experimental analysis of dense multipath components in an industrial environment

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. 62, NO. 7, JULY 2014 3797

Experimental Analysis of Dense MultipathComponents in an Industrial EnvironmentEmmeric Tanghe, Davy P. Gaillot, Martine Liénard, Luc Martens, Member, IEEE, and

Wout Joseph, Senior Member, IEEE

Abstract—This work presents an analysis of dense multipathcomponents (DMC) in an industrial workshop. Radio channelsounding was performed with a vector network analyzer andvirtual antenna arrays. The specular and dense multipath com-ponents were estimated with the RiMAX algorithm. The DMCcovariance structure of the RiMAX data model was validated.Two DMC parameters were studied: the distribution of radiochannel power between specular and dense multipath, and theDMC reverberation time. The DMC power accounted for 23% to70% of the total channel power. A significant difference betweenDMC powers in line-of-sight and nonline-of-sight was observed,which can be largely attributed to the power of the line-of-sightmultipath component. In agreement with room electromagneticstheory, the DMC reverberation time was found to be nearly con-stant. Overall, DMC in the industrial workshop is more importantthan in office environments: it occupies a fraction of the totalchannel power that is 4% to 13% larger. The industrial envi-ronment absorbs on average 29% of the electromagnetic energycompared to 45%–51% for office environments in literature: thisresults in a larger reverberation time in the former environment.These findings are explained by the highly cluttered and metallicnature of the workshop.

Index Terms—Channel characterization and modeling, channelsounding, dense multipath components, industrial environment.

I. INTRODUCTION

O VER the course of the last decade, the physical view ofthe radio channel has undergone an important change.

Before that, the radio channel was commonly considered to bea collection of only specular multipath components or planewaves, see, e.g., [1]. These specular paths have well-defineddiscrete locations in the different radio channel dimensions(e.g., space, frequency, time, etc.). Nowadays, it is knownthat part of the radio channel is also continuous across thesedimensions. This part is put under the umbrella of dense

Manuscript received October 15, 2013; revised February 20, 2014; acceptedApril 17, 2014. Date of publication April 30, 2014; date of current version July02, 2014. This workwas supported by the INTERREG IV ProjectWiSE , the Re-gion Nord Pas-de-Calai , the French Ministry of Research as part of the Interna-tional Campus on Safety and Intermodality in Transportation Systems (CISIT)Project (France), and the FWO-V Project G.0325.11N.E. Tanghe, L. Martens, and W. Joseph are with iMinds, Department

of Information Technology, Ghent University, B-9050 Ghent, Belgium(e-mail: [email protected]; [email protected];[email protected]).D. P. Gaillot and M. Liénard are with the Institute of Electronics, Microelec-

tronics, and Nanotechnology, University of Lille 1, F-59655 Villeneuve d’Ascq,France (e-mail: [email protected]; [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TAP.2014.2321162

multipath components [2], [3]. Among other sources, thesedense multipath components originate from distributed diffusescattering on electrically small objects. However, the definitionis broader: the philosophy of dense multipath components isto include all radio channel energy—regardless of their exactpropagation mechanics—that cannot be associated with thespecular multipath components [4], [5]. Hence, this meansthat dense multipath components may also comprise multipathcomponents that would be categorized as specular multipathfrom a physical point of view, but cannot be reliably detectedbecause they are too weak and too close to other multipath com-ponents in the angular and delay domains. Because of this, onlythe average power of dense multipath components across thesedomains can be modeled, while their phase is inaccessible forestimation. This contrasts the specular multipath componentswhere both the power and the phase of the path can be obtained.Therefore, the dense and specular multipath components areoften also referred to as the incoherent and coherent parts ofthe radio channel, respectively. Because the available informa-tion and modeling strategies of dense and specular multipathcomponents differ, well-known radio channel parameters haveto now be evaluated for both types of multipath separately. Theeffect of dense multipath components has been investigatedon, e.g., the angular channel characteristics in [4] and [5], thepolarization channel characteristics in [6] and [7], the multipathclustering behavior in [8], and the channel capacity in [9].Dense multipath components have already been investigated

in office environments [4]–[6], [8]. This work presents—tothe best of the authors’ knowledge—the first characterizationof dense multipath components in an industrial environment,specifically a workshop for shipping container restoration.The parameters under investigation are the fractional powerof the dense multipath components and their reverberationtime. Their characterization is based on narrowband channelsounding around a 3 GHz center frequency. The specular anddense multipath components are estimated from the channelsounding data by means of the RiMAX framework, i.e., aniterative maximum-likelihood multipath search algorithm [2].The RiMAX algorithm is particularly suitable for the topic ofthis paper because it is built on a data model that allows forboth specular and dense multipath components. In contrast,estimation algorithms such as space-alternating generalized ex-pectation-maximization (SAGE) [10] and estimation of signalparameters via rotational invariance techniques (ESPRIT) [11]historically only assumed specular components in their datamodel and have not been widely modified yet to account fordense multipath [12]. Although scarce, there exist a number of

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3798 IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. 62, NO. 7, JULY 2014

Fig. 1. Measurement environment.

publications analyzing multipath propagation in industrial en-vironments, e.g., [13]–[17]. However, the novelty of this paperlies in the fact that it presents the first attempt at separating spec-ular and dense multipath in industrial radio channels.

II. MEASUREMENTS AND DATA PROCESSING

A. Measurement Environment

The industrial environment under consideration is a work-shop for reparation of intermodal shipping containers. Theworkshop consists of a single room with dimensions 20.4 m(length) by 22.0 m (width) by 4.8 m (height). The buildingmaterials for the walls are a collection of bricks, concrete slabs,steel plates, steel industrial doors, and glass windows. Thefloor and ceiling are made up of concrete slabs and corrugatedsteel panels, respectively. The workshop’s inventory consistslargely of metal container parts, machinery, and small cranes.It is noted that the workshop is intended for manual labor andas such no automated industrial processing lines are present.Fig. 1 shows a photo of the measurement environment, inwhich the highly cluttered, predominantly metallic nature ofthe workshop’s inventory is clearly apparent.

B. Channel Sounding Procedure

Narrowband1 frequency-domain channel sounding measure-ments were performed in the industrial environment under con-sideration. A vector network analyzer (VNA) of type Rohde &Schwarz ZNB8 was used to probe the radio channel in a 100MHz bandwidth centered around 3 GHz. In this frequency band,

uniformly spaced frequency points were sampled.The feeder cables for the transmitting and the receiving antennaof type Huber+Suhner S-04212-B were included in the VNAcalibration so they do not appear in the measurement data. Atboth link ends, a virtual antenna array was created by an auto-mated positioning system. A virtual antenna array does not

1Narrowband in this context means a small relative bandwidth, i.e., the ratioof the measurement bandwidth to the center frequency. This ensures that theelectrical dimensions of the antenna arrays do not change much over the mea-surement bandwidth. This property is used by the multipath estimation algo-rithm to separate the spatial and frequency dimensions in order to keep the al-gorithm computationally viable.

Fig. 2. Floor plan with Tx and Rx locations (circle: LoS, square: OLoS, tri-angle: NLoS).

suffer from antenna coupling which perturbs the radio channelmeasurements. At both transmit (T) and receive (R) side, the vir-tual array was a planar horizontal uniform circular array (UCA)consisting of antenna elements. The interele-ment spacing was 0.45 times the wavelength at the largest mea-surement frequency of 3.050 GHz. For each position of transmitand receive antenna, the VNA took 10 sweeps of the frequencyrange to be used for measurement noise reduction when ap-plying the RiMAX algorithm. As transmitting and receivingantenna, broadband omnidirectional discone antennas of typeelectro-metrics EM-6116 were used. Both antennas were 1.50mabove ground level during measurements. Measurements weredone outside of regular working hours because frequency-sweptmeasurements with virtual antenna arrays require a static radiochannel without movement (in contrast to time-domain mea-surements which allow for faster acquisition times and whichcan also capture time-variant channels [18], [19]).Fig. 2 presents a floor plan of the measurement environment

with transmitting (Tx) and receiving (Rx) antenna locations.In total, 12 Tx-Rx links were measured: the Tx remained at afixed location while the Rx was moved to different locations be-tweenmeasurements ( for in Fig. 2).Measure-ment were done for three different Tx-Rx link shadowing condi-tions: line-of-sight [(LoS), circles in Fig. 2], obstructed line-of-sight [(OLoS), squares], and nonline-of-sight [(NLoS), trian-gles]. The distinction between OLoS and NLoS was made asfollows: in the former case the LoS component was blocked byan obstacle such as a small crane, which contains gaps throughwhich the Tx and Rx can still partially see each other. In thelatter case the LoS component was visually fully blocked by anobstacle such as a container.

C. Estimation of Specular and Dense Multipath Components

1) RiMAX Data Model: The measured array response vectorcan be written as the sum of a determin-

istic part and a stochastic part , i.e.,

TANGHE et al.: EXPERIMENTAL ANALYSIS OF DENSE MULTIPATH COMPONENTS IN AN INDUSTRIAL ENVIRONMENT 3799

. The deterministic part is a function of the channel’s Spec-ular Multipath Components (SMC). The SMC parameters aregrouped into the parameter array . The stochastic part is de-termined by the dense multipath components (DMC) as wellas the thermal noise inherent to the measurement equipment.The DMC and noise parameters are grouped into the parameterarray . Based on the principle of maximum entropy in mul-tiple-input–multiple-output (MIMO) channel modeling, it is as-sumed that is a realization of a multivariate complex Gaussianprocess with probability density function [20]

(1)

where . The covariance matrix de-pends only on the DMC + noise parameters . The data modelof the RiMAX estimation framework assumes the DMC is Kro-necker-separable in the spatial and frequency domains. Mathe-matically, the covariance matrix has the following struc-ture [2]:

(2)

where is the identity matrix of size . According to (2),RiMAX’s data model assumes DMC that is spatially white attransmit and receive side (i.e., has constant angular power den-sities). Additionally, measurements are corrupted by complexadditive white Gaussian noise with power . Furthermore, theDMC is assumed correlated and wide-sense stationary in thefrequency domain, which implies that the frequency covariancematrix has a Toeplitz structure. The DMC power ismodeled as an exponential decay as function of delay [2]. Anexponential decay is commonly found in highly scattering en-vironments such as for example reverberation chambers [21]

(3)

From (3), the frequency covariance matrix is calculated as, where is a sampled version of

the power spectral density associated with [i.e., the Fouriertransform of (3)]. Based on the capabilities of the measurementsystem in Section II-B, and (2) and (3), the following structurefor SMC and DMC + noise parameter arrays is set:

(4)

In (4), is a matrix where is the number of spec-ular multipath components. Each row of contains the corre-sponding specular parameter for each of the specular paths.Furthermore, is a 4 1 vector containing the DMC and noiseparameters. Finally, we note that the RiMAX framework is an

iterative maximum-likelihood algorithm: the estimates andof the SMC and DMC + noise parameter arrays are deter-

mined such that they maximize the likelihood of observing themeasured response .2) Model Order Selection and Reliability of Specular Paths:

A major topic of this paper is the power distribution betweenSMC and DMC. Because of this, the issue of model order se-lection or the determination of the number of specular pathsshould be treated with care. The usual approach to model orderselection is to use algorithms such as the Akaike informationcriterion or the minimum description length which calculate thelog-likelihood of candidate models with different to decideon its value [22]. Instead in this paper, we use the approach out-lined in [2] that is based on the estimated power of specularpaths. An important feature of the RiMAX algorithm is that itprovides as a by-product an estimate of the Fisher informationmatrix (FIM). For asymptotically uncoupled parameters, the di-agonal elements of the inverse of the FIM are variance estimatesof the channel parameters in (4). It is possible to associate aSignal-to-Noise Ratio (SNR) with the estimated specular pathmagnitudes: a specular path with estimated complex ampli-tude has an SNR equal to

(5)

In (5), is the variance of its argument. The specularpath is deemed unreliable and is removed from further anal-ysis if its SNR is too small. It can be derived that the es-timator follows a half-normal distribution (i.e., the distributionof the absolute value of a normally distributed random variablewith zero mean) with variance [2]. The SNR in(5) then follows a chi-squared distribution with one degree offreedom . We retain path if its estimated SNR is largerthan the 90th percentile of , equal to 2.71 or 4.32 dB. TheRiMAX algorithm is iterative: in each iteration, a fixed numberof new specular paths are estimated from the measured channelresponse. The number of new paths per iteration can be chosenarbitrarily and was set to five. If all five paths in one iterationfail the SNR threshold of 4.32 dB, then we consider the channelto be exhausted of reliable specular paths and the RiMAX al-gorithm is stopped. We further note that model order selectionbased on (5) is more suited for this paper’s topic than selectionbased on information criteria. The latter calculate an optimalvalue for the size of the signal subspace as a whole without de-ciding on the reliability of individual specular paths. However,the path SNR method checks each individual path for its reli-ability. This is more in line with the philosophy of dense mul-tipath: dense multipath can also comprise specular paths thatcannot be resolved reliably due to the channel sounder’s limitedaperture(s).Fig. 3 visualizes the model order selection for the Tx-

link. Shown are the measured averaged power delay profile(APDP), the estimated SMC, and the APDP of the DMC +noise part after subtracting the (reliable) SMC from the channelresponse. In Fig. 3, the estimated SMC are shown as circles.Vertical lines connect each specular path with the varianceof the path’s magnitude, shown as crosses. The vertical linesdirectly represent the SNR in (5) in dB. If the SNR is lessthan 4.32 dB then the corresponding path is considered to


Fig. 3. SMC and DMC+ noise in the delay domain for the Tx- link.

Fig. 4. SMC of the Tx- link (the three most powerful SMC end in a dia-mond marker).

be unreliable, which is indicated by a filled circle in Fig. 3.Specular paths for which the SNR is greater than 4.32 dB arereliable and thus retained: these are indicated by an emptycircle. We note that the choice of the threshold for the pathSNR is argued but still in part arbitrary, as is the case with anycriterion. Despite this, our choice appears to work well for ourmeasurement data: for example in Fig. 3, SMC that are clearlyin the measurement noise part (delays of 500 ns and larger) areall rejected by the SNR criterion. For the Tx- link in Fig. 3,the RiMAX algorithm stopped with 50 SMC rejected and 75SMC retained. The model order selection procedure for thislink thus resulted in specular paths. Fig. 4 shows the 75SMC of the Tx- link, where the length of the plotted raysis proportional to their power in dB. The three most powerfulSMC end in diamond markers: they are the LoS component,the ground-reflected ray with azimuth angles close to those ofthe LoS component, and the ray reflected off the top wall.

3) Validity of the DMC+Noise CovarianceModel: This sec-tion aims to analyze the goodness-of-fit of the simple DMC+noise covariance structure (2) to our industrial channel soundingdata. Following the estimation of the SMC and DMC+ noiseparameter arrays and in the previous sections, the deter-ministic and stochastic parts of the measured channel can bereconstructed as follows:

(6)

(7)

In (6), is an appropriate steering matrixaccounting for the array geometries and frequency settings ofthe channel sounder. The goodness-of-fit analysis is started byperforming a decorrelation transformation on the stochastic partusing the reconstructed covariance matrix in (2)

(8)

If the covariance structure (2) is a perfect fit for the stochasticpart then the decorrelation transformation (8) is a whitening

transformation. In this case , i.e., the co-variance matrix associated with , is the identity matrix. Dueto the usual high dimensionality of channel sounding data, itis more than often impossible to obtain a well-conditioned es-timate of . For example, the size of is

for our channel sounding data.In contrast only ten channel observations (ten VNA frequencysweeps in Section II-B) are available to estimate the expecta-tion , which is largely insufficient. To alleviate this issue,it is further assumed that the spatial and frequency dimensionsof are separable: this is justified given the narrowband char-acter of the channel sounding procedure. This is mathematicallygiven by , with

(9)

(10)


Fig. 5. Spatial and frequency covariance matrices of the DMC + noise part ofthe Tx- link. (a) magnitude of in dB. (b) magnitude of in dB.

and are the spatial and frequency covariance ma-trices, respectively. (size ) and (size

) are reshaped versions of . Each row of(respectively ) contains the channel gain for one Tx-Rxsubchannel (respectively one frequency point) across all fre-quency points (respectively Tx-Rx subchannels). Equations (9)and (10) lead to well-conditioned covariancematrices: and

can be interpreted as frequency and spatially averaged ver-sions respectively of the full covariancematrix . Fig. 5 showsthe magnitudes in dB of the elements of and for theTx- link. It is observed that both the spatial and frequencycovariance matrices approach the identity matrix, thereby con-firming the validity of the DMC + noise covariance structure (2)for this link.The proximity of and to the identity matrix is

further investigated quantitatively using the Correlation MatrixDistance (CMD) measure [23]. The CMD between twocovariance matrices and of equal size is defined as

(11)

In (11), denotes the trace and is the Frobenius norm.is zero for equal covariance matrices and becomes one

if the matrices differ maximally. For all 12 Tx-Rx links of ourmeasurement campaign, varies between 0.03and 0.05, while ranges between 0.19 and 0.23.

These low values for both CMDs support the whiteness ofunder the narrowband assumption and thus by extension thevalidity of the DMC + noise covariance structure (2) for ourmeasurement data. Finally, the observation thatis larger than is not necessarily because thefrequency covariance model is a worse fit to our measurementdata than the spatial covariance model. We note that theestimator in (10) is less accurate (has larger variance) than the

estimator in (9), because . The smallerestimation accuracy of may make this covariance matrixappear to be further away from the identity matrix than .4) Calculation of SMC and DMC Power: For the purpose

of this section, the reconstructed deterministic part in (6) andstochastic part in (7) are first reshaped as matrices and

. Each column of (respectively ) con-tains the frequency response of the SMC (respectively DMC+noise) for one Tx-Rx subchannel. The APDPs of the SMC andDMC + noise can then be calculated

(12)

(13)

In (12) and (13), is the discrete Fourier trans-form matrix and returns the main diagonal of its matrixargument. The average powers of the SMC and of the DMC +noise for a single-input–single-output (SISO) link can be ob-tained by summing the APDPs (12) and (13) over all delay bins

(14)

(15)

Because only radio channel characteristics are of interest, itis important to modify (15) to remove the contribution of thenonchannel effect, i.e., the measurement noise. This is easilydone by observing the independence of the DMC and noise pro-cesses, and by making use of the RiMAX estimator for themeasurement noise power. The average power of the DMC isthen calculated as

(16)

A quantity of interest is the fractional DMC power , i.e.,the percentage the DMC contributes to the total channel power

(17)

III. RESULTS

Table I lists for each Tx-Rx link a number of channel pa-rameters that are relevant for the topic of this paper. Shownare the number of SMC obtained with the procedure inSection II-C2 and two DMC-related parameters: the fractionalDMC power in (17) and the DMC reverberation time


TABLE INUMBER OF SMC, FRACTIONAL DMC POWER, AND DMC REVERBERATION TIME FOR ALL TX-RX LINKS

TABLE IICOMPARISON WITH RELATED WORK FOR

in (3).2 The Tx-Rx links are grouped according to their linkshadowing circumstances. The DMC-related parameters arediscussed in-depth in the following subsections.

A. Fractional DMC Power

From Table I, there is indication that the fractional DMCpower becomes larger with increased link shadowing: thesample median of equals 26.6%, 50.0%, and 63.0% forLoS, OLoS, and NLoS, respectively. Several comparative sta-tistical tests were carried out on the dataset in Table I tosubstantiate this observation. In the following, all tests are car-ried out with the link shadowing categories (LoS, OLoS, andNLoS) as groups.A Kruskal-Wallis analysis of variance confirms our obser-

vation that there is a difference in the median value ofbetween at least two link shadowing groups (p-value = 0.04).Mann-Whitney U tests are subsequently performed to search forsignificant differences inmedian between link shadowingpairs. No significant difference is found between OLoS and bothLoS and NLoS (p-values of 0.11 and 0.29, respectively). TheOLoS category can thus be considered as a gray zone betweenLoS and NLoS when it comes to DMC power: the distinction ofan OLoS category for this particular channel parameter may beomitted in the future if continued studies reach the same con-clusion. However, a significant difference in in medianwas found between LoS and NLoS (p-value = 0.04). Further in-vestigation shows that this difference can be largely attributedto the fractional power of the LoS component: the power of theLoS path accounts for 21%, 34%, and 31% of the total channelpower of the Tx- , Tx- , and Tx- links, respectively(calculated with (6) and (14) by using the SMC estimates ofonly the LoS component). As mentioned at the start of this sec-tion, the sample median of the fractional DMC power is 63% forNLoS and 27% for LoS, a difference of 36%. On the other hand,the sample median of the LoS component’s fractional power is31%. Hence, one can conclude that the decrease in fractionalDMC power from NLoS to LoS is largely caused by the intro-duction of a specular LoS component.

2For increased readability, the hat symbol is omitted from the estimated pa-rameters in the remainder of the paper.

Table II presents a comparison of the fractional DMC powerwith related work in office environments. It has been establishedthat the fractional DMC power strongly depends on the typeof environment [9]. Furthermore, the work of [4] also found astrong link between and link shadowing category. Ourstatistical analysis reaches the same conclusion, except for per-ceived weak differences between OLoS and LoS, and OLoS andNLoS. From Table II, the range of the LoS category ofour measurements is shifted 13% up from [4]. The largerin the industrial environment may be explained by an increasedsusceptibility of this environment to diffuse scattering: Fig. 1shows a highly cluttered environment comprising small metalparts and tools as well as oblong structural steel. Additionally,our measurements were performed at a lower center frequencythan in [4]. This means reduced electrical dimensions of ob-jects and, therefore, reduced occurrences of specular reflectionin favor of diffuse scattering.For our measurements, the largest range is found for

the OLoS category (see Table II). This can be explained by thecomparatively larger diversity of shadowing objects for this cat-egory. While there are no shadowing objects for LoS and ship-ping containers are the only type of shadowing object for NLoS,in comparison the types of shadowing objects encountered forOLoS are broader: a container edge for the Tx- link, a work-bench for Tx- , and a small crane for Tx- and Tx- .These results suggest that may be best treated as a con-tinuous variable and that there appears to be a gradual powertransfer from the SMC to the DMC as link shadowing objectsbecome more dense. Furthermore in Table II, the rangefor OLoS in [4] is somewhat smaller than for our measurements:in line with the explanation above, this may be because there isonly one type of OLoS shadowing object in [4], i.e., a flight ofstairs.For the NLoS category, the values of in [4] are clearly

larger compared to [6] (in the latter a mix of OLoS and NLoSmeasurements were made). This may have to do with the factthat—contrary to [6]—the transmitter and receiver were on dif-ferent floors in [4]. The authors of [4] state that this creates aharsh NLoS environment subject to mostly continuous propa-gation phenomena, for which also part of the SMC cannot be


detected due to limited measurement resolution. The NLoS sce-nario in this paper is more readily comparable to the NLoS sce-nario in [6]: both have transmitter and receiver on the samefloor and while the NLoS situation in [6] is created by brickand plaster walls, the containers (seen in Fig. 1 on the left and inFig. 2 at the bottom) create a similar NLoS obstruction situation(although not obstructing the entire distance between floor andceiling like a wall does). The NLoS values in this paperare slightly larger than those found in [6], despite the NLoS ob-struction not reaching from floor to ceiling in our environment.This redemonstrates the prevalence of diffuse scattering in theindustrial environment, just as for the LoS category.

B. DMC Reverberation Time

First, Fig. 3 clearly shows that the single exponential decay(3) fits the DMC process very well. This contrast the findingsof [8], which uses the OLoS office-environment measurementof [4] and where visually about ten DMC clusters are distin-guished. Second, Table I reveals that the DMC reverberationtime does not change noticeably between link shadowing cat-egories and is even fairly constant across all Tx-Rx links. Thisobservation is backed by a Kruskal–Wallis analysis of variancewhich found no difference inmedian between link shadowingcategories (p-value = 0.55).The two observations made about DMC in the previous para-

graph—a single exponential decay and a reverberation time thatis independent of Rx location—is entirely in line with the theoryof room electromagnetics (RE) [24]–[26]. The RE theory bor-rows from the field of room acoustics and obtains an exponentialdecay for the diffuse field under these assumptions: the field’sintensity does not depend on direction (rich scattering environ-ment) and its energy density is constant across the whole room(valid for rooms that are small enough). The industrial work-shop can be deemed to satisfy both of these assumptions. Aswith room acoustics, RE is a scalar theory and therefore doesnot account for more than one polarization at Tx and/or Rx.Furthermore, RE provides a simple relationship between the re-verberation time , the room’s geometry and a single constantdescribing the room’s electromagnetic absorption qualities

(18)

In (18), and are the room’s volume and area, respec-tively, is the speed of light in vacuum, and is the fractionof energy absorbed by the area . Using the industrial work-shop’s dimensions in Section II-A, we obtainand . From Table I, the median across allTx-Rx links is determined as 75.8 ns. Using (18), we then ob-tain the median absorption coefficient as 0.29.The parameter is particularly interesting to compare the

reverberation properties of different rooms: unlike the pa-rameter, is independent of room geometry and solely dependson the average electromagnetic absorption characteristics of thematerials present. These materials in turn directly relate to thetype of environment. For example for office environments andvertical polarization at both link ends, [24] found and

[26] obtained . In comparison, in the indus-trial workshop for the same polarization conditions reveals thatthe materials of the latter are 16 to 22% less absorbent. This iseasily explained by the highly reflective nature of the materialscommonly found in an industrial environment.

IV. CONCLUSION

This work presented a statistical analysis of dense multipathcomponents (DMC) in an industrial workshop for shippingcontainer restoration. The analysis was based on radio channelsounding experiments and the RiMAX maximum-likelihoodalgorithm was used to estimate specular and dense multipathcomponents from the channel sounding data. The DMC co-variance structure of the RiMAX data model was found to bea good fit for the workshop’s radio channel. Two parametersrelated to the DMC were investigated: the fractional DMCpower, i.e., the percentage of total channel power that can beattributed to dense multipath, and the DMC reverberation timein the time-delay domain. Among other things, the impact of thelink shadowing category, i.e., line-of-sight (LoS), obstructedline-of-sight (OLoS), or nonline-of-sight (NLoS), on these twoDMC parameters was examined. The following results wereobtained for the fractional DMC power:1) The fractional DMC power varies between 23%–38%(LoS), 27%–70% (OLoS), and 57%–64% (NLoS).

2) The difference between the median fractional DMCpowers of the NLoS and the LoS categories (= 36% fromsample) is statistically significant. Most of this differ-ence can be attributed to the power of the LoS multipathcomponent: the sample median of the LoS component’sfractional power is 31%.

3) The fractional DMC power was found to be somewhathigher in the industrial workshop compared to office en-vironments in literature due to the cluttered and metallicnature of the workshop’s inventory. A cautious estimate ofthe difference is 4 to 13%, while acknowledging the pos-sibility that the fractional DMC power may also correlatewith other environment parameters that cannot be easilydeduced from literature, e.g., room dimensions, Tx-Rx dis-tance, etc.

These results were obtained for the DMC reverberation time.1) The DMC process in the time-delay domain is well-de-scribed by a single exponential decay. Furthermore, theDMC reverberation time is nearly constant and thus inde-pendent of Rx location. These observations are entirely inline with the theory of room electromagnetics.

2) Room electromagnetics can be used to calculate the av-erage absorption of electromagnetic energy by the environ-ment. The industrial workshop is found to be 16% to 22%less absorbent compared to office environments in litera-ture. This is due to the metallic and thus highly reflectivenature of the materials in the workshop.

ACKNOWLEDGMENT

The authors would also like to acknowledge the scientificinput of the COST IC1004 action on Cooperative Radio Com-munications for Green Smart Environments.


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Emmeric Tanghe was born in Tielt, Belgium, onAugust 31, 1982. He received the M.Sc. degreein electrical engineering from Ghent University,Ghent, Belgium, in July 2005, and the Ph.D. degreein May 2011, with is scientific work focused on themodeling of indoor and outdoor propagation throughfield measurements.From September 2005 to May 2011, he was a Re-

search Assistant at iMinds-UGent/INTEC (Depart-ment of Information Technology) of Ghent univer-sity. Since May 2011, he has been a Postdoctoral Re-

searcher at Ghent University and continues his work in propagation modeling.Since October 2012, he has been a Postdoctoral Fellow of the FWO-V (ResearchFoundation-Flanders).

Davy P. Gaillot received the B.S. degree in mechan-ical engineering from Ecole Nationale d’Ingénieursde Metz (ENIM), Metz, France, in 2002, and themaster’s degree in mechanics, materials, structures,and processes from the University of Metz, Metz,France. He also received the Ph.D. degree, in 2007,from the Department of Materials Science andEngineering at the Georgia Institute of Technology,Atlanta, GA, USA.He is a Postdoctoral Fellow at the Institute of

Electronics, Microelectronics, and Nanotechnology(IEMN) in Villeneuve d’Ascq, France. Since 2008, he has been an AssociateProfessor at the University of Lille 1 in the IEMN–TELICE Group. Hisresearch interests include the development of outdoor/indoor radio channelmodels for localization techniques that are supported by legacy or upcomingmillimeter-wave and centimeter-wave wireless network standards. In addition,he focuses on highly diffuse and industrial radio channels for propagationmodeling and exposure assessment.

Martine Liénard received the M.Sc. and Ph.D.degrees from Université Lille 1, Villeneuve d’Ascq,France, in 1988 and 1993, respectively.Since 1990, she has been with the Telecommuni-

cations, Interferences et Compatibilité Electromag-nétique (TELICE) Group, Institut d’Electronique,de Microélectronique et de Nanotechnologie, whereshe is currently a Professor and the head of TELICE.Her current research interests include both theo-retical and experimental prediction of propagationcharacteristics in confined areas, optimization of

the PHY layer including diversity schemes with applications to wireless localarea networks, powerline communications, and rail and road transportationcommunication systems.


Luc Martens received the M.Sc. degree in electricalengineering from Ghent University, Ghent, Belgium,in July 1986, and the Ph.D. degree in December 1990.From September 1986 to December 1990, he was a

Research Assistant at the Department of InformationTechnology (INTEC) of Ghent University. Duringthis period, his scientific work was focused on thephysical aspects of hyperthermic cancer therapy.His research work dealt with electromagnetic andthermal modeling, and with the development ofmeasurement systems for that application. Since

1991, he manages the wireless and cable research group at INTEC. This groupis, since 2004, part of the iMinds institute. Since April 1993, he has beena Professor at Ghent University. His experience and current interests are inmodeling and measurement of electromagnetic channels, in electromagneticexposure e.g., around telecommunication networks and systems such ascellular base station antennas, and in energy consumption of wireless networks.He is an author or coauthor of more than 300 publications in the domain ofelectromagnetic channel predictions, dosimetry, exposure systems and health,and wireless communications.

Wout Joseph was born in Ostend, Belgium, onOctober 21, 1977. He received the M.Sc. degreein electrical engineering from Ghent University,Ghent, Belgium, in July 2000, and the Ph.D. degreein March 2005.From September 2000 to March 2005, he was a

Research Assistant at the Department of InformationTechnology (INTEC) of Ghent University. Duringthis period, his scientific work was focused onelectromagnetic exposure assessment. His researchwork dealt with measuring and modeling electro-

magnetic fields around base stations for mobile communications, and withhealth effects of exposure to electromagnetic radiation. Since April 2005, hehas been a Postdoctoral Researcher at iMinds-UGent/INTEC. From October2007 to October 2013, he was a Postdoctoral Fellow of the FWO-V (ResearchFoundation-Flanders). Since October 2009, he has been a Professor in the do-main of experimental characterization of wireless communication systems. Hisprofessional interests are electromagnetic exposure assessment, propagation forwireless communication systems, and antennas and calibration. Furthermore,he specializes in wireless performance analysis and quality of experience.

experimental analysis of dense multipath components in an industrial environment

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