worth stressing on the potential harmfulness of such space charge transient processes for cables endurance [START_REF] Montanari | Bringing an insulation to failure[END_REF]. Kinetic energy and field strengthening are among the harmful processes whereby early ageing and breakdown of insulations may occur.

IV. CONCLUSION

Space charge measurements were realized on miniature cables at room temperature or under temperature gradient, and considering different stresses. Homogeneous negative space charge build-up was observed in the bulk of the insulation, consistently with what is expected from a field and temperature dependent conductivity material with a negative voltage applied to the cable conductor. In addition, a single space charge packet was observed to form at voltage application, crossing the insulation. It features negative differential mobility of negative charges with a packet velocity decreasing with increasing field. No evident correlation to current transients could be produced.

Fig. 1 :

 1 Fig.1: Section of mini-cable samples. Inner conductor radius r1=0.7 mm; Inner semi-conductor radius r2=1.4 mm; Insulation radius r3=2.9 mm; Outer semi-conductor radius r4=3.05 mm.

Fig. 4 :Fig. 3 :

 43 Fig. 4: Position of the charge front and velocity of the front as a function of time and field as obtained from data of Fig.3.a) and 3.b).

  Cite as: T.T.N.Vu, G. Teyssedre, B. Vissouvanadin, C. Laurent, "Charge front and negative differential mobility in HVDC model cables", IEEE Int. Conf. on Electrical Insulation and Dielectric Phenomena (CEIDP), Ann Arbor, MI, USA, 18-21 Oct. 2015. Proc. CEIDP, pp. 19-22, 2015. DOI: 10.1109/CEIDP.2015.7352104

  Arrhenius plot of time to peak vs. applied voltage and temperature.

Fig. 5 :

 5 Fig. 5: Charging current transient of mini-cable for different applied voltages and time to current peak function of temperature and field.

  Cite as: T.T.N.Vu, G. Teyssedre, B. Vissouvanadin, C. Laurent, "Charge front and negative differential mobility in HVDC model cables", IEEE Int. Conf. on Electrical Insulation and Dielectric Phenomena (CEIDP), Ann Arbor, MI, USA, 18-21 Oct. 2015. Proc. CEIDP, pp. 19-22, 2015. DOI: 10.1109/CEIDP.2015.7352104


Gilbert Teyssedre

N I Sinisuka

J Y Steven

T T N Vu

Conductivity Measurements and Space Charge Inference in Polymeric-insulated HVDC Model Cables

Conductivity Measurements and Space Charge Inference in Polymer-insulated HVDC Model Cables

Abstract:

The general objective of this work is to obtain a better understanding of polymeric-insulated HVDC cable characteristics, especially related with conduction current and space charge features. It goes with the building of a model of conductivity vs. field and temperature in order to forecast field distribution in cables. Conduction current was measured in miniature of HVDC power cables with construction 1.5mm polymer insulation thickness, 0.7mm inner semiconductor, 0.15mm outer semiconductor and conductor diameter of 1.4mm. Current measurements were performed with applied voltage varying from 2kV to 30kV and temperature in the range from 30°C to 90°C. The time of polarization (voltage applied) and depolarization (short-circuit) were 1h/1h.

The results show that there is not a single conduction mechanism for all applied fields and in the all temperature range. The conductivity is clearly non-linear at voltages above about 10kV (field at inner semicon of 10kV/mm), the threshold being dependent on temperature. With the increase in thermal and electrical stresses, transient currents appear characteristic of processes with massive charge injection at the electrode and therefore the treatment of transport based on a homogeneous conductivity is no longer a sufficient hypothesis.

Index Terms -HVDC Cables, polymeric insulation, XLPE, conduction current, conductivity, space charge

I. INTRODUCTION

In the last 20 years, research has been done for design and development of insulating material, especially polymers, for HVDC cables. The space charge features and conductivity of the insulation versus field and temperature are important topics as they drive the field distribution within the insulation of the cable and therefore directly impact on the design of the cable. The non-linear character of conductivity and its strong temperature dependency bring strong redistribution of the electric field, which in turn manifests by space charge build-up in the cable. One of the processes whereby space charge settles in the material is linked to these conductivity gradients.

To provide an interpretation of space charge profile measurements, it is therefore important to know about the dependencies of conductivity vs. field and temperature. The purpose of the work presented in this paper is to obtain the conductivity characteristics of HVDC model cables as function of temperature and voltage by conduction current measurement.

II. THEORY

In a general way, the current density is the current flowing through the cross-section of a conductor, which consists of the amount of transferred charge per unit time (second). Since the transferred charge is equal to density of charge (ρ) with velocity (ν) at the cross-section in time, then current density (J) is equal to quantity of charge with proper velocity. Charge velocity depends on electric field (E) through a proportional coefficient which is charge mobility (μ). So based on Ohm's Law this relation can be expressed by equation below:

E J          (1)
and relation with conductivity (σ),

    i i i q n     (2)
There are few assumptions about relation between conductivity and temperature [START_REF] Mazzanti | Extruded Cable for High-Voltage Direct-Current Transmission[END_REF] that; "temperature influences the concentration of charge carriers and their mobility; the carriers transport is thermally activated; the carriers are located in potential wells that limit their mobility".

From these assumptions, due to a change in temperature, conductivity can be changed since the mobility also changes depending on temperature. Therefore, based on Arrhenius equation, the relation between conductivity and temperature (T) through thermal activation (ϕ) is given by:

          T k B    exp 0 ( 3
)
where σ0 is the reference value of conductivity, kB is Boltzmann's constant (8.62 x 10 -5 eV/K), and T is temperature in K. Therefore, conductivity depends on electric field and temperature, which for polymeric insulations has been modeled using several expressions [START_REF] Mazzanti | Extruded Cable for High-Voltage Direct-Current Transmission[END_REF][START_REF] Dissado | Electrical Degradation and Breakdown in Polymers[END_REF].

To provide an analytical model of the conduction as a function electric field and temperature, we considered the expression for high-field ionic conduction, a relevant mechanism in HVDC polymeric-insulated cables [START_REF] Mazzanti | Extruded Cable for High-Voltage Direct-Current Transmission[END_REF][START_REF] Dissado | Electrical Degradation and Breakdown in Polymers[END_REF][START_REF] Wintle | Charge motion and trapping in insulators -Surface and bulk effects[END_REF][START_REF] Boggs | Effect of Insulation Properties on the Field Grading of Solid Dielectric DC Cable[END_REF]:

    E E B T k A T E B sinh exp ,             (4)
where A and B are constants.

III. EXPERIMENTAL

A. Materials

Samples from model cables, called minicable were considered in this research. Minicables are special cables that were manufactured for research purposes. With their reduced sizesee Fig. 1, these cables can reduce the cost of production, transportation and testing. Related with the research, especially for measurement of conductivity and space charge, with their geometry, (conductor diameter of 1.4 mm, diameter of outer insulation 5.8 mm), applied voltage is moderate: with this geometry, voltages (in kV) represent roughly maximum electric field (in kV/mm) at the inner semiconductor (SC). These cables have 3 layers from conductor: inner SC, polymer insulation and outer SC.

B. Set-up and procedure

Prior to conductivity measurements, the cables were thermally treated for 2 days at 60°C. Cables were pealed and prepared in 4 layers structure from conductor, inner SC, insulation, outer SC and aluminum foil. Cables were prepared for 3 cell set-up, as depicted in Fig. 2, where side cells act as ground protection to block the surface current. The active length of cable during current measurement was 20 cm.

The experiments were performed at 30 0 C to 90 0 C in steps of 10°C. The conduction current were measured under 2kV, 4kV, 6kV, 8kV, 10kV, 13kV, 16kV, 19kV, 22kV, 25kV and 30kV, but at 80 0 C and 90 0 C, measurement were achieved only up to 22kV, since at these temperatures, the insulation strength could be affected. The oven temperature was stabilized to the desired temperature, and then measurements were performed with duration 1 hour for voltage ON and 1 hour for voltage OFF, for all voltage levels. Then, for each temperature, full measurements required duration more than 22 hours including transition process. Since the measurement needed long duration with various voltage levels, a different sample was used for every temperature level. For the results, conduction currents vs. field and temperature were obtained after 1 hour measurement assuming it is a steady current. For cases were noisy currents were measured, averaging over reliable data points was achieved. The obtained field-current characteristics are shown in Fig. 3.

From 1h current measurement, it is clearly seen that under constant electric stress (considering applied voltage) and temperature, for most of measurements there is a transient current regime, as discussed below. It must be stressed that 1h polarization is not enough to reach a steady state current, especially at low temperature/low field.

From these results, as example for 30 0 C as shown in Fig. 4, it can be seen that there are transient currents after the voltage application, with in many instances a peak in the transient current. The shapes of current transients for all temperatures/fields follow the general 4 curves depicted in Fig. 5. The curves are classified depending on time to peak of the current. The below pictures may appear as a combination of continuously decaying curve (C) and some transient composed of a peak in current followed by a slow decrease to steady state. There are cases where the minimum in current is at short time, not in the steady state (schematized by curve A). The most classical shape in transient current is as curve C. One of the reasons for this is the build-up of orientation polarization in the material. Normally charging and discharging polarization currents are symmetrical: this is a criterion to identify the process. Even if polyethylene is a non-polar material, there are some residues in it that are polar groups.

Let us now consider transport contributions to current. Theoretically, if at time = 0, there is no charges in the insulation, then the current must be increasing in time (at short time), owing to the fact that the external current in the circuit is the average over volume of the transport current. However, not often such transient features are seen, because they can be compensated by the above described polarization current. Once charges have been injected, they move through the insulation and the space-averaged transport current increases. When the front of charges reaches the opposite electrode, the current evolves (slow decrease) towards steady state. We can reasonably suppose that the 4 typical curves described in Fig. 5 are a superposition, of these two basic mechanisms, the relative amplitudes being varying along with the characteristic times.

We consider curves can be described by the following features: i/ time of current to decrease due to polarization process (∆d), ii/ time of current to increase (∆u) to reach the peak current; and iii/ current decrease from peak to quasisteady state (∆y). Curve A shows the two time constants. In curve B, ∆u is very fast (∆uB << ∆uA), then only ∆d is seen. In curve C not only ∆u but also prediction that ∆d is too fast (∆dC << ∆dA), then the peak current is not seen. For curve D, besides ∆d is very fast, we observed another current peak.

Consider just the transient current peak. There can be several reasons for the appearance of this process: 1) Due to injection and transport of charges through the insulationin that case, the carrier transit time would be measured; 2) A specific case of 1) associated with a front of charges observed by space charge measurements in the same samples like shown in Fig. 6. However, the transient front of charges is slower in PEA: 30min to 1h at RT (which seems not consistent with data presented here); slower with increasing voltage, not faster like in the current results. 3) Due to an association of field dependent conductivity and divergent field (cylindrical geometry) where current transient could correspond to the equilibration of the field distribution, in this case then one need to compute conductivity in voltage and temperature variation. In respect to hypothesis 1), we consider the time to peak of conduction current. The voltage and temperature variations of the time to current peak are shown in Fig. 7. From this transit times, we have estimated the velocity of the front of charges and then the mobility of carriers from the average field, see [START_REF] Mazzanti | Extruded Cable for High-Voltage Direct-Current Transmission[END_REF] and [START_REF] Dissado | Electrical Degradation and Breakdown in Polymers[END_REF]. The obtained values of mobility are in the same range as those given by other authors (Seanor, Davies, Tanaka and Calderwood) in Ref. [START_REF] Raju | Dielectrics in Electric Fields[END_REF], i.e. 5.86 x 10 -13 m²V -1 s -1 to 2.82 x 10 -13 m²V -1 s -1 for varying voltage at 30 0 C, and 1.65 x 10 -11 m²V -1 s -1 to 2 x 10 -11 m²V -1 s -1 , for varying voltage at 60 0 C. 3) was obtained by plotting the conduction current vs. reciprocal temperature in K, cf. Fig. 8. Linear fit to the data provided a thermal activation energy of 0.57 eV in average (for data under different voltages). Data at different fields were fitted to (4) to obtain a parameterized equation of conductivity as:

    E E T k T E B 8 10 1 . 9 sinh 57 . 0 exp 85 . 31 ,              (5)
where the field is in V/m, and conductivity in  -1 .m -1 . Equation ( 5) is relevant to high-field ionic conduction as well as to hopping conduction, cf. [START_REF] Teyssedre | Charge Transport Modeling in Insulating Polymers: From Molecular to Macroscopic Scale[END_REF] and references therein. So, at the present stage, it is not possible to identify the actual transport mechanism.

Fig. 8 shows a superposition of measured and fitted current using [START_REF] Raju | Dielectrics in Electric Fields[END_REF]. The conductivity becomes clearly non-linear in field beyond an apparent threshold of about 13kV/mm. The nonlinear feature of conductivity can constitute an advantage for the design of cables, as it contributes to field grading along the cable radius: In condition of conductivity gradient as in cables, it tends to homogenize the field and to balance out the stress inversion process associated with the temperature gradient, i.e. field at the outer SC becoming higher than that at the inner SC of the cable [START_REF] Boggs | Effect of Insulation Properties on the Field Grading of Solid Dielectric DC Cable[END_REF]. The activation energy obtained in the present work is rather on the lower range compared to literature data on XLPE. For example, Hestad et al. [START_REF] Hestad | Electrical conductivity of medium voltage XLPE insulated cables[END_REF] measured activation energies close to 1.5eV using the same principle as herein, working with XLPE-insulated MV cables. The low activation energy of conductivity is an objective for the insulation of HVDC cables as the stress inversion process is strengthened when high conductivity gradient along the cable radius is present [START_REF] Boggs | Effect of Insulation Properties on the Field Grading of Solid Dielectric DC Cable[END_REF]. Conduction currents were measured on polymeric-insulated HVDC model mini-cables. Results revealed transient currents, with shape depending on field and temperature. Besides decaying transient due to polarization processes, a peak in current was observed, time to peak decreasing with increasing field or temperature. This feature presumably arises from the injection of charges and their transit through the insulation. However, it seems not related to the front of charge shown in space charge measurements. The thermal activation energy for conductivity was estimated to 0.57 eV, and a functional dependence of conductivity vs. field and temperature has been obtained. From the modeling, conductivity threshold can be identified at about 13kV/mm. Work is in progress to compare measured and predicted space charge and field profile within the cables under thermal gradient.

  Cite as: J.Y. Steven, T.T.N. Vu, G. Teyssedre, N.I. Sinisuka, "Conductivity measurements and space charge inference in polymeric-insulated HVDC model cables", Int. Conf. on High Voltage Engineering and Application (ICHVE), Poznan, Poland, 8-11 Sept. 2014. Proc. ICHVE-14, pp. 1-4, 2014. DOI: 10.1109/ICHVE.2014.7035386

Figure 1 .Figure 2 .

 12 Figure 1. Sample cable section

Figure 3 .Figure 4 .

 34 Figure 3. Conduction current results on mini-cables as a function of field for different temperatures.

Figure 5 .

 5 Figure 5. Curve models of current measurement results

Figure 6 .

 6 Figure 6. Charge density distribution measurement on minicable by pulsed electroacoustic method under -30kV with a thermal gradient of 12°C (72 / 60°C at in/out-SC)

Figure 7 .

 7 Figure 7. Time to peak of current vs. temperature

Figure 8 .

 8 Figure 8. Fit of conduction current data to the model V. CONCLUSIONS

ACKNOWLEDGMENT

We acknowledge Nexans for provision of the samples, and one of us (J. Y. S.) thanks PLN for the financial support.


B Qiao

G Teyssedre

C Laurent

AC Electroluminescence spectra of Polyethylene Naphthalate: impact of the nature of electrodes

One of the unresolved questions in the EL spectrum of insulating materials in general is the occurrence of red emissions under AC electric field when using e.g. silver or gold as metal electrodes. Surface plasmons (SPs) effects and/or charge recombination on surface states have both been advocated as possible origins for such emissions. In this contribution, EL of 25µm Polyethylene 2, 6-Naphthalate (PEN) films are investigated using Indium Tin Oxide (ITO) and gold (Au) electrodes separately, the singularity of ITO being that it does not exhibit the SPs effects characteristic of noble metals (e.g. gold) in the visible domain. EL spectra and EL-field characteristics were recorded under DC and AC fields, as well as phase-resolved EL patterns. Emission spectra from ITO-PEN-ITO do not reveal the red emission contrary to that of Au-PEN-Au. Results are discussed regarding both the origin of the emissions, and the consequences that SPs-mediated emissions may have on EL features such as EL-field characteristics, spectra, and phase patterns.

I. INTRODUCTION

The electroluminescence (EL) of insulating polymers is an exciting field of research because it gives insight into electrical ageing and breakdown processes. Various physical and chemical mechanisms have been involved in the aging of electrical insulations under DC and AC stress [START_REF] Laurent | Hot electron and partial-discharge induced ageing of polymers[END_REF][START_REF] Aubert | Electrically active defects in silica-filled epoxy as revealed by light emission analysis[END_REF]. Electroluminescence (EL) is a phenomenon involving physical processes such as charge injection, extraction, and transportation, but also chemical reaction through excited states. Electric fields can produce excited or ionized states, some of which have a non-zero probability for light emission during their relaxation and for chemical reaction in their excited states. Hence, on one hand, EL can be a probe of charge excitation and recombination processes, on the other hand, it may provide information on the nature of defects and luminescent sources. Because interfacial electronic states are very important in charge transfer at the electrode-dielectric interface, it should impact on the EL features, especially under AC stress. Therefore, EL can also gather information and be used to probe the physical/chemical processes occurring at the metal-dielectric interface.

Furthermore, electromagnetic properties at metal-dielectric interface undergo an increasing interest in a booming area of sciences called "plasmonics". Surface Plasmons (SPs, or more exactly Surface Plasmon Polaritons, SPPs) are electromagnetic excitations that propagate along the interface between a metal and a dielectric. It can be excited by a supply of energy, either photon or electron irradiation on a rough (not perfectly smooth) surface [START_REF] J M Pitarke | Theory of surface plasmons and surface-plasmon polaritons[END_REF]. Therefore, SPs effect can be expected at a dielectric/metal interfaces submitted to an electric field. For these reasons, electroluminescence phenomena observed in the red region (600-850nm) in insulating polymers with noble metal electrodes (Au, Ag) have been attributed to SP emission [START_REF] Teyssedre | Ac and dc electroluminescence in insulating polymers and implication for electrical ageing[END_REF]. SP processes should not be present in indium tin oxide (ITO), a transparent conducting oxide used as electrode in electroluminescent devices.

The aim of this paper is to analyze the impact of the nature of different electrodes on electroluminescence features through EL field characteristics, spectral analysis, and phase angle analysis under AC field. In this way, the origin of the red emission should be unraveled. Polyethylene 2, 6-Naphthalate (PEN, an aromatic polyester), is particularly suitable for understanding electroluminescence in polymeric dielectrics, not only because it gives a high performance with physical, thermal, and chemical characteristics but also EL intensity of PEN under AC and DC field is relatively strong.

II. EXPERIMENTAL PROCEDURES

All the measurements were performed on PEN films of 25 μm thickness supplied by Dupont (Dupont Teijin Films Teonex ® Q51). These PEN films were metallized with ITO electrodes (thickness: 200 nm, diameter: 50 mm) and Au electrodes (thickness: 30 nm, diameter: 50 mm), respectively. Samples, sandwich structures as Au-PEN-Au and ITO-PEN-ITO, were designated.

EL experiments were performed at room temperature, under high vacuum (10 -7 mbar) in order to avoid gaseous discharges. Light detection is carried out by two systems: a cooled photomultiplier (PM) working in photon counting mode for integral light detection, and a grating monochromator (4.5 nm in resolution) coupled to a liquid nitrogen cooled charge coupled device (CCD) camera for spectral analysis which covered the wavelength from 300 nm to 850 nm.

III. EXPERIMENTAL RESULTS AND DISCUSSION

A. EL vs. Field Characteristic

In Fig. difference between two kinds of electrodes. The EL thresholds are both at about 160 kV/mm (though slightly lower for Au-PEN-Au). However, under AC stress, it can be seen clearly that there is an embossment for the Au-PEN-Au sample in the low field region, increasing with an increase in frequency, which does not exist in the ITO-PEN-ITO. This embossment could be eliminated from the EL spectrum of several polymers with Au electrodes using a cut-off optical filter in the red part of the spectrum [START_REF] Teyssedre | Ac and dc electroluminescence in insulating polymers and implication for electrical ageing[END_REF]. This was a first indication of the existence of several contributing processes, with distinct field dependencies, to the EL of insulating polymers.

B. Spectra

The emission spectra of the PEN samples with Au or ITO electrodes are shown in Fig. 3 in order to identify and analyze the light components. The shape of the spectra is fully consistent with those reported earlier, with bands at 500, 578, 618 and 675nm [START_REF] Teyssedre | Dependence of electroluminescence intensity and spectral distribution on ageing time in polyethylene naphthalate as modeled by space charge modified internal field[END_REF]. In both two graphs of Fig. 3, under DC stress, normalized at 578nm, the bands at 618nm and 675nm are strengthening with the increase of the field. In the same time, the contribution of the wide band at approximately 500 nm clearly decreases. In brief, the two spectra follow the same trends with the field.

However, under AC stress as in Fig. 4, the spectra of PEN with Au and ITO electrodes are obviously different. Under low field as in Fig. 4 (a) and (b), the maximum EL intensity of the broad peak in Au-PEN-Au sample is at about 650 nm, while that of ITO-PEN-ITO sample is rather flat from 450 nm to 650 nm. The EL intensity of the former is also much stronger than the later under the same field. Hence, there exists an additional red component emission (600nm-750nm) in the Au-PEN-Au samples, which does not exist in the ITO-PEN-ITO samples and which is also not observed in both two samples under DC stress. It is tempting here to associate the emission in the red to the process at the origin of the embossment in Fig 2 . From Fig. 4 (c) and (d), in the high field range, the spectra are almost the same with different electrodes, demonstrating that the red component has a higher contribution to the EL at low field.

C. Origin of the Red Component Emission

There are many experimental facts inclining for an emission process occurring at the contact of electrode/polymer under AC, due to charge injection/extraction at the interface [START_REF] Laurent | Charge dynamics and its energetic features in polymeric materials[END_REF]. This also explains the fact that the red component is not detected under DC because the EL comes from the bulk of the sample in that case, contrary to the AC case where charge exchange is mainly occurring at the contact. The fact that the red component is not observed with ITO electrodes under AC is another evidence that its source is located at the contact. But there are still two possibilities: either the electrodes themselves are involved in the emission process, or the interface states.

In the interface between gold and PEN, there are interface states due to structural disorder, along with possible chemical disorder due e.g. to oxidation. Both of them may lead to specific emission spectra. Under AC field, with the injection and extraction of charge carriers, these states can act as recombination centers and could emit red emission as postulated in [START_REF] Mizuno | Electroluminescence in Insulating Polymers in ac Electric Fields[END_REF]. Though the surface states of PEN metalized with Au or ITO should be almost the same, SPs in Au could produce surface enhanced luminescence [START_REF] Fort | Surface enhanced fluorescence[END_REF], boosting therefore luminescence from charges recombination on surface states.

The other possibility is direct emission from SPs. Indeed, it is established that SPs, that are coherent electron oscillations can relax radiatively [START_REF] Ritchie | Surface plasmons in solids[END_REF]. Along a perfectly smooth interface, the momentum ksp of SPs is parallel to the interface and is always greater than the momentum kph of photons, precluding light emission from SPs (The momentum of both particles, photons and surface plasmons, must match). Along a rough interface however, SP momentum components parallel to the interface can match the momentum of photons. Therefore, SPs can radiate at a rough interface. The optical red emission due to SPs from Cu, Ag, and Au was investigated by P. Dawson et al. [START_REF] Dawson | Observation and explanation of light-emission spectra from statistically rough Cu, Ag, and Au tunnel junctions[END_REF].

The surface roughness of our PEN film is shown in Fig. 5. The root mean square roughness, i.e. Rq, is 21.1nm, the average roughness, i.e. Ra, being 14.5nm, the maximum of roughness, i.e. Rmax, being 331 nm, which should be almost the same with the roughness of electrode. The interface under study possesses different scales of roughness, providing the conditions for coupling SPs and light. More sharp tips at nanoscale may improve the light emission due to SPs.

The spectral distribution of light emitted by SPs depends on the surface roughness of electrodes itself and the energy distribution of charge injection/extraction produced by the field. Hence, the roughness of electrode surface and the field applied on it are two key factors. Under AC stress, due to alternative injection at the surface of electrodes, high density of carriers always exists here. So under AC low field, the energy distribution meets the requirement of SPs. However, under DC stress, charge injection starts, then due to screening effect, the current density of injected carriers is much less than in the AC case. The peak of current density happens in each half cycle as modeled in [START_REF] Baudoin | Modeling electroluminescence in insulating polymers under ac stress: effect of excitation waveform[END_REF]. Hence, we consider that the red emission is mediated with SPs effect.

D. Phase-resolved EL

Interpretation of the EL dependence on voltage waveform can give us information on charge injection and extraction as well as transport and recombination. More importantly, it may be also influenced by the red emission component. Fig. 6 shows the phase-resolved EL patterns of PEN with the two kinds of electrodes under study for sinusoidal waveform voltage. Using a cooled photomultiplier, the counting dwell time was set suitable for phase-resolved EL measurements with 200 data points in one cycle under AC voltage waveform. The noise level is the same as in Fig. 1 and Fig. 2, and is negligible for these measuring conditions under AC. From Fig. 6, the light is emitted twice in one voltage cycle as is typical in EL experiments. In one EL cycle, EL intensity increases relatively slowly with the voltage ramping up, then it decreases more rapidly after the crest of voltage. A phase shift between the max of voltage and the max in EL is observed, which changes with the increase of the field. The EL phase angle, defined as the distance between the voltage maxima and EL wave crest are not the same for the two samples.

The average phase angles for positive and negative voltage half cycle are plotted in Fig. 7. EL phase angle of Au-PEN-Au changes drastically from 32.4 ° at low field (160 kV/mm) to 1.8 ° at high field (272 kV/mm), while that of ITO-PEN-ITO gradually decreases from 18.0 ° to 9.0 °. Under AC field, injected charges either are trapped or recombine with carriers of opposite polarity trapped during the previous half cycle. EL is emitted in advance to the crest voltage because the recombination rate of carriers is at maximum there. It can be seen that EL phase angle depends on field and it also depends on electrodes. EL phase angle has also indeed some relationship with the red component emission. Given the relatively enhanced contribution of red emission at low field, the results tend to show that the phase angle of EL from SPs would be larger than that from PEN emission. Although we need more experiments to make it clear, especially using optical filters with phase detection, it seems the red component has a specific phase angle dependence vs. field.

IV. CONCLUSION

Electroluminescence characteristic and spectra under DC and AC stress have been investigated in polyethylene naphthalate (PEN) with gold and ITO electrodes. Under low AC field, an additional red emission component is revealed with Au electrode and not with ITO. The red emission is added to the original EL intensity and spectral distribution of PEN. So it may derive from a new source. We have proposed that surface plasmons at the surface of gold emit the red emission whereas ITO electrodes cannot radiate light due to its optical properties. The roughness of electrode surface and the energy distribution of charge injection/extraction produced by the field are two key factors that should affect surface plasmons.

Phase-resolved EL is emitted twice in one AC sinusoidal waveform field cycle. EL intensity peaks are in advance of voltage maxima, the phase advance depending on the field. This is controlled by the recombination of injected carriers and trapped carriers with opposite charges. Injected charges either are trapped or recombine with trapped carriers of opposite polarity from last half field cycle, generating advance of EL crest. More interestingly, with Au electrodes the phase angle changes with field more drastically than with ITO. At the ramping region of field, the SPs-mediated red emission exists and may contribute an additional light, result in their different phase angles. This can be an indirect evidence of SPsmediated red emission. Further work is needed to fully understand the SPs-mediated red light emitting process.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Electroluminescence intensity and current vs. field characteristics of Au-PEN-Au and ITO-PEN-ITO under DC stress. The dotted line is EL noise level. "cp2s" stands for counts per 2 seconds. Data are taken after 10 min under constant voltage.

Fig. 3 .Fig. 4 .

 34 Fig. 3. Spectra of (a) Au-PEN-Au and (b) ITO-PEN-ITO under DC stress, Spectra normalized at 578nm, EL578nm=100.

Fig. 5 .

 5 Fig. 5. Topography of a PEN film obtained by AFM. Statistics on the image are as follows: r.m.s. roughness Rq=21.1nm, average roughness Ra=14.5nm, max. amplitude Rmax=331nm.

Fig. 6 .

 6 Fig. 6. Phase-resolved EL of Au-PEN-Au and ITO-PEN-ITO under AC sinusoidal waveform stress, measured at uniform frequency 40 Hz under different field. All the data are normalized at EL wave crest.

Fig. 7 .

 7 Fig. 7. Phase angle of Au-PEN-Au and ITO-PEN-ITO under AC stress.

  1, the EL and current vs. field characteristics of PEN under DC stress are plotted for different electrodes. EL vs. field of PEN under AC stress at different frequency is also shown in Fig. 2. Under DC stress, there is no significant Cite as: B. Qiao, G. Teyssedre, C. Laurent, "AC Electroluminescence spectra of polyethylene naphthalate: impact of the nature of electrodes", IEEE Int. Conf. on Electrical Insulation and Dielectric Phenomena (CEIDP), Shenzhen, China, 20-23 Oct. 2013. Proc. CEIDP-13, pp. 93-96, 2013.

DOI: 10.1109/CEIDP.2013.6747421

ACKNOWLEDGMENT

The authors are indebted to Kremena Makasheva and Bernard Despax for stimulating discussions and would like to thank China Scholarship Council (CSC) for the grant.


Feihu Zheng

Gilbert Teyssedre

Christian Laurent

Cécilien Thomas

Mario Hoyos

Yewen Zhang

Use of Dynamic Space Charge Measurements to Differentiate Charge Injection and Ionic Dissociation in Polypropylene

The time resolved fast pulsed electro-acoustic (PEA) [1-3] method is used to investigate the space charge characteristics in an additive-free poly(propylene-ethylene) (PPE) copolymer in film form. The 190μm-thick films were submitted to DC and low frequency AC stress for various durations at various temperatures. Upper electrode (UE) and lower electrodes (LE) is semi-conductive polyethylene and aluminum, respectively.

When positive 50 kVdc/mm is applied to UE at 25 °C, negative charge is injected from LE and shifts up to UE during 1 hour stress. No positive charge is detected by PEA. However, at 50 °C, hetero-charge formation is observed with approximately equivalent densities for positive and negative carriers under the same applied field (Figure 1). Negative charge in the bulk becomes more and more dominant for long stressing time. A similar process (i.e. symmetrical heterocharge accumulation at first and then dominance of positive charge carriers in the bulk) occurs when a negative voltage is applied to the UE. The symmetrical hetero-charge arises after HV application for 30 s.

Two frequencies of 5 mHz and 100 mHz are selected: the half-periods are therefore respectively longer and shorter than the time for hetero-charge detection at that field. At 50 °C, surprising periodical symmetrical hetero-charge accumulation is observed under 5 mHz square 50kV/mm RMS stress, while only injected charge is observed at 0.1 Hz square 50 kV/mm RMS stress (Figure 2). Because the charge injection rating is symmetrical not for the asymmetrical electrode structure, the symmetrical hetero-charge can not be attributed to the injected charge transiting across the bulk. Combining dc data with ac data, it is shown that the charge build-up in the bulk results from different processes for homo-charges and hetero-charges: The homo-charge is injected from electrodes and the molecular dissociation in the bulk can be the original source of the symmetrical hetero-charge. The time of the hetero-charge emergence under high voltage application depends on the field intensity, being shorter for higher field (Table 1). The time resolved heterocharge migration shows ionic transport characteristics, which can be well fitted with an ionic transport model. The molecular dissociation is temperature and applied field dependent [START_REF] Dissado | Electrical Degradation and Breakdown in Polymers[END_REF]. We observed the symmetrical hetero-charge again in the film bulk under 100 kVdc/mm at 25 °C.

1 .

 1 Pohl Institute of Solid State Physics, Tongji University, 1239 Siping Road, Shanghai 200092, China 2. Laboratoire Plasma et Conversion d'Énergie, Université de Toulouse, CNRS-UPS-INPT, 31062 Toulouse, France 3. Instituto de Ciencia y Tecnología de Polímeros, CSIC, c/ Juan de la Cierva, 3, 28006 Madrid, Spain

Figure 1 .

 1 Figure 1. Space charge dynamics in PPE film under 50 kVdc/mm at (a) 25°C and (b) 50 °C

Figure 2 .

 2 Figure 2. Space charge dynamics in PPE film under 50 kVac/mm at 50 °C at (a) 5 mHz and (b) 0.1 Hz

Table 1 .

 1 1 Hz Time for the onset of quasi-symmetrical hetero-charge apparition in PPE film under different field at 50 °C

	applied field (kV/mm)	15	30	50 100
	time (s)	1800 340 30 0.3

(a) (b) (a) (b)

ACKNOWLEDGMENT Two of the authors (F. Z. and Y. Z.) are grateful to National Natural Science Foundation of China (No.50537040), Shanghai Committee of Science and Technology (contract No. 07DZ22302) and Program for Young Excellent Talents in Tongji University. We acknowledge the French Ministry of education for providing a Post-Doc grant.


Morgane Goetz-Fu

Marion Cortet

Cyril Huissoud

Pascal Gaucherand

Benoît De

La Fournière

Corinne Dupont

Birth plans : Developing a decision aid tool following a Delphi consensus process

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Projet de naissance : Construction d'un outil d'aide à la décision médicale partagée

Méthode Introduction

Le projet de naissance est défini par la HAS comme la conjonction entre les aspirations de la femme et du couple et l'offre de soins locale mais il n'existe à ce jour pas de référentiel pour les construire avec les professionnels de la périnatalité en France.

Conclusion et perspectives

-Grâce à une méthode Delphi, nous avons pu commencer à construire un outil d'aide à la décision médicale partagée pour accompagner la rédaction des projets de naissance. -La validation de l'étape 3 en cours de finalisation par les patientes permettra de développer un outil numérique sous la forme d'une application connectée.

GOETZ-FU M 1 , CORTET M 1 , HUISSOUD C 2 , GAUCHERAND P 1 , DE LA FOURNIERE B 1,4 , DUPONT C 2,3 , GAUCHER L 2 .

1, Département de Gynécologie-Obstétrique, Hôpital de la Croix-Rousse, Groupement Hospitalier Nord de Lyon, 103 Grande Rue de la Croix-Rousse, 69004 LYON.

2, Département de Gynécologie-Obstétrique, Hôpital Femme-Mère-Enfant, Groupement Hospitalier Est, 59 boulevard Pinel, 59500 BRON.

des thèmes avec les associations de patientes Créer le contenu avec les professionnels de santé

3, Laboratoire RESHAPE -Research on Healthcare Performance -Coordination du réseau AURORE 4, LabTAU INSERM U1032, UNiversité Lyon 1, Lyon 03, France Définition "L'équipe médicale est là pour vous conseiller et vous aider à choisir le moment de poser la péridurale. Elle vous propose également d'autres options pour gérer la douleur."

Valider le contenu avec les patientes

Cet item est-il pertinent pour vous pour construire votre projet de naissance ?

Etape 02 Etape 01 Etape 03 Consensus 1 Consensus 2 TOTAL Méthodes d'analgésie en cours de travail (28 items)

	17 (60%)	3 (27%)	20 (71,4 %)

Administration de médicaments en cours de travail (25 items)

	Diffusion
			1er tour de consensus
	Méthode de consensus
	selon Delphi
	2e tour de consensus
		16 (64%)	2 (17%)	18 (64,3 %)
	Déroulement pratique du travail (27 items)	17 (62%)	8 (70%)	24 (88,9 %)
	Accueil du nouveau-né en salle de naissance (21 items)	11 (52%)	2 (20%)	13 (61,9 %)
	Protection périnéale (20 items)	10 (50%)	5 (50%)	15 (75 %)
	TOTAL	71 (57,4%)	20 (37,7%)	91/124
				73,3 %


Nazih Salhab
email: nazih.salhab@univ-eiffel.fr

Rana Rahim
email: rana.rahim@ul.edu.lb

Rami Langar
email: rami.langar@univ-eiffel.fr

Autonomous Anomaly Detector for Cloud-Radio Access Network Key Performance Indicators

Keywords: OpenAirInterface (OAI), TICK Stack, Cloud Radio Access Network Slicing, Autonomous Anomaly Detection, FlexRAN Docker

In this demonstration, we present an autonomous anomaly detector for Cloud Radio Access Network (C-RAN) performance metrics through a prototype using OpenAirInterface implemented as Docker containers. First, we show network slicing configuration capabilities including slice lifecycle management in addition to devices re-homing through our developed northbound interface allowing to manage a software-defined radio access network controller. Next, using conventional off-the-shelf smartphones, we run experiments to present the real-time data display in addition to the instant detection of anomalies in time-series C-RAN data. We demonstrate a reaction to such detection through the auto-trigger of planned responses.

I. INTRODUCTION

Fifth Generation (5G) is anticipated to use Network Slicing (NS) to deliver multiple types of services suitable to heterogeneous requirements of vertical applications [START_REF] Nakao | End-to-end Network Slicing for 5G Mobile Networks[END_REF]. Such heterogeneity requires agility that employs dynamic lifecycle management of NS. However, Key Performance Indicators (KPIs) of such agile network can deteriorate fast. Thus, Autonomous Anomaly Detection (AAD) on collected KPIs and its real-time analysis plays a crucial role in the performance management of 5G networks. AAD allows to minimize related risks in a timely manner before it becomes out of control. Indeed, managing a deterioration at its early stage would be much easier than when it is overlooked and related damages have taken place. In this context, we exploit the periodic measurements genered by Cloud Radio Access Network (C-RAN), next generation Node-B (gNB).

In this demo, we demonstrate the real-time streamprocessing of KPIs to detect anomalies on C-RAN performance and auto-trigger planned responses as outcome of such detection to efficiently address deterioration and maximize service objectives.

Authors in [START_REF] Nakao | End-to-end Network Slicing for 5G Mobile Networks[END_REF] discussed how to introduce OpenAirInterface (OAI) [2] in a prototype called "FLARE", standing for an "Open deeply programmable network node architecture".

Authors in [START_REF] Huang | Design and Prototype of A Virtualized 5G Infrastructure Supporting Network Slicing[END_REF] elaborated the design of a prototype of a virtualized 5G Infrastructure supporting network slicing using OAI, Mobile Central Office Re-architected as a Datacenter (M-CORD) [START_REF][END_REF] and OpenStack [START_REF][END_REF].

Authors in [START_REF] Foukas | Experience building a prototype 5G testbed[END_REF] described their experience building a 5G prototype using OpenStack to leverage an infrastructure as a service platform. Differently from these works, we interface our prototype with KPIs engine and we use Docker containers [START_REF] Docker | [END_REF] to implement the service based approach for deployment.

Our contributions can be summarized as follows: i) we develop a Northbound Interface (NBI) for configuration management of the software-defined radio access controller, ii) we integrate a software stack [START_REF] Influxdata | [END_REF] for C-RAN metrics acquisition, visualization and stream-processing, and iii) we develop a script for time series autonomous anomalies detection in metrics trends using both instant flagging by comparing the metric value to a pre-set threshold or using Welch's T-test [START_REF] Welch | The generalization of 'Student's' problem when several different population variances are involved[END_REF], that we made openly available [10]. The remainder of this paper is organized as follows. In section II, we describe our prototype architecture, while in section III, we elaborate the demonstration steps.

II. PROTOTYPE DESCRIPTION

We use a prototype that is a further development of our previously built platform [START_REF] Salhab | Machine Learning Based Resource Orchestration for 5G Network Slices[END_REF]. The elaborated architecture with added building blocks (interface to Slack channel [START_REF] Slack | [END_REF] and AAD sub-module) is depicted in Fig. 1. As C-RAN controller, we use FlexRAN [2] due to its lesser footprint in terms of Central Processing Unit (CPU) utilization and memory consumption compared to alternative software-defined radio access network controller such as FLARE and Orion [START_REF] Foukas | Orion: RAN Slicing for a Flexible and Cost-Effective Multi-Service Mobile Network Architecture[END_REF]. The Southbound Interface (SBI) of FlexRAN uses Google Protocol Buffer (Protobuf) [START_REF] Foukas | Orion: RAN Slicing for a Flexible and Cost-Effective Multi-Service Mobile Network Architecture[END_REF]. In a software-defined networking paradigm, a controller is the central point interfacing multiple underlying nodes through its SBI, thus, we used FlexRAN to centralize the collection process of the C-RAN metrics. Infrastructure layer is implemented using OAI-5G [2] to implement the C-RAN with fronthaul split with a Digital Unit (DU) and a Central Unit (CU), backhauled to the Evolved Packet Core (EPC), which is also implemented using OAI-Core Network [2]. We use a Universal Software Radio Peripheral (USRP) from National Instruments, that is B210 [14] to implement the radio unit onto which a Commercial-Off-The-Shelf (COTS) User Equipment (UE) is connected.

ISBN 978-3-903176-28-7©2020 IFIP All of the modules of the infrastructure are implemented as services using Docker. We complement our prototype by using a stack to implement the extract-transform-load process, along to display and detect functionalities. This stack is composed of 4 modules: "Telegraf", "InfluxDB", "Chronograph" and "Kapacitor" (TICK) [START_REF] Influxdata | [END_REF]. We program the daemon of this TICK stack to extract C-RAN metrics in JSON format, and load them in a time-series database. Telegraf collects raw performance data from different sources. In particular, we interfaced it with FlexRAN, so that it parses encoded metrics, extracts pertaining counters, transforms it into normalized values and loads it into an InfluxDB time-series optimized database. We use Chronograph to display the metrics in a real-time updated dashboard. We use Kapacitor as data processing engine to stream-process received data and detect the anomalies on the fly and consequently, trigger configuration management commands to FlexRAN through our developed northbound interface. The Northbound application offers a user-friendly interface for configuration management of the FlexRAN through its RESTful API [2]. Our developed AAD sub-module is a script that can act as a simple comparison to a predefined threshold or as a T-Test with two-sample locationtest to validate the hypothesis that related two populations have equal means.

As hardware, we use a workstation with Ubuntu 16.04, kernel 4.15 having an Intel core i7-7820HQ CPU and 16 GB of memory to implement the EPC. This workstation is connected to the Internet on its Giga-Ethernet port serving as SGi interface of the EPC. We use a second interface for the S1 interface of RAN backhaul. For the C-RAN, including the FlexRAN controller, we use Ubuntu 16.04 machine with lowlatency kernel 3.19.0-61-low-latency SMP PREEMPT, running on a quad-core i7-8650U CPU and 16 GB of memory.

III. DEMO OVERVIEW

Our demonstration consists of the following steps. As an initial setup, we deploy our 5G prototype as a Mobile Virtual Network Operator (MVNO) from which any COTS UE in vicinity of our platform could detect its broadcast signal. With properly provisioned Subscriber Identity Modules (SIMs) in our Home Subscriber Server (HSS), multiple UEs can attach to our MVNO's Mobility Management Entity (MME), once we turn them on. These UEs establish a mobile Internet connection by activating a packet data protocol context through the Serving and Packet Gateways (SGW/PGW). Then, while streaming a video from YouTube, we move one UE to change the radio conditions in order to see the real-time update on our Chronograph dashboard. We display multiple RAN metrics such as the "Power Head Room", "Modulation and Coding Scheme", "Transport Block Size" in downlink/uplink and the number of Physical Resource Blocks (PRBs). Using our NBI interface to manage the FlexRAN controller, a new slice on the fly will be created with preset parameters in terms of allocated PRBs. In our experiments, we initially set 15% of allocated PRBs to such slice. Then, we manually change this percentage of allocated PRBs to 80% to see its effect on the download speed in one of our UEs, which increases approximately 5 times. We re-home a UE to the newly created slice with higher percentage of PRBs to observe the increase in download speed. Finally, we trigger an abrupt change in download by turning off the mobile data of one of the two UEs, camping on slice 2. Using the AAD sub-module, the stream-processing engine autonomously detects such change and takes the following planned actions: i) a message is instantly posted in a Slack channel, and, ii) as a corrective measure, the TICK script instructs the FlexRAN to execute our predefined configuration management commands. In occurrence, the issued command in this case is to release the resources of the unused slice and return them back to the pool of available resources. In case of two slices consuming the full pool, the released PRBs will be added to the remaining slice.

Fig. 1 .

 1 Fig. 1. 5G Prototype Service Oriented Architecture

ACKNOWLEDGEMENT

This work was supported by FUI SCORPION project (Grant no. 17/00464), CNRS PRESS project (Grant no. 239953), "Azm & Saade Foundation", and the Lebanese University.


Nazih Salhab
email: nazih.salhab@univ-eiffel.fr

Rana Rahim
email: rana.rahim@ul.edu.lb

Rami Langar
email: rami.langar@univ-eiffel.fr

Raouf Boutaba
email: rboutaba@uwaterloo.ca

Deep Neural Networks approach for Power Head-Room Predictions in 5G Networks and Beyond

Keywords: Deep Neural Networks, Machine Learning, Enhanced Interference Mitigation and Traffic Adaptation eIMTA, 5G, OpenAirInterface OAI, Power Head-Room prediction

The enhanced Interference Mitigation and Traffic Adaptation (eIMTA) mechanism is a key enabler for 5G networks and beyond. Knowing that a User Equipment (UE) cannot transmit more than the maximum power allowed by its power class. The level of available Transmission Power (TP) in each UE is an essential input for the Uplink (UL) scheduler of the next Generation Node-B (gNB). Scheduling higher data rate than what is supported by the available TP is a waste of resources. In the Downlink (DL), the power level is known by the gNB that manages the power amplifier and the DL-scheduler. Conversely, in the UL, the available power is estimated by the UE and sent to the gNB as a key input for eIMTA, known as Power Head-Room (PHR). In this context, we propose in this paper, a Deep Neural Network (DNN) based model to predict the PHR and reduce dependency on reported measures. We evaluate the effectiveness of our proposal in a 5G experimental prototype, based on Open Air Interface (OAI). Obtained results show that using DNN outperforms state-of-the-art machine-learning approaches in terms of prediction accuracy, computation complexity and throughput.

I. INTRODUCTION

In Long Term Evolution-Advanced (LTE-A) and fifth Generation of mobile communication (5G) New Radio (NR), Power Head-Room (PHR) is a type of Medium Access Layer (MAC) Control Element (CE). It reports the headroom between the current User Equipment (UE) Transmission Power (TP) and the nominal power. The Evolved Node-B (eNB) in LTE-A and next Generation Node-B (gNB) in 5G use this reported value to estimate the Uplink (UL) bandwidth that a UE can use for a specific subframe [START_REF] Kukushkin | The Road to 5G[END_REF]. The more resource blocks the UE uses, the higher UE TP gets, without exceeding the maximum power defined in the UE class specification. Consequently, a UE cannot use allocated resource blocks from assigned bandwidth if it does not have enough PHR.

The "enhanced Interference Mitigation and Traffic Adaptation" (eIMTA) allows for very dynamic adaptation of the Time Division Duplexing (TDD) pattern in response to capacity requirements in the UL and Downlink (DL). eIMTA was standardized in Third Generation Partnership Project (3GPP) Technical specification for LTE-A Release 12 [START_REF]User Equipment radio access, Rel. 12[END_REF]. "eIMTAlike" functionality is considered to be one of the key enablers for 5G technologies and beyond [START_REF] Pauli | Dynamic TDD for LTE-A and 5G[END_REF]. Even though the PHR is reported per subframe, the eIMTA time division duplexing has two sets of UL power control subframes configured. Therefore, PHR can also be very different between the two subframe sets [START_REF]Summary of discussion PHR for TDD eIMTA[END_REF]. According to [START_REF] Lohr | Power headroom reporting for non-scheduled uplink[END_REF][START_REF] Loehr | Efficient extended power headroom for semi-persistent scheduling[END_REF], a power headroom report can only be sent in sub-frames in which a UE has an UL transmission grant. Furthermore, the reported value corresponds to the subframe in which it is sent. Therefore, it is well-suited to predict the PHR value. Indeed, the UE cannot directly measure its actual transmission power headroom for the subframe in which the report is to be transmitted. In this context, we propose in this article to use deep neural networks (DNNs) in order to predict future PHR values in 5G. These DNNs are formed by the accumulated wealth of data in the gNB from previous measurements.

Our contributions can be summarized as follows:

• We provide an analytical PHR model to identify the predictors for a feature-based prediction. • We formulate different Machine Learning (ML) approaches to predict the PHR values and evaluate them. • We show the effectiveness of our proposal based on real measurement of PHR values using our 5G experimental prototype based on OpenAirInterface™ (OAI) [START_REF] Salhab | Machine learning based resource orchestration for 5g network slices[END_REF]. The remainder of this paper is organized as follows. In section II, we present the related works. Section III describes our system model followed by our Problem Formulation and complexity Analysis in Section IV. In section V, we present the performance evaluation and we discuss obtained results. We finally conclude this paper in section VI.

II. RELATED WORK

In this section, we discuss a selection of relevant papers that investigated PHR reporting for 5G networks and beyond.

Authors in [START_REF] Pauli | Dynamic TDD for LTE-A and 5G[END_REF] analyzed the main characteristics of eIMTA and illustrated its behavior using system-level simulations. The center for Advanced Technology in Telecommunications (CATT) along to multiple telecommunications suppliers [START_REF]Summary of discussion PHR for TDD eIMTA[END_REF] discussed the possibility of getting PHR for a set of two subframes. The goal is to use the current PHR mechanism in an eIMTA context. They concluded by emphasizing on the importance of having the eNB in control of individual PHR for each set.

Authors in [START_REF] Kim | Power headroom report-based uplink power control in 3gpp lte-a hetnet[END_REF] proposed a power-efficient resource allocation scheme using power headroom reports. They elaborated an adaptive Open-loop power control scheme based on the Signal-To-Interference-Ratio (SINR) and the UL interference in an ISBN 978-3-903176-28-7©2020 IFIP aim to improve cell capacity. They also detailed a fast closedloop power control based on received SINR.

Authors in [START_REF] Martikainen | Dynamic range aware LTE uplink P0 optimization in HetNet[END_REF] relied on PHR to design autonomous cellcentered self-optimizing-network. All of these papers exploited the PHR and did not address aforementioned eIMTA challenges using a proactive PHR computation approach.

Authors in [START_REF] Esswie | Inter-cell radio frame coordination scheme based on sliding codebook for 5g tdd systems[END_REF] proposed an inter-cell radio frame coordination scheme based on sliding codebook for fully dynamic TDD 5G networks. They formulated a two-objective optimization problem aimed at minimizing the average Inter-cell cross link interference while maximizing the achievable UL/DL capacity leveraging eIMTA for flexible TDD adaptation.

Authors in [START_REF] Yang | An Advanced Power Control Algorithm Based on PHR[END_REF] proposed an optimization of the Physical Uplink Shared Channel (PUSCH) closed loop power control algorithm based on PHR. They developed an algorithm that calculates the power of each resource block once PHR measurement is received by the eNB/gNB.

Authors in [START_REF] Turkka | Using LTE Power Headroom for Coverage[END_REF] investigated how to leverage PHR to detect outages in LTE network in an aim to minimize costly drive-test missions for mobile network operators. They used a dynamic LTE system simulator to validate their proposal. However, they did not leverage the advents in ML based prediction.

Authors in [START_REF] Bakri | Channel stability prediction to optimize signaling overhead in 5G networks[END_REF] proposed a prediction scheme for channel stability using machine learning for application in 5G networks.

Finally, authors in [START_REF] Jha | Dual connectivity in lte small cell[END_REF] investigated dual connectivity in LTE networks by allowing UEs to connect to multiple eNBs in an aim to improve user throughput and mobility. They emphasized on the resulting technical challenges including PHR calculation and reporting. They relied on a system level simulation study to quantify their analysis.

III. SYSTEM MODEL

PHR reporting provides the serving eNB/gNB with information about the difference between the nominal UE maximum transmit power and the estimated power for Uplink Shared Channel (UL-SCH) transmission for the serving cell.

According to the standard [START_REF]Medium Access Control Protocol, r15[END_REF], the UEs transmit buffer status and PHR reports on the PUSCH in the following four cases. The first case is when the path loss changes above a predefined threshold (dl -PathlossChange in dB) at each time window defined by prohibitPHR -Timer. The second case is at the expiration of a certain periodic timer (periodicPHR -Timer). The third case is when the configuration or reconfiguration of the PHR by the upper layers occurs. And finally, when a secondary servicing cell is activated.

Note that the PHR reporting range is from -23 to +40 dB [START_REF]Medium Access Control Protocol, r15[END_REF]. Positive values indicate that there is still some headroom under the maximum allowed power. It implies that the UE can transmit more data if required and if approved by the eNB/gNB UL scheduler. In contrast, negative power headroom values indicate that the per-carrier transmit power was limited at the time of the power headroom reporting. This meaning that the network has scheduled a higher data rate than the terminal can support, given the available transmission power. In such case, allocated resources are wasted.

In what follows, we provide the analytical model of PHR to identify the parameters that affect it for use in our ML engineering models.

A. Analytical Model for PHR

According to the standard on physical layer procedures [START_REF][END_REF], the UE computes its UL Transmission Power P TX as follows:

P TX = min P max , P 0 + 10log 10 (M ) + α.PL + ∆ MCS + f c
It is the minimum between required power and P max . On first hand, the maximum possible output power of the UE is fixed according to its category or class that defines its performance specifications. For example, P max is of 23, 20, or 14 dBm for UE class 3, 5, or 6, respectively [START_REF]UE radio transmission and reception, r15[END_REF]. On second hand, the required power is proportional to all of: P 0 (SINR per single Physical Resource Block (PRB)), the number of transmitting PRBs (M ), the estimated Path-loss (PL) weighted by a pre-configured fractional PL compensation factor (α), the adjustment factor for higher Modulation and Coding Schemes (∆ MCS ) and finally, the closed loop power-control adjustment factor (f c ). Parameter f c is the value commanded by the eNB/gNB to the UE to adjust its transmission power according to received signal strength and quality indicators at the eNB/gNB. We consider P 0 to be constant for an operator in homogeneous environment and f c ∼ 0 for a fine-tuned cell [START_REF][END_REF]. We assume parameter α to be constant per type of environment (urban, suburban or rural) as it relates to the UE used frequency band and the number of neighbor cells to a hosting cell, which is our case [START_REF][END_REF]. From the P TX , the PHR is calculated as PHR = Pmax -P TX [START_REF][END_REF], where Pmax is a computed transmission power set by the UE based on requirements elaborated in [START_REF]UE radio transmission and reception, r15[END_REF].

B. ML Models

We employ multiple ML models having different complexities to evaluate the prediction efficiency for the PHR. In addition, we used the neural networks-based models. We evaluated two exogenous models with selected features and two endogenous models with no required external feature engineering to be attached to the model.

Let D = {(X (1) , y (1) ), (X (2) , y (2) ), . . . , (X (N ) , y (N ) )} be a dataset of N records. X = (x 1 , x 2 , . . . , x d ) ∈ R d is a vector with d features used as predictors. Variable y is our response for a predicted PHR value.

1) Trees and Random-Forest based Models: A tree T is a form of a directed graph with a Vertex set (V) and Edge set E, such that E ⊆ V × V. It is a particular graph that has no simple cycles and is completely connected. Let T = (V, E), where each v ∈ V has up to two descendants {v l , v r } for left and right. For each vertex v ∈ V, we denote by val(v) a value of the vertex v, by idx(v), the feature index of vertex v, and by desc(v), the number of descendants of v. Thus, we define the vertex function f (v, x) as follows.

f (v, x) =      f (v l , x) , x idx(v) ≤ val(v) ∧ desc(v) = 2 f (v r , x) , x idx(v) > val(v) ∧ desc(v) = 2 val(v)
, otherwise

Operator ∧ denotes the logical "And" operator between the conditions. The growing function of a single tree T starting from vertex v 0 is T (x) = f (v 0 , x). In practice, regression trees are grown such that we have minimum squared error between the value y and the estimated value by the tree T (x). A random forest F is a set of M trees T = {T 1 , T 2 , . . . , T M }, where each tree T i is grown on a subset of the original dataset D used for the training of the ML model.

2) Neural Networks: Nonlinear Auto-Regressive (NAR) is a neural network based ML technique. It may be used with Exogenous input (NARX) or without relying on hand-crafted features and parameters. In this case, it is only based on previous values of the time series. NAR uses differentiable functions to model the system leveraging numerical optimization methods. When used without exogenous input, NAR might discover unknown high-level features from the data itself. It is a kind of dynamic filtering, in which past values of one or more time series are used to predict future values. These dynamic neural networks, which include tapped delay lines are widely used for nonlinear filtering and prediction. Provided d past values (delay parameter), on first hand, NARX predicts the series ỹt based on a series of features X t . On the other hand, NAR predicts series values ỹt given only d past values of ỹt or y t , in an open-loop or closed-loop scenario, respectively.

Recursive Neural Network (RNN) is a form of DNN that is well-suited for time-series prediction. Due to its recurrent structure, a cell state variable c t is updated according to current input i t and previous state c t-1 . Given a time series sequence x = (x 1 , x 2 , . . . , x t ), the joint probability is given by: p(x 1 , x 2 , . . . , x t ) = p(x 1 ).p(x 2 |x 1 ) . . . .p(x t |x 1 , . . . , x t-1 ). The hidden layer of the RNN model provides an output which is h t ∼ p(x t |x 1 , . . . , x t-1 ). RNNs are trained using stochastic gradient descent or its similar variants. Long Short Term Memory (LSTM) was proposed [START_REF] Hochreiter | Long short-term memory[END_REF] to avoid having vanishing or exploding gradient. Differently from the RNN, where a repeating module has a single hyperbolic tangent tanh layer, LSTM has four layers (forget f t , input i t , candidate ĉt , and output o t ). They are used to calculate the new cell state (c t ) and the new hidden output (h t ). Each layer uses an activation function that is either the logistic sigmoid (σ) or tanh of the weighted entry (w) topped by a bias (b). Thus, it is capable of learning Long-term dependencies using Short-Term Memory. The LSTM unit is depicted in Fig. 1 and formulated as follows.

f t = σ(w f [h t-1 , x t ] + b f ) (1a) i t = σ(w i [h t-1 , x t ] + b i ) (1b) ĉt = tanh(w c [h t-1 , x t ] + b c ) (1c) o t = σ(w o [h t-1 , x t ] + b o ) (1d) c t = f t .c t-1 + i t .ĉ t (1e) h t = o t . tanh(c t ) (1f)

IV. PROBLEM FORMULATION AND COMPLEXITY ANALYSIS

In this section, we formulate the problem of predicting PHR values as an optimization problem and analyze the complexity of each of the studied ML models. Recall that our goal is to estimate a function ỹ = β, X such that (ỹ ≈ y) where the regularized error is minimum. Using Ridge regression min

β∈R d Z = λ β 2 + 1 N N i=1 (ỹ i -y i ) 2 (2)
where β ∈ R d is the regression parameter vector and λ ∈ R is a scalar allowing to control the complexity of the forecast according to the chosen set of parameters. We notice that for λ = 0, the effect of the parameters is neglected and only the Mean Squared Error (MSE) is considered (the second term), while for high values of λ, the null β leads us to our goal.

Let us now assess the complexity of each of the studied models. Starting from ML-based regressions, and denoting by Y all the y (i) , i = 1..N , the computation of β is about completing the following matrix operation (X T X) -1 X T Y . Thus, the time complexity is: O(N d 2 + d 3 ), where N d 2 results from the product X T X and the d 3 results from the matrix inversion of X T X. As for the space complexity, it is O(N d + d 2 ), as storing X T X and its inverse is O(d 2 ) and storing X costs O(N d) floats.

As for the tree-based regressions: we denote by D the depth of the deepest tree in the forest F. Knowing that the number of nodes in a binary tree is (2 D+1 -1), we can say that the forest F has a worst space complexity of O(2 D M ). Access time complexity is O(log(n)), where n is the number of nodes.

Finally, for the Neural Networks (NN) complexity, recall first that the computation of NN consists of two parts: a forward and a backward pass. In the forward pass, the computation consists of additions, subtraction and multiplication or memory operations. In the backward pass, there is a need to compute the difference between the output and target value, and propagate the gradient all the way back to the beginning. Working on N rows, a matrix multiplication has a asymptotic run-time of O(N 3 ). Denoting by n layers the number of layers, the time complexity in the forward pass is O(N 3 .n layers ). The complexity of activation operations is O(N.n layers ). Knowing that ∀N ≥ 1, N 3 + N ≤ 2N 3 , we simplify the summation (N 3 + N ).n layers ) to conclude that the total run-time complexity in the forward pass is O(N 3 .n layers ). Similarly, the time complexity of the backward pass is O(N 3 .n layers .n gi ), where n gi is the count of gradient iterations.

V. PERFORMANCE EVALUATION

In this section, we evaluate the performance of our proposed models using an OAI-based [START_REF]OpenAirInterface (OAI[END_REF] 5G experimental prototype. We present our methodology to generate the dataset used in our simulations, followed by a presentation of the results.

A. Dataset Generation and Simulation Environment

Using our 5G prototype [START_REF] Salhab | Machine learning based resource orchestration for 5g network slices[END_REF], a test dataset has been generated as follows. During simulation time (Sim. time), Metrics, collected every second from FlexRAN [START_REF]OpenAirInterface (OAI[END_REF], include configuration data of the two UEs and the hosting cell/slice parameters in addition to performance data of the MAC layer. We extracted a subset of the collected data, including all of: PHR, Channel Quality Indicator (CQI), MCS, number of PRBs and Transport Block Size (TBS). The ML models are implemented in MATLAB [START_REF]Matrix Laboratory: MATLAB[END_REF].

B. Simulation Scenarios

Fig. 2 depicts the features importance in terms of the predictor variables. In order to cater different prediction models including offline and online scenarios, we identified three features subsets as follows. 1) Full set of Features (FF) in which the prediction process depends on all features. 2) Reduced set of Features (RF), where the prediction process depends only on the two most important features. And 3) No Features set (NF) where the model is independent from any engineered feature and just relies on previous observed values. We note that for ML-based regressions, and in particular the tree-based ones, the algorithm, proposed in our earlier work [START_REF] Salhab | Machine learning based resource orchestration for 5g network slices[END_REF] tends to split predictors with many unique values (levels). It prioritizes continuous variables, over discrete ones with fewer levels as depicted in Fig. 2a. As our data is heterogeneous, we considered using the curvature, known as interaction tests for split-predictor selection. To decrease the features from our dataset, we compute the predictors importance values by permuting Out-Of-Bag (OOB) observations among the trees and summing gains in the MSE due to splits on each predictor, as depicted in Fig. 2b. Since prediction time increases with the number of predictors in trees and forests, we created a model using as few predictors as possible. Thus, we evaluated the performance of Random forests using the best two predictors only (RF) and the neural networks with NF. We considered the neural networks with NF and we evaluated the (NAR) with 3 delays using multiple training methods. Note that the delay number is equal to the number of stored previous values of y. Finally, we considered the RNN LSTM using multiple optimizers. We evaluate the Prediction accuracy based on the MSE, Root-Mean Squared Error (RMSE), Mean Absolute Error (MAE), coefficient of determination R-squared (R 2 ) that are all centered around calculating the gap between the predicted value and the observed one. Unlike the MAE that treats all errors uniformly, the RMSE stresses on large prediction errors and de-stresses on small ones. Finally, the R 2 measures the proximity of data to the fitted regression line.

C. Simulation Results

In order to validate our predicted output with the FF, we used k-fold cross-validation that consists of partitioning the original sample of data into k equal sized sub-samples. A single sub-sample is retained as the validation data for testing the model, and the remaining (k -1) sub-samples are used as training data. The cross-validation process is then repeated k times, with each of the k sub-samples used exactly once as the validation data. The k results are then averaged to produce a single estimation. The advantage of this method over repeated random sub-sampling is that all observations are used for both training and validation, and each observation is used for validation exactly once.

For single trees, we considered different values q of minimum leaf size to denote the type of the tree (Fine, Medium or Coarse). As for the ensemble of trees (Boosted or Bagged), we used a number of learner NL and a learning rate LR. For Gaussian Support Vector Machine (SVM), we used different kernel scales (ker) according to its type (Fine, Medium or Coarse). For LSTM, we considered a number of hidden units (N h ), Number of Epochs (N epoch ), Gradient Threshold (GT ), Initial Learning Rate (ILR), Learning Rate Drop Period (LRDP), and Learning Rate Drop Factor (LRDF). Simulation parameters are listed in Table I.

Tables II and III report the RMSE, R 2 and MAE performance metrics for the used prediction models. We can see LSTM are efficient at extracting patterns in input feature space that spans over long sequences, as it leverages its memory state to predict, which is ideal for our recurrent PHR prediction. Fig. 3 depicts the observed PHR using OAI and predicted PHR using LSTM for 682 seconds (∼10 minutes). Visually, inspecting the trends, we can see that the evolution of both graphs are very close except when it comes to sudden changes shown in high peaks or drops triggered by external environmental factors. This is clearly seen in the bottom subplot showing that the instant value of the error with less than 10 units in magnitude, during these first ∼10 minutes.

In addition, we can observe in Fig. 4a that the RMSE is decreasing over time, as a result of continuous improvement on the long run, which proves its suitability for application. Indeed, when additional training data are accumulated, the prediction accuracy will be enhanced.

Finally, we plot in Fig. 4b the PUSCH throughput versus the Received Signal Reference Power (RSRP). We can see that the RSRP value of -100 dBm acts as an inflection level separating two zones of throughput value. The throughput is enhanced with prediction as a consequence of an early awareness by the eNB/gNB of required PRBs corresponding to the UE output power. We can explain this behavior as follows. When the RSRP is less than -100 dBm (poor radio conditions), the transmitted power of the UE is increased. This causes higher interference and thus the number of PRBs is decreased, limiting the data rate. Conversely, for higher values of RSRP (above -100 dBm), the throughput is increased till reaching a saturation level as it is affected by the MCS and the buffering of the operating system. All in all, we can conclude that the prediction of the PHR helps in increasing the throughput and reducing the probability of wasted resources.

VI. CONCLUSION

In this paper, we have presented several ML-based approaches to predict the power headroom, for an enhanced Interference Mitigation and Traffic Adaptation (eIMTA) application. We formulated Machine Learning Feature-based prediction, in addition to neural networks approaches with and without features. We also considered a reduced set of features and found that it results in minor deteriorated performance. Particularly, we have observed that deep neural networks, Long Short Term Memory (LSTM) requiring no features, outperforms other ML models in terms of prediction accuracy and feature independence. Implementing the LSTM in our 5G prototype using OpenAirInterface (OAI), we observed that the Root Mean Square Error between real and predicted PHR decreases with time and the throughput is enhanced.

Fig. 1 :

 1 Fig. 1: Long Short Term Memory Modular Diagram

Fig. 2 :

 2 Fig. 2: Deducing Reduced Features out of Full Features

Fig. 4 :

 4 Fig. 4: RMSE convergence with DNN and impact on the Throughput

TABLE I :

 I Simulation Parameters and Values

	Parameter	Value	Parameter	Value
	k		10	NL	30
	q (Fine)	4	ker (Fine)	0.71
	q (Medium)	12	ker (Medium) 2.8
	q (Coarse)	36	ker (Coarse)	11
	q (Ensemble) 8	LR	0.1
	d		2 3	ILR	0.005
	N h		200	LRDP	125
	N epoch	30	LRDF	0.2
	GT		8	Sim. time (s)	37073
	TABLE II: Full Features (FF) Simulation Results
	Class	Forecast		RMSE R 2	MAE
	Linear	Linear		5.2874	0.59	4.0795
		Interaction Linear	5.0786	0.62	3.8216
		Robust Linear	5.3929	0.57	3.989
		Stepwise Linear	5.0209	0.63	3.8134
	Tree	Fine		4.3661	0.72	30953
		Medium		4.4468	0.72	3.1601
		Coarse		4.4964	0.7	3.227
	SVM	Linear		5.4060	0.57	3.9732
		Quadratic		5.0878	0.62	3.6559
		Cubic		16.438	0.297 9.7541
		Fine Gaussian	4.7019	0.68	3.3028
		Medium Gaussian	4.8889	0.65	3.5253
		Coarse Gaussian	5.3101	0.59	3.975
	Gaussian	Rational Quadratic	4.3581	0.72	3.1127
	Process	Square Exponential	4.4555	0.71	3.1712
		Matern 5/2		4.4401	0.71	3.1657
		Exponential		4.3712	0.72	3.1203
	Ensemble	Boosted Trees	4.6415	0.68	3.5402
	of Trees	Bagged Trees	5.3909	0.57	4.1355
	Random	Full Features (FF)	4.3293	0.73	3.0778
	Forests	Reduced Features (RF)	5.0287	0.63	3.8435
	NARX	Levenberg-Marquardt	5.86	0.9	4.941
	3 delays	Bayesian Regulation	5.8934	0.9	4.672
		Scaled Conjugate Gradient 6.5338	0.9	4.651

TABLE III :

 III No Features (NF) Simulation Results

	Class	Forecast		RMSE R 2	MAE
	NAR	Levenberg-Marquardt 5.8700	0.90 4.198
	3 delays	Bayesian Regulation	4.2000	0.89 4.489
					Scaled Conjugate Gr.	6.6500	0.88 4.91
	RNN	ADAM Optimizer	2.9652	0.69 2.0538
	LSTM	SGDM Optimizer	3.1063	0.63 2.2143
					RMSProp Optimizer	3.0281	0.66 2.1658
		40
	PHR	20
				Observed
		0	0	100 Predicted	200	300	400	500	600	700
							RMSE = 2.9652
		10
	Error	0
	-10
	-20	0	100	200	300	400	500	600	700
							Time (s)
	Fig. 3: Observed vs Predicted and Error Comparison over time
	that LSTM using ADAM optimizer provides the lowest RMSE.

ACKNOWLEDGEMENT

This work was partially supported by FUI SCORPION project (Grant no. 17/00464), CNRS PRESS project (Grant no. 239953), "Azm & Saade", and the Lebanese University.


Aïcha Xiaowen Liang

Ben Zemzem

Sébastien Almagro

Jean-Charles Boisson

Angelo Luiz

Timm Steffenel

Laurent Weitkamp

Nicolas Debelle

Passat

Steffenel

Mouse arterial wall imaging and analysis from synchrotron X-ray microtomography

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

MOUSE ARTERIAL WALL IMAGING AND ANALYSIS FROM SYNCHROTRON X-RAY MICROTOMOGRAPHY

Xiaowen Liang 1,2 , Aïcha Ben Zemzem 2 , Sébastien Almagro 2 , Jean-Charles Boisson 3 , Luiz-Angelo Steffenel 3 , Timm Weitkamp 4 , Laurent Debelle 2 , Nicolas PASSAT 1 1 -Université de Reims Champagne-Ardenne, CReSTIC EA 3804, 51100 Reims, France 2 -Université de Reims Champagne-Ardenne, CNRS, MEDyC UMR 7369, 51100 Reims, France 3 -Université de Reims Champagne-Ardenne, LICIIS / LRC CEA DIGIT, 51100 Reims, France 4 -Synchrotron SOLEIL, 91192 Gif-sur-Yvette, France

Context

Vascular ageing is characterized by profound modifications of the large elastic arteries (mainly aorta), leading to consequences such as aneurysm formation. Age-related diseases can be precipitated by metabolic disorders, e.g. diabetes or chronic kidney diseases. Vascular diseases involve morphological changes of the arterial extracellular matrix, particularly remodelling of the elastic lamellae found in the wall. Synchrotron X-ray microtomography (µCT) gives access to images with a micrometric resolution. In the context of vascular imaging, this allows the study of structural properties of arterial walls, even for small animals such as the mouse.

A methodological pipeline

Preprocessing: to remove the noise and artifacts.

Main step 1:

-Segmentation of the lumen -Computation of the normal field and distance maps with respect to the lumen. Finding the contour of the lumen, which is the inner contour of the arterial wall.

Computation of a distance map that shows the distance of a pixel to the lumen, in order to calculate a "contour line" at any distance.

Calculating the normal vector at any point using "contour lines". Training on 1457 2D images that generate 47118 patches (20% for validation)

: Deep learning-based classification

Results

Classification at the pixel level by comparison between sub-patches.

The Euclidean distance between vector0 and vector2 is smaller than the one between vector0 and vector1, so the corresponding pixel is classified in the region of the surrounding tissues (the other region is that of the elastic lamellae).

Segmentation on a whole image: extraction of the internal contour by the lumen segmentation + classification of the pixels at the border by the Siamese network.

Dice score = 0,926 (Ground truth obtained by manual annotation)

Conclusion

Chronic cardiovascular diseases are often discovered when irreversible damage has already occurred, early identification of the very first stages of these diseases is therefore extremely important. Synchrotron imaging has shown its power to detect these very localized events with its high resolution. The proposed segmentation pipeline is a first step towards that goal, mixes conventional image processing and deep learning paradigms, and provides promising results.

The next steps of that work will consist of improving the lumen segmentation step by reducing the parameter space to be handled and of exploring more thoroughly the deep learning part of the process.

  Main step 2: -Deep learning-based texture classification. 1 : Segmentation of the lumen & computation of the distance maps Segmentation of the lumen based on a co-occurrence texture analysis.

  Rotating and cutting patches according to the calculated normal vectors.Training a Siamese network to distinguish dissimilar sub-patches.