IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, VOL 50, NO 4, NOVEMBER 2008 861 Impulsive Noise Characterization of In Vehicle Power Line Virginie Degardin, Martine Lienard, Pierre Degauque, Member, IEEE, Eric Simon, and Pierre Laly Abstract—Impulsive noise can have a great influence on the per formance of in vehicle power line communication systems Inten sive noise measurements in the time domain were thus carried out on five different vehicles Preliminary trials were first made on a statio.
IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, VOL 50, NO 4, NOVEMBER 2008 861 Impulsive Noise Characterization of In-Vehicle Power Line Virginie Degardin, Martine Lienard, Pierre Degauque, Member, IEEE, Eric Simon, and Pierre Laly Abstract—Impulsive noise can have a great influence on the performance of in-vehicle power line communication systems Intensive noise measurements in the time domain were thus carried out on five different vehicles Preliminary trials were first made on a stationary vehicle and the motor idling, but the characteristics of the measured low-amplitude pulses greatly vary from one car to another We thus emphasize the characteristics of high-amplitude pulses, greater than 70 mV, observed when the vehicles were moving in traffic, during a 20-min trip Noise is statistically characterized in terms of duration, frequency content, peak amplitude, and time interval between successive pulses Stochastic models based on mathematical distribution functions and fitting the experimental distribution of the various pulse characteristics are proposed It has been found that interarrival time, i.e., the time interval between two successive pulses, is rather short and would be thus the most critical parameter when optimizing the power line communication physical layer Index Terms—Impulsive noise, in-vehicle power line communication, wire communication I INTRODUCTION VER the last few decades, electronic systems have been used more and more in vehicles to insure the safety and comfort of the occupants These include both safety systems, such as antilock brake system (ABS), electronic stability program (ESP), and electromechanical brake-by-wire (EMB), and comfort systems, such as adaptive cruise control (ACC), as well as numerous multimedia systems providing automotive multimedia and personal computer networking In most cases, automobile manufacturers have chosen to transmit data from sensors to computers via dedicated communications networks, using standardized protocols like controller area network (CAN), local interconnect network (LIN), media-oriented systems transport (MOST), or FLEXRAY Currently, twisted wires or fiber optics cables are used for transmitting safety-related signals This practice has led to increasingly complex network architectures, with the result of increasing the weight of the cable harness and the number of connections, making it harder to insure the reliability of the combined systems One possible medium-term solution is to use the dc power O Manuscript received January 3, 2008; revised June 30, 2008 Current version published November 20, 2008 This work was supported in part by the French Ministry of Research, by the Region Nord Pas de Calais, and by the FEDER funds, conducted in collaboration with PSA Peugeot Citroen, Valeo, and Institut National des Sciences Appliquees de Rennes (INSA-Rennes) The authors are with the Institute of Electronics, Microelectronics and Nanotechnology (IEMN)/Telecommunications, Interference and Electromagnetic Compatibility (TELICE), University of Lille, 59655 Villeneuve d’Ascq, France (e-mail: virginie.degardin@univ-lille1.fr; martine.lienard@univ-lille1.fr; pierre.degauque@univ-lille1.fr; eric.simon@univ-lille1.fr; pierre.laly@univlille1.fr) Digital Object Identifier 10.1109/TEMC.2008.2006851 network as the physical support for transmission [1]–[6] This method, often called power line communication, is currently being developed primarily for use in indoor applications to allow, for example, an Internet connection via any home electrical outlet The principal difficulties encountered when developing such power line carrier (PLC) systems are mostly related to the channel transfer function, which is highly variable both in terms of frequency and time [7], and to the impulsive noise generated by the various electrical devices connected to the network In fact, unlike transmissions over coaxial and/or twisted lines, the disturbing currents and voltages that propagate through a power network are superposed directly over the useful signal Knowledge of the noise characteristics is thus essential for optimizing modulation schemes and channel coding (e.g., error-correcting codes, interleaving, and equalization algorithms), and also for predicting link performance In the automobile domain, electromagnetic compatibility (EMC) standards have already been established, but they are defined either by car manufacturers for each individual piece of equipment or by international normalization bodies that impose a maximum level for the electromagnetic field radiated by a vehicle at a given distance The measurement procedures are, of course, clearly explained in these standards, but these procedures are primarily defined in the frequency domain This can be problematic for studying digital telecommunication performance In the time domain, only a few studies have been published For example, the process for characterizing transient voltages for automotive 42-V power systems is described in [8], with an emphasis on the effects of the relay arcing process, used mainly to deal with EMC problems A few measurements of channel transfer functions and a description of several noise pulses are provided in [9], together with a model of the interarrival time (IAT) distribution In an attempt to overcome this lack of data in the literature, we present in this paper an exhaustive study of impulsive noise, exploring a frequency bandwidth ranging from 500 kHz to 40 MHz, which corresponds to a possible bandwidth for future automotive PLC systems [3], [5], [10], [11] It must be emphasized that we not seek to identify and characterize each disturbing source Preliminary measurements on one car, of the channel transfer function and the impulsive noise at one point, are described in [12], while in [13], four measurement points in the same car were considered To make a statistical approach of the impulsive noise characteristics, these previous studies have been extended by making extensive noise measurements, taken at various connection points in five new upmarket cars First, Section II presents the 0018-9375/$25.00 © 2008 IEEE 862 IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, VOL 50, NO 4, NOVEMBER 2008 principles behind the measurement techniques To make measurement in realistic conditions, the first idea was to extract, in a car moving in traffic, all types of impulsive noise whose amplitudes exceed a given threshold, for example, much smaller than the average amplitude of the useful PLC signal However, preliminary studies have shown that, in this case, thousands of pulses are recorded in less than s, and that these low-amplitude pulses are also present when the vehicle was stationary It thus appeared that it would be more interesting to divide the experiments in two steps: measurements in a car in stationary conditions and by using a small triggering threshold for the acquisition system, and in a car in driving condition using a threshold higher than the peak amplitude of the pulses already recorded Few results are given in Section III for stationary conditions showing that pulses are mainly due to interference sources rather than noise randomly generated by electronic equipment Furthermore, the pulse characteristics are often quite different from one car to another Additional measurements on a larger number of cars would thus be needed to have an idea of the spread of the pulse characteristics We thus focus our attention to pulses recorded in driving conditions, under the constraint on high triggering threshold Section IV describes the results of the analysis of the distribution of the peak amplitude, pseudofrequency, duration, and IAT of the pulses Probability density functions fitting the experimental data are also proposed We conclude this paper by a short comparison between in-house and in-vehicle noise characteristics, since actual PLC modems have been primarily optimized for in-house communications II IMPULSIVE NOISE MEASUREMENT SYSTEM Before describing the measurement techniques and how the impulsive noise was extracted from the ambient noise and characterized statistically, it is important to briefly review the main features of PLC communication systems in order to highlight the importance of characterizing noise in the time domain as well as in the frequency domain A Main Features of PLC Communication Systems The cable harness tree-shaped bundle configuration is composed of numerous interconnected multiconductor transmission lines During data transmission, reflections occur at the different junctions and on the unmatched terminal equipment Orthogonal frequency division multiplexing (OFDM) is usually chosen to cope with the effects of such multipath propagation, though code division multiple access (CDMA) has also been studied [14] To give some figures for a very simple example, a usual HomePlug 1.0 scheme for a bit rate of 14 Mb/s is based on a maximum OFDM symbol (21 B) duration of 8.4 µs [15] Each maximum code word consists of 20 symbols, plus additional bytes associated with a Reed–Solomon (RS) and convolutional concatenated code The duration of a PHY block of 20 symbols is 168 µs Depending on the characteristics of the impulsive noise, such as pulsewidth and pulse spacing, and thus on the number of pulses occurring during a code word, the RS code may or may not be able to correct the errors Interleaving the code words is thus often used, but to optimize the interleaving depth, Fig Typical noise recording compared to an OFDM signal with a PSD of –60 dBm/Hz the statistical properties of the impulsive noise and especially the IAT must also be known Another important parameter that plays a leading role in communication performance is, of course, the power level of the injected signal Fig superposes the impulsive noise measured in a vehicle (method to be explained later) with an OFDM signal with a power spectral density (PSD) of –60 dBm/Hz This PSD value seems to be the value that will most likely be authorized by international normalization bodies for indoor PLC applications As shown in Fig 1, the signal and peak values of the disturbing pulses have the same order of magnitude, equal to about 500 mV It must be emphasized that, due to EMC constraints, one cannot arbitrarily increase the transmitting power The EMC specifications can be defined by the car manufacturer as well as by international standards For example, International Special Committee on Radio Interference (CISPR) 25 (electromagnetic disturbances related to the electric/electronic equipment in vehicles) [16] specifies that to obtain acceptable radio reception in a vehicle using typical radio receivers, the disturbing voltage at the end of the antenna cable should not exceed a limit on the order of dB·µV The choice of the maximum PSD is out of the scope of this paper, but this example underlines that an in-depth knowledge of impulsive noise statistics is necessary for studying PLC communication performance B Measurement Setup A test platform [see Fig 2(a)] was developed to measure the impulsive noise in both stationary vehicles and vehicles moving in traffic As illustrated in Fig 2(b), the disturbing voltage between the power line and the ground (the coachwork or a ground wire) was measured through a capacitive coupling composed of a high-pass filter with a cutoff frequency of 500 kHz and an impedance matching RC network It is followed by a transformer to isolate the output signal from the power supply and by a protecting device, limiting the peak amplitude to 3.5 V The total insertion loss of these devices is dB The signal is then amplified and low-pass filtered, and the cutoff frequency of the filter being 40 MHz The main characteristics of the coupler are total power gain: 6.5 dB, gain flatness: dB, bandwidth: 0.5–40 MHz, DEGARDIN et al.: IMPULSIVE NOISE CHARACTERIZATION OF IN-VEHICLE POWER LINE Fig 863 (a) Measurement setup (b) Block diagram of the coupling device input voltage standing wave ratio (VSWR) maximum in the dB bandwidth: 1.5, and output VSWR maximum: 1.2 One of the problems in measuring impulsive noise is distinguishing this kind of noise from the background noise For wireless communications, a procedure proposed in [17] was to capture data in both horizontal and vertical polarization simultaneously Unfortunately, in our application, the only possibility is to adjust a threshold level A spectrum analyzer was thus used to measure this background noise In the frequency band from 500 kHz to MHz, the noise spectral density was shown to decrease with frequency from –110 to –130 dBm/Hz, and then to remain constant between and 40 MHz Given the 40-MHz bandwidth of the acquisition system, a triggering threshold varying between few millivolts and 200 mV was chosen, depending on the configuration of the measurements, as it will be explained in Sections III and IV The acquisition time (i.e., the width of the observation window) after triggering was 650 µs, the signal being sampled at a frequency of 100 MHz before it is stored At the end of an observation window, a clock with a time resolution of 400 ns was activated to measure the time intervals separating two successive recordings in order to determine the IAT between two successive pulses, even if the pulses are not in the same observation window [see Fig 2(a)] The minimum time between two successive recordings, i.e., before the acquisition system can be triggered again, is 250 ns and is thus negligible compared to the average time between two successive pulses C Detection of the Impulsive Noise and Examples of Recorded Pulses Data processing was necessary to extract the different pulses occurring during an observation window, in order to analyze their properties in both the time and frequency domains To avoid ambiguities due to the presence of background noise, a technique based on probabilistic criterion and using a decision level is described in [18] Once the decision level is fixed, the pulse start and end points are determined where the measured signal crosses the decision level with positive and negative slopes, respectively However, applying such a technique to extract pulses having the shape of a damped sinusoid, as we will see in a next paragraph, seems quite difficult We have thus preferred to develop another approach, consisting of calculating the cumulative variance y(k) of the sampled signal during the time k∆t (denoted hereafter as k), ∆t being the sampling period If Fig (a) Example of a noise recording (b) Cumulative variance during an observation window xi is the signal amplitude sampled at the instant i, y(k) is the result of k −1 n −1 (xi )2 − (1) xi y (k) = n i=0 i=0 where n is the total number of samples during an observation window In the presence of background noise, the cumulative variance is a linear function of k, and thus a break in the line indicates the presence of a pulse The duration of a pulse or a burst corresponds to the time interval during which the slope of y(k) is significantly different than the slope of background noise only The first two steps of this calculation are illustrated in Fig The upper curve shows a recording for one observation window; the abrupt change of the y(k) slope on the lower curve clearly shows the instants when a transient phenomenon begins and stops An example of the pulses during one observation window is given in Fig 4, highlighting the damped sinusoidal form of the pulses, which was found in almost all of the recordings To classify the pulses, two categories—single pulse and burst— were first considered In general, a burst is defined as a sequence of distinct pulses but experimental results show that in most cases, a burst is formed by only two successive pulses, as shown in Fig 4(b) Considering all the observation windows, one can determine the percentage of either single pulses or bursts, and thus their probability of occurrence Since the approximate pulse shapes are damped sinusoids, a pseudofrequency Fps can be defined as the frequency at which the PSD reaches a maximum value Consequently, the analysis can turn to the statistical distributions of the following characteristics: peak amplitude, pseudofrequency, duration, and IAT D Description of the Successive Measurements To draw pertinent conclusions from the study of impulsive noise, noise recordings from a certain number of vehicles are 864 Fig IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, VOL 50, NO 4, NOVEMBER 2008 Typical noise recording (a) Single pulse (b) Burst needed, since the noise characteristics are likely to vary with the car brand and model We thus chose to take measurements on a group of five upmarket cars from different manufacturers in different countries Two of these cars, henceforth denoted as CarG1 and CarG2, had gas engines; the other three—CarD3, CarD4, and CarD5—were diesels The horsepower of the different motors ranged from 150 to 210 hp Furthermore, in each vehicle, the measurements were taken at a variety of different points, including the cigarette lighter, the 12-V outlet, and the on-board computer power supply point Indeed, one can expect that the noise also depends on the electronic equipment connected in the vicinity of the measurement point and on the harness architecture Since the length of the cables and the possible source of disturbances in the vicinity of these points are quite different from one another, one can hope that the measurement locations represent a true picture of what happens in the network For the different cars, the typical number of measurement points is because, unfortunately, the access points on the power network in new cars are quite limited Preliminary measurements have shown that thousands of pulses were stored in less than s for a triggering level on the order of few tens of millivolts, depending on the car Since such a high number of events also occurred while the vehicle was stationary and the motor idling, we have divided the study into two parts, dealing with static and dynamic conditions, respectively In the static conditions, few results are given in Section III In the sequel, pulses characteristics in driving conditions are emphasized In this case, the triggering level is chosen high enough to be greater than the average peak value of the pulses recorded in static conditions For these trials, the measurements were taken during a 20-min trip in urban and suburban driving conditions in dry or rainy weather, with certain systems (e.g., electric windows, fans, windshield wipers, and headlights) activated under normal use conditions About 2000 observation windows were recorded at each measurement point on the dc line while the vehicles were moving, leading Fig Recording of impulsive noise in static conditions Fig Probability density of the characteristic parameters of noise in static conditions IAT distribution is given within a pulse sequence to about 104 observation windows per car The main results are presented in Section IV III IMPULSIVE NOISE IN STATIC CONDITIONS In the stationary vehicle with an idling motor, impulsive noise was detected at the measurement points close to the on-board computer power supply point An example of a pulse sequence (i.e., a series of single pulses), measured on CarD5, can be seen on the recording shown in Fig The various recordings show that the length of the pulse sequences may vary between 100 µs and ms The probability densities of the characteristic parameters of the impulsive noise shown in Fig are given in Fig The curves show that each single pulse has duration between 0.5 and µs, and a pseudofrequency of 15 and 22 MHz for CarD4 and MHz for CarD5 For this very specific case of DEGARDIN et al.: IMPULSIVE NOISE CHARACTERIZATION OF IN-VEHICLE POWER LINE impulsive noise observed under static conditions, we distinguished two distinct series of IAT; the first involved IAT within a pulse sequence and the second involved IAT between two sequences The statistical study of the IAT between sequences indicated that their average value is 10 ms The various recordings show that the length of the pulse sequences may vary between 100 µs and ms The IAT within a sequence being smaller than 100 µs, we concentrated on the distribution in that time interval, since it has about the same duration as a code word As can be seen in Fig 5, the series of pulses for CarD5 is quasi-periodic, with the probability density function presenting a sharp maximum for an IAT of 50 µs (see Fig 6) For CarD4, the most probable IAT is 30 µs, but the distribution function is more spread out, extending from 25 to 50 µs The IAT distribution presenting narrow peaks and the pseudofrequency being clearly identified, this suggests the presence of an interference source rather than a random noise source This could be, among other possible sources, either a clock or impulse currents generated by modules used for the local control and diagnosis of electrical devices as well as to register and display operating elements situated some distance away On other recordings, data packets were clearly identified but with a much lower peak amplitude However, since vehicle communication systems use proprietary communication protocols and since the access points for the measurements in the vehicles were quite limited, it was impossible to identify these sources of interferences As it appeared from measurements made on the five cars during our experiments, the characteristics of these interferences are quite different from one car to another It would be necessary to make additional measurements on a much larger number of cars to have an idea of the spread of the low-amplitude pulse characteristics IV STATISTICAL PROPERTIES OF IMPULSIVE NOISE IN DYNAMIC CONDITIONS A Introduction This section presents the statistical properties of all the pertinent parameters of the single pulses and bursts recorded when the vehicles are moving The triggering threshold was increased to 70 mV to avoid triggering on the numerous pulses also occurring in stationary conditions, and even to 200 mV for cars CarD4 and CarD5 The noise statistics presented in this section will be, of course, only valid under these triggering conditions The recordings show that the burst occurrence probability is 4% and 6% for cars CarG2 and CarD4, respectively, 16% for cars CarG1 and CarD5, and lastly, 26% for CarD3 This probability is thus much lower than the one for single pulses Based on the results obtained for CarG2, Fig 7(a) and (b) represents, respectively, the scatter plots of peak amplitude versus pseudofrequency and the scatter plots of duration versus pseudofrequency (single pulses and bursts are differentiated) In these figures, the pseudofrequencies of the pulses, whether single pulses or bursts, are spread out over the entire PLC bandwidth, from several hundred kilohertz to 40 MHz Fig also highlights the similarity of the characteristics of single pulses and bursts with respect to their pseudofrequency or 865 Fig Scatter plot (a) Peak amplitude versus pseudofrequency plane (b) Duration versus pseudofrequency plane duration Since the same result was obtained for the other cars, in the following discussion, no additional distinction will be made between single pulses and bursts We analyzed the pulse characteristics for each car statistically in order to identify the possible dispersion between the cars Depending on the intervals of the variable, we wanted to analyze if either the cumulative distribution function (cdf) or the complementary cdf (ccdf) Future PLC systems will employ some adaptive coding/modulation technique, and statistics based on a per-car basis is interesting However, for the time being, such PLC modems are not yet commercially available, and, in addition, low cost is an important constraint when developing new automotive devices We have thus preferred to perform a global analysis, integrating all the data from all five vehicles in order to determine the average statistical distributions We then tried to find simple distribution functions based on well-known mathematical expressions, which would fit the experimental distribution These functions will allow us to build a noise model for subsequent use in software for communication link simulators B Peak Amplitude Distribution The curves in Fig show the ccdf of the peak values, deduced from the pulses recorded in each car The ccdf representation was chosen, instead of the cdf, because it clearly identifies the maximum values of the peak amplitude, which correspond to the most disturbing pulses The average distribution curve, denoted Experiment, was calculated for all the measurements on the five vehicles Two curves corresponding to measurements in two cars (G1 and D4) are also plotted They have been chosen since they are at the larger distance from the average curve, and this gives an idea of the spread of the experimental values The pulse amplitudes are mostly between 0.5 and V, with the probability of obtaining an amplitude greater than V being between 1% and 8% If the low probabilities (typically less than 10−2 ) are excluded, the results indicate a low dispersion from one car to another To find the mathematical expression of the distribution functions that would be most appropriate, six well-known 866 IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, VOL 50, NO 4, NOVEMBER 2008 Fig Complementary cumulative distribution of the amplitudes deduced from values measured in two cars (curve CarG1 and CarD4), from all the values measured in-vehicle (curve Experiment) The curve Model is based on three gamma distribution functions Fig Cumulative distribution of the pseudofrequencies calculated from the measurements and the statistical model functions—Gauss, gamma, Weibull, beta, Rayleigh, exponential—have been tried However, a single function could obviously not cover the entire interval of the possible amplitude values A, and it was necessary to consider successive subintervals This was done empirically by observing the experimental curve of the probability density function and by trying to find intervals in which the variation of the function is smooth and does not present more than one maximum or minimum For the amplitude A, three intervals were considered: A < 0.31 V, 0.31 V < A < 0.67 V, and A > 0.67 V Then, to find the most appropriate function, iterative trials are made The function that minimizes the Kolmogorov–Smirnov statistics is chosen For the distribution of the amplitudes, the gamma probability density function, expressed mathematically in (2), is the one that provides the best fit with the measurement results y(x/a, b) = e−x/b xa−1 ba Γ(a) (2) where Γ() is the gamma function The values of the parameters a and b are indicated in Fig together with the probability P (A) that A belongs to one of the three intervals Given the dispersion of the results from one car to another, it did not seem necessary to try a greater number of subintervals, nor to try various distribution function combinations It must also be emphasized that, regardless of the variables studied, the gamma distribution functions provided the best fit between the experimental and the modeled results C Pseudofrequency Distribution The cumulative distribution of the pseudofrequency Fps calculated from all experimental values (curve Experiment) is plotted in Fig 9, together with the curve obtained from a modeling based on gamma functions The pseudofrequencies are widely spread, especially for CarD3 and CarG2, and are within the PLC frequency band Fig 10 Cumulative distribution of the pulse duration In-vehicle experimental results and simulation D Distribution of the Pulse Duration The cumulative distribution of the pulse duration (curve Experiment) calculated from all experimental values is plotted in Fig 10 The extreme cases, corresponding to cars CarG1 and CarD4, not show a large dispersion between cars We see that 50% of the pulses have a duration of less than µs, and 99% of the pulses last less than 20 µs The parameters of the gamma functions used for the model are also given In most cases, a correlation between the pulse length and its pseudofrequency was observed, since the average number of sinusoids per pulse is often between and E Distribution of the IAT IAT is an important pulse characteristic for PLC communication Indeed, to avoid two pulses disturbing the same code word, DEGARDIN et al.: IMPULSIVE NOISE CHARACTERIZATION OF IN-VEHICLE POWER LINE 867 TABLE I SUMMARY OF THE IN-VEHICLE AND IN-HOUSE CHARACTERISTIC PARAMETERS Fig 11 Cumulative distribution of IATs data interleaving can be an interesting solution In this case, the interleaving depth must be chosen in such a way that there is little probability that two pulses will occur during the time of an interleaved matrix Fig 11 presents the cdf of the IAT for all vehicles (Experiment), for two extreme cases (cars G1 and D4) The various intervals (expressed in log of their values) in which the parameters of the gamma function were optimized are also indicated As Fig 11 shows, the spread of the IAT values is very wide, ranging from a few microseconds to more than These are not long IAT, which are disadvantageous for communication performance, but rather are IAT with an order of magnitude measured in microseconds In fact, the average duration of an OFDM symbol for this kind of communication is on the order of 10 µs in the HP1.0 standard with its maximum rate of 14 Mb/s [15] It appears that four out of five vehicles behave quite similarly in terms of IAT distribution, with a 20%–40% probability of obtaining IATs under 100 µs, for example F Summary of the Characteristic Values The first line in Table I summarizes the principal pulse characteristics measured in the five vehicles The first column of the table gives the burst occurrence probability In the next two columns, the 50th percentile of the amplitude and the duration are given, with the 90th percentile in parentheses For the IAT, it was more interesting to calculate the 10th percentile rather than the 90th percentile since the successive pulses with very low IAT values risk disturbing the link The IAT has a strong impact on the performances of the link For example, in HomePlug 1.0 [15], the duration of an OFDM symbol is 8.4 µs, and RS and convolutional concatenated encoding is used on PHY blocks based on 20 or 40 symbols Therefore if, for example, two pulses separated by a time interval of 21 µs disturb a PHY block of 20 symbols, the error-correcting code does not provide any improvement in the error rate, because it could not correct more than two disturbed symbols An example of application of the impact of the noise on the system performances, and especially on the maximum data rate and the bit rate, is given in [11] To our knowledge, there is not yet PLC modems that have been specifically developed for in-vehicle communication, and the first idea is to implement in-the-car, on-the-shelf modems optimized for in-house PLC However, as shown in Table I, it appears that the pulses measured on the vehicles electrical networks have very different characteristics than those measured on the in-house network [19] The pulse duration and pulse amplitude are much smaller invehicle than in-house but the most critical point when optimizing the PLC physical layer will be related to the IAT, since the tenth percentile is 21 µs in-vehicle compared to 7.1 ms in-house Since the successive OFDM symbols could be disturbed more frequently, the channel coding for in-vehicle PLCs must be different than the one used in-house V CONCLUSION The objective of this study was to collect the data necessary for building a vehicle noise model to be implemented in a PLC simulation tool to allow the channel coding to be optimized An exhaustive study of the statistical properties of impulsive noise on a vehicle power network was thus conducted Noise was recorded in five vehicles, either in a stationary vehicle whose motor was idling or in a moving vehicle For a stationary vehicle, sequences of pulses, 100 µs to ms long, were observed They are likely due to interference from dedicated communications networks already implemented in the vehicle and from pulses generated by control-command devices as well The average amplitude of these pulses may greatly vary from one car to another, and additional measurement campaigns on a much larger number of cars are needed before drawing conclusions on the statistics of this low-amplitude noise For a moving vehicle, the triggering level was chosen equal to 70 or 200 mV, to be higher than the peak value of the pulses observed in stationary conditions The average distribution functions were plotted, and distribution functions corresponding to the experimental results were proposed Since available PLC modems have been developed mainly for in-house applications, the statistical properties of impulsive noise in the dc network inside a vehicle were compared to those obtained on the main power network inside buildings ACKNOWLEDGMENT The research reported in this paper was done in part within the framework of the Safe Transportation—Science and Technology Research Center (Pˆole ST2: Sciences et Technologies pour la S´ecurit´e dans les Transports) and in part within the Campus International de Recherche sur la S´ecurit´e et l’Intermodalit´e des Transports de surface (CISIT) project 868 IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, VOL 50, NO 4, NOVEMBER 2008 REFERENCES [1] Y Maryanka, “Wiring reduction by battery power line communication,” in Proc Inst Electr Eng Semin Passenger Car Electr Archit., Jun 2000, pp 8/1–8/4 [2] A Rubin, “Implementing automotive protocols for communications over noisy battery power lines,” in Proc 22nd Conv Electr Electron Eng Israel, Dec 2002, p 306 [3] W Schulz and T Hesse, “Channel 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indoor power line used for data communications,” IEEE Trans Consum Electron., vol 48, no 4, pp 913–918, Nov 2002 Virginie Degardin received the Engineer degree from the Ecole Universitaire d’Ingenieurs de Lille, Lille, France in 2000, and the Ph.D degree from the University of Lille, Lille, in 2002 She is currently an Associate Professor at the Telecommunications, Interference and Electromagnetic Compatibility (TELICE) Group, Institute of Electronics, Microelectronics and Nanotechnology (IEMN), University of Lille, where she is engaged in research on both the theoretical and experimental prediction of propagation characteristics and the optimization and performances of modulation and channel coding for power line communications Martine Lienard received the M.S and Ph.D degrees from the University of Lille, Lille, France, in 1988 and 1993, respectively In 1990, she joined the Telecommunications, Interference and Electromagnetic Compatibility (TELICE) Laboratory, University of Lille (which became a group of the Institute of Electronics, Microelectronics and Nanotechnology (IEMN) in 2004), where she is currently a Professor Her current research interests include the theoretical and experimental prediction of propagation characteristics in confined areas, as in tunnels, and the optimization and performances of modulation and diversity schemes, such as multiple-input–multiple-output (MIMO), orthogonal frequency division multiplexing (OFDM), and spread spectrum techniques, for wireless local area network and power line communications Pierre Degauque (M’76) received the M.S and Ph.D degrees from the University of Lille, Lille, France, in 1966 and 1970, respectively, and the Engineer degree from the Institut Superieur d Electronique du Nord, Lille, in 1967 He is currently a Professor at the University of Lille, where he is the Head of the Telecommunications, Interference and Electromagnetic Compatibility (TELICE) Group, the Institute of Electronics, Microelectronics and Nanotechnology (IEMN) Since 1967, he has been engaged in research in the field of electromagnetic wave propagation and radiation from various antenna configurations He was involved in research on radiation of antennas situated in absorbing media for geophysical applications His current research interests include radio propagation in confined areas, mines, and tunnels He is also engaged in research on electromagnetic compatibility, including wave penetration into structures and coupling to transmission lines Prof Degauque was the Vice Chairman of the International Union of Radio Science (URSI) Commission E, Electromagnetic Noise and Interference from 1999 to 2002 and the Chairman from 2002 to 2005 Eric Simon received the Master’s degree in electronics engineering from the Superior School of Electronics (ESCPE), Lyon, France, in 1999, and the Ph.D degree in signal processing and communications from the National Polytechnic Institute of Grenoble (INPG), Grenoble, France, in 2004 During 2005, he was a Teaching Assistant at the INPG and the following year he joined one of France Telecom R&D Laboratories as a Postdoctoral Fellow He is currently an Associate Professor at the Institute of Electronics, Microelectronics and Nanotechnology (IEMN), University of Lille, Lille, France His current research interests include the area of wireless and digital communications Pierre Laly received the Licence’s degree in telecommunication network from the Institut Universitaire de Technologies (IUT) de Lille, Lille, France, in 2002 From 1991 to 1999, he was with Micropuce, Inc He joined the Institute of Electronics, Microelectronics and Nanotechnology (IEMN)/Telecommunications, Interference and Electromagnetic Compatibility (TELICE), University of Lille, Lille, in 2000, where he is currently an Engineer His research interests include measurement techniques for wire or wireless communication systems ... specific case of DEGARDIN et al.: IMPULSIVE NOISE CHARACTERIZATION OF IN-VEHICLE POWER LINE impulsive noise observed under static conditions, we distinguished two distinct series of IAT; the first... DEGARDIN et al.: IMPULSIVE NOISE CHARACTERIZATION OF IN-VEHICLE POWER LINE 867 TABLE I SUMMARY OF THE IN-VEHICLE AND IN-HOUSE CHARACTERISTIC PARAMETERS Fig 11 Cumulative distribution of IATs data... end of the antenna cable should not exceed a limit on the order of dB·µV The choice of the maximum PSD is out of the scope of this paper, but this example underlines that an in-depth knowledge of