# Large-Scale Fading and Time Dispersion Parameters of UWB Channel in Underground Mines

Authors: Abdellah Chehri; Paul Fortier; Pierre Martin Tardif
Journal: International Journal of Antennas and Propagation
 (2008)

---

## Abstract

RF channel measurements in underground mines have important applications in the field of mobile
communications for improving operational efficiency and worker safety. This paper presents an experimental
study of the ultra wideband (UWB) radio channel, based on extensive sounding campaigns covering the
underground mine environment. Measurements were carried out in the frequency band of 2–5 GHz. Various
communication links were considered including both line-of-sight (LOS) and non-LOS (NLOS) scenarios. In
this paper, we are interested in more details of the variations of the RMS delay spread and mean excess delay
with Tx/Rx separation, and the variation of RMS with mean excess. The distance dependency of path loss and
shadowing fading statistics is also investigated. To give an idea about the behaviour of UWB channel in
underground mines, a comparison of our approach with other published works is given including path loss
exponent, shadow fading variance, mean excess delay, and RMS delay spread.


---

## Body

## 1. Introduction

During the  last
years, a number  of mining  companies
have invested  in modern communications systems in order to increase the
productivity, automate processes, and to reduce operational costs. Remote
monitoring, video transmission at very high speed, and the extensive use of
cellular phone technology are all of interest. Safety of miners is also a
concern, regulated by health and safety regimes.Underground communication systems still rely on analog
technology. Digital upgrades modernize legacy technology using a coaxial cable
to transmit important signals like voice, video, and telemetry. Therefore, all
wired equipments can benefit from an increase in efficiency from digital technology.However, when it comes to mobile equipment, the only
medium is radio wave. Mobile RF communications is important in mining since it
can improve operational efficiency and worker safety [1]. Successful RF
communications require an understanding of the RF propagation channel in mine
tunnels. A communication system in this environment is a convenience for daily
activities. However, in emergencies, it may become vital. In a disaster, such
as a fire, the conventional wire communication system may become unreliable,
necessitating a wireless radio system.Ultra-wideband is a radio communication technique
using signals over an extremely wide frequency bandwidth, typically in the
order of 500 MHz to several GHz. This particularity may be exploited to develop
communication systems with high-rate and low-rate wireless communications and
localization capabilities [2].The development of UWB communication systems has
become an intensive research activity in both academic and industrial
communities. A considerable amount of work has already been performed in this
field, and a number of experimental sounding campaigns were set up in order to
characterize the UWB propagation channel [3]. For instance, the IEEE 802.15.3a
and IEEE 802.15.4a task groups developed UWB channel models for the simulation
of systems proposed for standardization [4, 5]. The analysis and design of
an UWB communication system requires an accurate channel model to determine the
maximum achievable data rate, to design efficient modulation schemes, and to
study associated signal-processing algorithms. Hence, accurate channel
characterization is necessary for UWB transceiver design and for efficient
utilization of the system's resources such as frequency spectrum and transmit
power. The propagation channel sets fundamental limits on the performance of
UWB communication systems.In this paper, characterization of some parameters of
the UWB channel in underground mines are extracted such as mean excess delay
and RMS delay spread. Large-scale modeling is an important characteristic for
link budget calculation and system design. Hence, this parameter is also
investigated in this paper. In the next section, we give details about the
environment, the measurement campaigns, and the methodology used for data acquisition.
The results on the analysis including large-scale modeling and time dispersion
of UWB channel in underground mines are summarized in Section3. We give a
comparison of published results for UWB and wideband channels in Section 4.
Section 5 concludes this paper.


## 2. Measurement Setup, Environment, and Methodology

The
measurements campaigns were carried out in a real underground mine. This former
gold mine is located approximately 530 kilometers north-west of Montreal and is
now managed by MMSL CANMET (Mining and Mineral Sciences Laboratories-Canadian
Center for Minerals and Energy Technology) in which tests and trials
in field can be performed. The
dimensions of the gallery varied between 2.5 m and 3 m in width and the height
was about 3 m, typical of a conventional mine.Since we gave other
details of the propagation experiments in other mine segments [6–8], we are going to summarize here only the fundamental issues of this paper.
A vector network analyzer (VNA) was used to sound the radio channel following a
classical S21 parameter measurement procedure. The VNA
recorded the variations of 1601 complex tones across the 2–5 GHz frequency
band. The resulting frequency resolution of 1.875 MHz implied a maximum
resolvable delay of 533 nanoseconds for the
measured channel impulse response (CIR).The static condition ensures that the impulse response
of the channel does not vary during the time required for the VNA to collect
all the needed data. The two ports of the VNA are connected to the transmitters
and receivers antennas. An Electro-Metrics EM-6116 antenna is used both in
transmission and reception (Figure1).Figure 1
Channel measurement setup.The sounding signal was transmitted at a power level
of 20 dBm. The antenna coaxial cables used in the measurements had been
carefully chosen. The attenuation of the coaxial cable (STORM, True Blue) was
found to be very high. The use of passive elements in the measurement apparatus
(cables, SMA connectors, etc.) imposed systematic and frequent calibration
measurements, in order to compensate undesirable frequency-dependent
attenuation factors that could affect the collected data. Following the VNA
manual's recommendations, the calibration through response type was selected,
and the cables and the connectors were included in this calibration.The Rx antenna was placed at a height of about 1 m.
The Tx antenna was fixed at a height of 1 m and arranged in a8×5 rectangular grid with 7 cm spacing, slightly
smaller than half the wavelength at the lowest measured frequency (2 GHz), to
obtain approximately independent samples. Both line-of-sight (Figure 2) and
nonline-of-sight (Figure 3) configurations were investigated.Figure 2
CANMET mine gallery-LOS case.Figure 3
CANMET mine gallery-NLOS case.As can be seen on Figures2 and 3, the gallery walls have a significant roughness. The gallery
constitutes a channel with multiple paths. Many obstacles are present (electric
wires, ventilation systems, cables, and pipes located near the ceiling) and
they act as more or less reflective surfaces for the waves. The floor of the
gallery is rather undulating and one can see water puddles of different sizes.


### 2.1. Measurements for LOS Case

In this measurement campaign, the transmitting antenna remained in a fixed position
whereas the receiving antenna was moved at variable distances going from 1 m up to 12 m with a 1 m step between measurements (Figure4(a)).Figure 4
Transmitter/receiver positioning (a) LOS case , (b) NLOS case.


### 2.2. Measurements for NLOS Case

This campaign
was similar  to the LOS case. However, because of the difficulty of placing  the
receiving antenna at close range, due to the topology of the mine, we started
the measurements at 5 m. A schematic diagram of the measurement setup is shown
in Figure4(b).


## 2.1. Measurements for LOS Case

In this measurement campaign, the transmitting antenna remained in a fixed position
whereas the receiving antenna was moved at variable distances going from 1 m up to 12 m with a 1 m step between measurements (Figure4(a)).Figure 4
Transmitter/receiver positioning (a) LOS case , (b) NLOS case.


## 2.2. Measurements for NLOS Case

This campaign
was similar  to the LOS case. However, because of the difficulty of placing  the
receiving antenna at close range, due to the topology of the mine, we started
the measurements at 5 m. A schematic diagram of the measurement setup is shown
in Figure4(b).


## 3. Measurement Data Processing

Extracting
characteristic radio channel parameters from channel measurements and creating
statistical channel models are a common method to
assess the performance of air interface candidates in terms of error rates and
outage probabilities. Thus the main objective of channel sounding is to
correctly model the UWB channel. Consequently, if the multipath medium is well
characterized, the transmitter and the receiver can be designed in order to
decrease the effect of multipath fading.


### 3.1. Large-Scale Modeling

For UWB systems, it was reported that the path loss
modeling can be simplified by assuming that the frequency dependence and the
distance dependence can be treated independently of each other:(1)P(f,d)=PL(f)⋅PL(d).
However, PL(f) was already analyzed in [6]. Hence, in this
paper, we analyze the path loss distance dependence.


#### 3.1.1. Path Loss Distance Dependence

The path loss
of the channel represents the attenuation a signal undergoes when transmitted
through the medium. The frequency response obtained from the VNA was referenced
to 1 m for LOS and 5 m for NLOS, as described in the measurement procedure.
Using the measured frequency response data, the average path loss at any
distanced can be estimated using the following
equation:(2)Pl(d)=1NM∑i=1N∑j=1M|H(fi,fj;d)|2,where H(fi,fj;d) denotes the jth channel transfer function snapshot at
frequency fi and at distance d, N is the number of observed frequencies, and M is the number of frequency response snap shots
over time. It is well known that the mean path loss referenced to a distance d0 can be modeled as a function of distance
using:
(3)Pl(d)=Pl0+10nlog10(dd0)+S(d),d≥d0,where d0 is the reference distance, in our case d0 = 1 m for LOS (d0 = 5 m, for NLOS) and Pl0 is the interception point, n is referred to as the path loss exponent, and S is the shadowing fading parameter that varies
randomly from one location to another.Equation (3) was used to compute path loss for all of
the individual power impulse response measurements and for each of the
spatially averaged power impulse response.In this paper, the average power for each measurement
is calculated by averaging the power over all sample points of the measured
frequency response. Using linear regression analysis, the minimum mean square
error (MMSE) line is calculated for the dependence of average power (dB) on the
logarithm of the distance for each global experiment. The slope of the
regression line gives the experimental value ofn.Figure5 shows a scatter plot of received power (dB)
versus distance on a log scale for the measurement and the MMSE line fitted to
the data. The linear regression analysis gives n = 1.47 for LOS and n = 2.45 for NLOS.Figure 5
Scatter plot of path loss versus Tx/Rx separation distance.


#### 3.1.2. Shadow Fading

Due to variations in the surrounding environments,PL observed at any given location
will deviate from its average value. This phenomenon is called shadow fading
and has been reported by many measurements [9–15] to follow a
log-normal distribution. In   (4), the shadow
fading parameter is given by the term S that varies randomly from one location to
another. It is a zero-mean Gaussian-distributed random variables in dB with
standard deviation σs which is also in dB.The statistical analysis ofS gives σS = 1.1 for LOS and σS = 2.94 for NLOS, respectively. The cumulative
distribution function (cdf) of the deviation between the fitted and measured
data is plotted versus the normal cdf (Figures 6 and 7). These curves show that
the shadow fading is lognormally distributed.Figure 6
CDF of shadowing fading fit for normal
distribution-LOS scenario.Figure 7
CDF of shadowing fading fit for normal
distribution-NLOS scenario.


### 3.2. Dispersions Properties of UWB Channel

The dispersive
properties of the UWB channel can be considered as an extension of the
large-scale study of the channel. There are two parameters to be characterized
and analyzed. These parameters are the RMS delay spreadτrms and mean excess delay τm.
These two important parameters are used to characterize the temporal dispersive
properties of the multipath channels. This is useful to estimate the
performance and potential for ISI.


#### 3.2.1. Mathematical Model

The discrete
mathematical model used for the complex impulse response is that of a linear
filter with variable complex weights and variable delays and is given
by(4)h(d,t,τ)=∑k=1Nak(d,t)δ(t−tk(d,t))ejθk(d,t),where N is the number of resolvable multipath
components, ak, tk, θk are random amplitude, delay time
(arrival-time), and phase sequences, respectively, and δ is the Dirac delta function.By obtaining adequate statistical modeling of these
random variables, we can offer a complete description of the multipath channel.d represents the measurement location, while t is the measurement instant. While d varies according to different locations, t will not be taken into consideration since
measurements were without interference of any moving objects (persons or
vehicular) and with stationary Tx and Rx.According to this model, the received signal is the
convolution of the transmitted signal with the complex impulse response, added
with the channel noise, usually modeled as a complex Gaussian process.In addition to the statistical information concerning
the above random variables, another important parameter is extracted, namely,
the RMS delay spread (τrms). It is a one-number representation of the
multipath channel computed from its complex impulse response that gives an
estimation of the amount of intersymbol interference (ISI) to be encountered in
the channel.Many researchers have reported results concerning the
measurement ofτrms for UWB channel in indoor environment [9–11], residential [12–14], and in industrial environment [16]. It
is computed using(5)τrms=∑k=1Nak2⋅(τk−τf−τm)2∑k=1Nak2,where τf is the arrival time of the first multipath
component to rise above the predefined threshold, τk is the arrival time sequence of the multipath
components, and τm is the average excess delay computed from  the
formula:(6)τm=∑k=1Nak2⋅(τk−τf)∑k=1Nak2.It is clear for (5) and (6) that τrms is the square root of the second central
moment of the power-weighted delay profile.


#### 3.2.2. RMS Delay Spread and Mean Excess Delay

The channel
parameters RMS delay spread and mean excess delay are computed for each
received profile at every location of the receiver in LOS and NLOS cases.
Figure8 shows the cumulative distribution function (cdf) of the variations in
RMS delay spread for LOS and NLOS with two different thresholds.
(The arrival time of the first path can be found by
applying a threshold to the peak of the received signal. For our study, we used
two different thresholds (e.g., 15 dB and 20 dB)).Figure 8
CDF of RMS delay spread values over LOS and
NLOS cases.The maximum variations in the RMS delay spread for LOS
environment were about 11.8 nanoseconds for 15 dB and 23.6
nanoseconds, for 20 dB. For NLOS environment, they
were about 29.07 nanoseconds (15 dB) and 44.38 nanoseconds (20 dB). This
indicates that NLOS channels are subject to high-spatial variations of the RMS
delay spread. This could be attributed to the presence of more scatterers, and
hence more paths in NLOS environments than in LOS environments. The variation
of the mean excess delay also indicates a similar pattern. Figure9 shows the
CDF of the mean excess delay. The variations of RMS and mean excess were summarized in Table 1.Table 1
Average values ofτrms and τm (in nanosecond).
τrmsτmμτrmsστrmsμτmστmLOS (15 dB)11.84.422.613.4LOS (20 dB)23.65.1433.765.72NLOS (15 dB)29.078.849.4212.04NLOS (20 dB)44.3810.658.308.46Figure 9
CDF of mean excess delay values over LOS and
NLOS cases.


#### 3.2.3. Variation of RMS Delay Spread

Dependence ofτrms on the transmitter-receiver separation is an
important result, which if established can greatly influence system design. A
review of previous works shows disagreement on this topic. Some measurements
for wideband indoor channel have shown correlation between τrms and Tx/Rx separation [17], while others
indicate no correlation [18].The time dispersion parameters of our measurements are
plotted versus transmitter-receiver separation in Figures10 and 11. While some
researchers in UWB channel measurements [9, 19] reported a correlation
between distance and delay spread, no such correlation is evident from our data
sets. Therefore, since these data sets represent a fairly diverse set of
environments, it is concluded that for the specific environments and distances
considered, the physical environment has a much more significant impact on the
time dispersive nature of the channel than the distance. For the underground
gallery considered, random reflections have the effect of flattering the
relationship between RMS delay spread and distance.Figure 10
RMS delay spread versus distance, LOS (15 dB threshold).Figure 11
RMS delay spread versus distance, NLOS (15 dB threshold).The RMS delay spread values are plotted versus the
corresponding mean excess delay values. As shown in Figures12 and 13 no
definite correlation of the variation mean excess delay could be established.Figure 12
RMS delay spread versus mean excess delay,
all channels, LOS (15 dB threshold).Figure 13
RMS delay spread versus mean excess delay,
all channels, NLOS (15 dB threshold).


## 3.1. Large-Scale Modeling

For UWB systems, it was reported that the path loss
modeling can be simplified by assuming that the frequency dependence and the
distance dependence can be treated independently of each other:(1)P(f,d)=PL(f)⋅PL(d).
However, PL(f) was already analyzed in [6]. Hence, in this
paper, we analyze the path loss distance dependence.


### 3.1.1. Path Loss Distance Dependence

The path loss
of the channel represents the attenuation a signal undergoes when transmitted
through the medium. The frequency response obtained from the VNA was referenced
to 1 m for LOS and 5 m for NLOS, as described in the measurement procedure.
Using the measured frequency response data, the average path loss at any
distanced can be estimated using the following
equation:(2)Pl(d)=1NM∑i=1N∑j=1M|H(fi,fj;d)|2,where H(fi,fj;d) denotes the jth channel transfer function snapshot at
frequency fi and at distance d, N is the number of observed frequencies, and M is the number of frequency response snap shots
over time. It is well known that the mean path loss referenced to a distance d0 can be modeled as a function of distance
using:
(3)Pl(d)=Pl0+10nlog10(dd0)+S(d),d≥d0,where d0 is the reference distance, in our case d0 = 1 m for LOS (d0 = 5 m, for NLOS) and Pl0 is the interception point, n is referred to as the path loss exponent, and S is the shadowing fading parameter that varies
randomly from one location to another.Equation (3) was used to compute path loss for all of
the individual power impulse response measurements and for each of the
spatially averaged power impulse response.In this paper, the average power for each measurement
is calculated by averaging the power over all sample points of the measured
frequency response. Using linear regression analysis, the minimum mean square
error (MMSE) line is calculated for the dependence of average power (dB) on the
logarithm of the distance for each global experiment. The slope of the
regression line gives the experimental value ofn.Figure5 shows a scatter plot of received power (dB)
versus distance on a log scale for the measurement and the MMSE line fitted to
the data. The linear regression analysis gives n = 1.47 for LOS and n = 2.45 for NLOS.Figure 5
Scatter plot of path loss versus Tx/Rx separation distance.


### 3.1.2. Shadow Fading

Due to variations in the surrounding environments,PL observed at any given location
will deviate from its average value. This phenomenon is called shadow fading
and has been reported by many measurements [9–15] to follow a
log-normal distribution. In   (4), the shadow
fading parameter is given by the term S that varies randomly from one location to
another. It is a zero-mean Gaussian-distributed random variables in dB with
standard deviation σs which is also in dB.The statistical analysis ofS gives σS = 1.1 for LOS and σS = 2.94 for NLOS, respectively. The cumulative
distribution function (cdf) of the deviation between the fitted and measured
data is plotted versus the normal cdf (Figures 6 and 7). These curves show that
the shadow fading is lognormally distributed.Figure 6
CDF of shadowing fading fit for normal
distribution-LOS scenario.Figure 7
CDF of shadowing fading fit for normal
distribution-NLOS scenario.


## 3.1.1. Path Loss Distance Dependence

The path loss
of the channel represents the attenuation a signal undergoes when transmitted
through the medium. The frequency response obtained from the VNA was referenced
to 1 m for LOS and 5 m for NLOS, as described in the measurement procedure.
Using the measured frequency response data, the average path loss at any
distanced can be estimated using the following
equation:(2)Pl(d)=1NM∑i=1N∑j=1M|H(fi,fj;d)|2,where H(fi,fj;d) denotes the jth channel transfer function snapshot at
frequency fi and at distance d, N is the number of observed frequencies, and M is the number of frequency response snap shots
over time. It is well known that the mean path loss referenced to a distance d0 can be modeled as a function of distance
using:
(3)Pl(d)=Pl0+10nlog10(dd0)+S(d),d≥d0,where d0 is the reference distance, in our case d0 = 1 m for LOS (d0 = 5 m, for NLOS) and Pl0 is the interception point, n is referred to as the path loss exponent, and S is the shadowing fading parameter that varies
randomly from one location to another.Equation (3) was used to compute path loss for all of
the individual power impulse response measurements and for each of the
spatially averaged power impulse response.In this paper, the average power for each measurement
is calculated by averaging the power over all sample points of the measured
frequency response. Using linear regression analysis, the minimum mean square
error (MMSE) line is calculated for the dependence of average power (dB) on the
logarithm of the distance for each global experiment. The slope of the
regression line gives the experimental value ofn.Figure5 shows a scatter plot of received power (dB)
versus distance on a log scale for the measurement and the MMSE line fitted to
the data. The linear regression analysis gives n = 1.47 for LOS and n = 2.45 for NLOS.Figure 5
Scatter plot of path loss versus Tx/Rx separation distance.


## 3.1.2. Shadow Fading

Due to variations in the surrounding environments,PL observed at any given location
will deviate from its average value. This phenomenon is called shadow fading
and has been reported by many measurements [9–15] to follow a
log-normal distribution. In   (4), the shadow
fading parameter is given by the term S that varies randomly from one location to
another. It is a zero-mean Gaussian-distributed random variables in dB with
standard deviation σs which is also in dB.The statistical analysis ofS gives σS = 1.1 for LOS and σS = 2.94 for NLOS, respectively. The cumulative
distribution function (cdf) of the deviation between the fitted and measured
data is plotted versus the normal cdf (Figures 6 and 7). These curves show that
the shadow fading is lognormally distributed.Figure 6
CDF of shadowing fading fit for normal
distribution-LOS scenario.Figure 7
CDF of shadowing fading fit for normal
distribution-NLOS scenario.


## 3.2. Dispersions Properties of UWB Channel

The dispersive
properties of the UWB channel can be considered as an extension of the
large-scale study of the channel. There are two parameters to be characterized
and analyzed. These parameters are the RMS delay spreadτrms and mean excess delay τm.
These two important parameters are used to characterize the temporal dispersive
properties of the multipath channels. This is useful to estimate the
performance and potential for ISI.


### 3.2.1. Mathematical Model

The discrete
mathematical model used for the complex impulse response is that of a linear
filter with variable complex weights and variable delays and is given
by(4)h(d,t,τ)=∑k=1Nak(d,t)δ(t−tk(d,t))ejθk(d,t),where N is the number of resolvable multipath
components, ak, tk, θk are random amplitude, delay time
(arrival-time), and phase sequences, respectively, and δ is the Dirac delta function.By obtaining adequate statistical modeling of these
random variables, we can offer a complete description of the multipath channel.d represents the measurement location, while t is the measurement instant. While d varies according to different locations, t will not be taken into consideration since
measurements were without interference of any moving objects (persons or
vehicular) and with stationary Tx and Rx.According to this model, the received signal is the
convolution of the transmitted signal with the complex impulse response, added
with the channel noise, usually modeled as a complex Gaussian process.In addition to the statistical information concerning
the above random variables, another important parameter is extracted, namely,
the RMS delay spread (τrms). It is a one-number representation of the
multipath channel computed from its complex impulse response that gives an
estimation of the amount of intersymbol interference (ISI) to be encountered in
the channel.Many researchers have reported results concerning the
measurement ofτrms for UWB channel in indoor environment [9–11], residential [12–14], and in industrial environment [16]. It
is computed using(5)τrms=∑k=1Nak2⋅(τk−τf−τm)2∑k=1Nak2,where τf is the arrival time of the first multipath
component to rise above the predefined threshold, τk is the arrival time sequence of the multipath
components, and τm is the average excess delay computed from  the
formula:(6)τm=∑k=1Nak2⋅(τk−τf)∑k=1Nak2.It is clear for (5) and (6) that τrms is the square root of the second central
moment of the power-weighted delay profile.


### 3.2.2. RMS Delay Spread and Mean Excess Delay

The channel
parameters RMS delay spread and mean excess delay are computed for each
received profile at every location of the receiver in LOS and NLOS cases.
Figure8 shows the cumulative distribution function (cdf) of the variations in
RMS delay spread for LOS and NLOS with two different thresholds.
(The arrival time of the first path can be found by
applying a threshold to the peak of the received signal. For our study, we used
two different thresholds (e.g., 15 dB and 20 dB)).Figure 8
CDF of RMS delay spread values over LOS and
NLOS cases.The maximum variations in the RMS delay spread for LOS
environment were about 11.8 nanoseconds for 15 dB and 23.6
nanoseconds, for 20 dB. For NLOS environment, they
were about 29.07 nanoseconds (15 dB) and 44.38 nanoseconds (20 dB). This
indicates that NLOS channels are subject to high-spatial variations of the RMS
delay spread. This could be attributed to the presence of more scatterers, and
hence more paths in NLOS environments than in LOS environments. The variation
of the mean excess delay also indicates a similar pattern. Figure9 shows the
CDF of the mean excess delay. The variations of RMS and mean excess were summarized in Table 1.Table 1
Average values ofτrms and τm (in nanosecond).
τrmsτmμτrmsστrmsμτmστmLOS (15 dB)11.84.422.613.4LOS (20 dB)23.65.1433.765.72NLOS (15 dB)29.078.849.4212.04NLOS (20 dB)44.3810.658.308.46Figure 9
CDF of mean excess delay values over LOS and
NLOS cases.


### 3.2.3. Variation of RMS Delay Spread

Dependence ofτrms on the transmitter-receiver separation is an
important result, which if established can greatly influence system design. A
review of previous works shows disagreement on this topic. Some measurements
for wideband indoor channel have shown correlation between τrms and Tx/Rx separation [17], while others
indicate no correlation [18].The time dispersion parameters of our measurements are
plotted versus transmitter-receiver separation in Figures10 and 11. While some
researchers in UWB channel measurements [9, 19] reported a correlation
between distance and delay spread, no such correlation is evident from our data
sets. Therefore, since these data sets represent a fairly diverse set of
environments, it is concluded that for the specific environments and distances
considered, the physical environment has a much more significant impact on the
time dispersive nature of the channel than the distance. For the underground
gallery considered, random reflections have the effect of flattering the
relationship between RMS delay spread and distance.Figure 10
RMS delay spread versus distance, LOS (15 dB threshold).Figure 11
RMS delay spread versus distance, NLOS (15 dB threshold).The RMS delay spread values are plotted versus the
corresponding mean excess delay values. As shown in Figures12 and 13 no
definite correlation of the variation mean excess delay could be established.Figure 12
RMS delay spread versus mean excess delay,
all channels, LOS (15 dB threshold).Figure 13
RMS delay spread versus mean excess delay,
all channels, NLOS (15 dB threshold).


## 3.2.1. Mathematical Model

The discrete
mathematical model used for the complex impulse response is that of a linear
filter with variable complex weights and variable delays and is given
by(4)h(d,t,τ)=∑k=1Nak(d,t)δ(t−tk(d,t))ejθk(d,t),where N is the number of resolvable multipath
components, ak, tk, θk are random amplitude, delay time
(arrival-time), and phase sequences, respectively, and δ is the Dirac delta function.By obtaining adequate statistical modeling of these
random variables, we can offer a complete description of the multipath channel.d represents the measurement location, while t is the measurement instant. While d varies according to different locations, t will not be taken into consideration since
measurements were without interference of any moving objects (persons or
vehicular) and with stationary Tx and Rx.According to this model, the received signal is the
convolution of the transmitted signal with the complex impulse response, added
with the channel noise, usually modeled as a complex Gaussian process.In addition to the statistical information concerning
the above random variables, another important parameter is extracted, namely,
the RMS delay spread (τrms). It is a one-number representation of the
multipath channel computed from its complex impulse response that gives an
estimation of the amount of intersymbol interference (ISI) to be encountered in
the channel.Many researchers have reported results concerning the
measurement ofτrms for UWB channel in indoor environment [9–11], residential [12–14], and in industrial environment [16]. It
is computed using(5)τrms=∑k=1Nak2⋅(τk−τf−τm)2∑k=1Nak2,where τf is the arrival time of the first multipath
component to rise above the predefined threshold, τk is the arrival time sequence of the multipath
components, and τm is the average excess delay computed from  the
formula:(6)τm=∑k=1Nak2⋅(τk−τf)∑k=1Nak2.It is clear for (5) and (6) that τrms is the square root of the second central
moment of the power-weighted delay profile.


## 3.2.2. RMS Delay Spread and Mean Excess Delay

The channel
parameters RMS delay spread and mean excess delay are computed for each
received profile at every location of the receiver in LOS and NLOS cases.
Figure8 shows the cumulative distribution function (cdf) of the variations in
RMS delay spread for LOS and NLOS with two different thresholds.
(The arrival time of the first path can be found by
applying a threshold to the peak of the received signal. For our study, we used
two different thresholds (e.g., 15 dB and 20 dB)).Figure 8
CDF of RMS delay spread values over LOS and
NLOS cases.The maximum variations in the RMS delay spread for LOS
environment were about 11.8 nanoseconds for 15 dB and 23.6
nanoseconds, for 20 dB. For NLOS environment, they
were about 29.07 nanoseconds (15 dB) and 44.38 nanoseconds (20 dB). This
indicates that NLOS channels are subject to high-spatial variations of the RMS
delay spread. This could be attributed to the presence of more scatterers, and
hence more paths in NLOS environments than in LOS environments. The variation
of the mean excess delay also indicates a similar pattern. Figure9 shows the
CDF of the mean excess delay. The variations of RMS and mean excess were summarized in Table 1.Table 1
Average values ofτrms and τm (in nanosecond).
τrmsτmμτrmsστrmsμτmστmLOS (15 dB)11.84.422.613.4LOS (20 dB)23.65.1433.765.72NLOS (15 dB)29.078.849.4212.04NLOS (20 dB)44.3810.658.308.46Figure 9
CDF of mean excess delay values over LOS and
NLOS cases.


## 3.2.3. Variation of RMS Delay Spread

Dependence ofτrms on the transmitter-receiver separation is an
important result, which if established can greatly influence system design. A
review of previous works shows disagreement on this topic. Some measurements
for wideband indoor channel have shown correlation between τrms and Tx/Rx separation [17], while others
indicate no correlation [18].The time dispersion parameters of our measurements are
plotted versus transmitter-receiver separation in Figures10 and 11. While some
researchers in UWB channel measurements [9, 19] reported a correlation
between distance and delay spread, no such correlation is evident from our data
sets. Therefore, since these data sets represent a fairly diverse set of
environments, it is concluded that for the specific environments and distances
considered, the physical environment has a much more significant impact on the
time dispersive nature of the channel than the distance. For the underground
gallery considered, random reflections have the effect of flattering the
relationship between RMS delay spread and distance.Figure 10
RMS delay spread versus distance, LOS (15 dB threshold).Figure 11
RMS delay spread versus distance, NLOS (15 dB threshold).The RMS delay spread values are plotted versus the
corresponding mean excess delay values. As shown in Figures12 and 13 no
definite correlation of the variation mean excess delay could be established.Figure 12
RMS delay spread versus mean excess delay,
all channels, LOS (15 dB threshold).Figure 13
RMS delay spread versus mean excess delay,
all channels, NLOS (15 dB threshold).


## 4. Comparisons with UWB and Published Wideband Results

Many measurement
campaigns and channel modeling efforts have been performed to characterize the
UWB channel. Several researchers have performed frequency-domain measurements
using vector network analyzers. Moreover, a few time-domain,
direct-pulse measurement systems have been used for UWB channel
sounding. However, it is difficult to give a global comparison between our
measurement and other published works (depending on the measurement
environments, scenarios, frequency bandwidth, etc.). Hence, in this
following subsection, we are interesting to give an overview about UWB channel
measurement.


### 4.1. Comparison with Other UWB Large-Scale
Modeling

The reported path loss exponents for narrowband
systems are between 1.6 and 1.8 for inbuilding LOS environments and between 4 and 6 for obstructed inbuilding environments [29].The path loss exponents for UWB are comparable with
the path loss exponents for narrowband LOS scenarios, but are smaller for NLOS
scenarios. In [11], the reported path-loss exponent was as low as 1.27 for a
narrow corridor. For LOS and NLOS scenarios, the global path-loss exponents
were found to be nearly 1.6 and 2.7, respectively. According to [12], the PL
exponent and the SDS for LOS are 1.7 and 1.5 dB, respectively, and for NLOS 4.1 and 3.6 dB, respectively.In [14], the reported values of n for Tx/Rx separation ranging from 1 to 20 m
are 1.18, 2.18, 2.48, and 2.69 for LOS (three-bedroom apartment), NLOS
(three-bedroom apartment), LOS (four-bedroom apartment), and NLOS (four-bedroom
apartment), respectively. According to [30], the PL exponent and the standard
deviation of shadowing (SDS) are 2.4 and 5.9 dB, respectively, and the best
model for shadowing is the log-normal distribution. The PL exponent is reported
in [31] as 1.7 and 3.5 and for the SDS as 1.6 dB and 2.7 dB for LOS and NLOS,
respectively. Some other reported values on path loss exponent and standard
deviation of shadowing based on previous measurement campaigns are summarized
in Table 2.Table 2
The reported values on large scale fading based on previous measurement
campaigns.
SourcePath loss exponentShadow fadingFrequency range  (GHz)Range
(m)EnvironmentLOSNLOSLOSNLOSAT&T  [9]1.73.52.84.44.375–5.6251–15ResidentialFrance Telecom  [10]1.623.221.75.53.1–10.61–20OfficeVirginia Tech [11]1.3-1.42.3-2.42.5–32.6–5.6Time
domain5–49 (LOS)2–9
(NLOS)LaboratoryIntel [12]1.724.091.483.632–81–11
(LOS)4–15 (NLOS)ResidentialSamsung
[14]1.82.180.981.433.1–10.61–20ResidentialNew
jersey Institute [15]1.55–1.65–1.77—1.98-1.19-0.77—2–61–10LaboratoryCEA-LETI [20]1.633.68——2–61.6
(LOS)3-20 (NLOS)ResidentialU.C.A.N [21]1.43.2
(soft)4.1
(hard)0.351.21
(soft)1.87
(hard)—4–14OfficeETH
Zurich [22]2.7–3.3 (on body)——3–60.5–1.5Body4.1 (a round the torso)Cassioli/ Molisch/Win
[23]2.04(d<11m)  ,——Time domain8–11, 11–13 (NLOS)Indoor56+74log(d),(d>11m)Oulu Univ.  [24]1.04–1.4–1.83.2–3.3–3.9——2–61–30
(LOS)4–14 (NLOS)IndoorWhyless
[25]1.581.96——1–112–5 (LOS)1-6
(NLOS)OfficeDARPA [26]1.32.32.62.4Time
domain10LaboratoryOulu Univ.  [27]1.62———3.1–63–6 (LOS)HospitalAquila Univ [28]2.5-2.6—2.11–5.23—Time
domain1–25ForestLRCS1.472.451.12.952–51–12 (LOS) 5–12 (NLOS)Underground  mines


### 4.2. Comparison with Other UWB Time
Dispersion Parameters

In the 5–30 m
range, indoor channels are expected to have an RMS delay spread of 19–47
nanoseconds [32] and mean values in the range of 20–30 nanoseconds [33]. For
example, Keignart and Daniele [34] presented their measurements for a maximum
range of 10 m in an indoor UWB channel. They found that their measured RMS
delay spread varies between 14 and 18 nanoseconds which is lower than what was
reported by Hashemi and Tholl [33]. They also found that the mean excess delay increases
when transmitter-receiver antenna separation increases.Some published works [32] suggest that a fairly
conservative RMS delay spread of 25 nanoseconds would be a good initial
starting point for personal area network- (PAN-) type applications with antenna
separations of about 10 m or less.The RMS delay spread seems to follow a normal
distribution [12, 31, 34]. For the LOS case, in [12] μrms = 9 nanoseconds, in [31] μrms = 4.7 nanoseconds, and σrms = 2.3 nanoseconds, and in [34] μrms = 2–12 nanoseconds and σrms = 3.3 nanoseconds are reported. For NLOS case,
in [12] μrms = 11.5 nanoseconds, in [31] μrms = 8.2 nanoseconds and σrms = 3.3 nanoseconds and in [34] μrms = 14–19 nanoseconds and σrms = 1–5 nanoseconds, are reported. Tables 3
and 4 present an illustration of reported results in the literature for the RMS
delay spread, τrms,
the mean excess delay, τm.Table 3
Results about time-domain parameters
in previous measurement campaigns for LOS case.
SourceτrmsτmFrequence
range (GHz)Range(m)EnvironmentAT&T [9]4.7—4.375–5.6251–15ResidentialFrance Telecom [10]4.1—3.1–10.61–20OfficeVirginia Tech [11]5.415.19Time domain5–49LaboratoryIntel [12]932–81–11ResidentialSamsung [14]12.48–145.01–5.883.1–10.61–20ResidentialNew Jersey Inst. [15]17.3411.862–61–10LaboratoryMitsubishi [16]28–31—3–102–8IndustrialCEA-LETI [20]10.076.422–64LaboratoryIKT, ETH Zurich [22]1.4–2.1,
2.2–7.51.2–1.4,
4.7–11.30.5–1.53–6BodyAquila Univ. [28]5–111–4.5Time domain1–25ForestLRCS11.822.612–51–12Underground
minesTable 4
Results about time-domain parameters
in previous measurement campaigns for NLOS case.
SourceτrmsτmFrequence range (GHz)Range (m)EnvironmentAT&T [9]8.2—4.375–5.6251–15ResidentialFrance Telecom [10]9.9—3.1–10.61–20OfficeVirginia Tech [11]13.716Time domain2–9LaboratoryIntel [12]11.5102–84–15ResidentialSamsung [14]26.51–38.6124.95–36.093.1–10.61–20ResidentialCEA-LETI [20]14.7816.012–64–10LaboratoryIKT, ETH Zurich [22]1.4–2.1,
7.3–9.91.1–1.3,
5.5–18.13–60.15–0.28BodyLRCS29.0749.422–55–12Underground
mines


### 4.3. Comparisons with Narrowband and
Wideband Channel in Underground Mines

Other results
have been published for the characterization of narrowband and wideband channel
in the underground mine environment. Nerguizian et al. [35] found that the path
loss is equal to 2.16 with a standard deviation of 6.13; the shadow fading was
not presented. The authors also showed that the RMS delay spread is about 27
nanoseconds with a 23.5-standard deviation. Liénard and Degauque [36]
have observed RMS delay spreads of 19 nanoseconds in LOS conditions in their
underground mine gallery and of 25 nanoseconds to 42 nanoseconds in NLOS
conditions. Hämäläinen et al. [37] have observed that the total delay spread
does not exceed 500 nanoseconds.


## 4.1. Comparison with Other UWB Large-Scale
Modeling

The reported path loss exponents for narrowband
systems are between 1.6 and 1.8 for inbuilding LOS environments and between 4 and 6 for obstructed inbuilding environments [29].The path loss exponents for UWB are comparable with
the path loss exponents for narrowband LOS scenarios, but are smaller for NLOS
scenarios. In [11], the reported path-loss exponent was as low as 1.27 for a
narrow corridor. For LOS and NLOS scenarios, the global path-loss exponents
were found to be nearly 1.6 and 2.7, respectively. According to [12], the PL
exponent and the SDS for LOS are 1.7 and 1.5 dB, respectively, and for NLOS 4.1 and 3.6 dB, respectively.In [14], the reported values of n for Tx/Rx separation ranging from 1 to 20 m
are 1.18, 2.18, 2.48, and 2.69 for LOS (three-bedroom apartment), NLOS
(three-bedroom apartment), LOS (four-bedroom apartment), and NLOS (four-bedroom
apartment), respectively. According to [30], the PL exponent and the standard
deviation of shadowing (SDS) are 2.4 and 5.9 dB, respectively, and the best
model for shadowing is the log-normal distribution. The PL exponent is reported
in [31] as 1.7 and 3.5 and for the SDS as 1.6 dB and 2.7 dB for LOS and NLOS,
respectively. Some other reported values on path loss exponent and standard
deviation of shadowing based on previous measurement campaigns are summarized
in Table 2.Table 2
The reported values on large scale fading based on previous measurement
campaigns.
SourcePath loss exponentShadow fadingFrequency range  (GHz)Range
(m)EnvironmentLOSNLOSLOSNLOSAT&T  [9]1.73.52.84.44.375–5.6251–15ResidentialFrance Telecom  [10]1.623.221.75.53.1–10.61–20OfficeVirginia Tech [11]1.3-1.42.3-2.42.5–32.6–5.6Time
domain5–49 (LOS)2–9
(NLOS)LaboratoryIntel [12]1.724.091.483.632–81–11
(LOS)4–15 (NLOS)ResidentialSamsung
[14]1.82.180.981.433.1–10.61–20ResidentialNew
jersey Institute [15]1.55–1.65–1.77—1.98-1.19-0.77—2–61–10LaboratoryCEA-LETI [20]1.633.68——2–61.6
(LOS)3-20 (NLOS)ResidentialU.C.A.N [21]1.43.2
(soft)4.1
(hard)0.351.21
(soft)1.87
(hard)—4–14OfficeETH
Zurich [22]2.7–3.3 (on body)——3–60.5–1.5Body4.1 (a round the torso)Cassioli/ Molisch/Win
[23]2.04(d<11m)  ,——Time domain8–11, 11–13 (NLOS)Indoor56+74log(d),(d>11m)Oulu Univ.  [24]1.04–1.4–1.83.2–3.3–3.9——2–61–30
(LOS)4–14 (NLOS)IndoorWhyless
[25]1.581.96——1–112–5 (LOS)1-6
(NLOS)OfficeDARPA [26]1.32.32.62.4Time
domain10LaboratoryOulu Univ.  [27]1.62———3.1–63–6 (LOS)HospitalAquila Univ [28]2.5-2.6—2.11–5.23—Time
domain1–25ForestLRCS1.472.451.12.952–51–12 (LOS) 5–12 (NLOS)Underground  mines


## 4.2. Comparison with Other UWB Time
Dispersion Parameters

In the 5–30 m
range, indoor channels are expected to have an RMS delay spread of 19–47
nanoseconds [32] and mean values in the range of 20–30 nanoseconds [33]. For
example, Keignart and Daniele [34] presented their measurements for a maximum
range of 10 m in an indoor UWB channel. They found that their measured RMS
delay spread varies between 14 and 18 nanoseconds which is lower than what was
reported by Hashemi and Tholl [33]. They also found that the mean excess delay increases
when transmitter-receiver antenna separation increases.Some published works [32] suggest that a fairly
conservative RMS delay spread of 25 nanoseconds would be a good initial
starting point for personal area network- (PAN-) type applications with antenna
separations of about 10 m or less.The RMS delay spread seems to follow a normal
distribution [12, 31, 34]. For the LOS case, in [12] μrms = 9 nanoseconds, in [31] μrms = 4.7 nanoseconds, and σrms = 2.3 nanoseconds, and in [34] μrms = 2–12 nanoseconds and σrms = 3.3 nanoseconds are reported. For NLOS case,
in [12] μrms = 11.5 nanoseconds, in [31] μrms = 8.2 nanoseconds and σrms = 3.3 nanoseconds and in [34] μrms = 14–19 nanoseconds and σrms = 1–5 nanoseconds, are reported. Tables 3
and 4 present an illustration of reported results in the literature for the RMS
delay spread, τrms,
the mean excess delay, τm.Table 3
Results about time-domain parameters
in previous measurement campaigns for LOS case.
SourceτrmsτmFrequence
range (GHz)Range(m)EnvironmentAT&T [9]4.7—4.375–5.6251–15ResidentialFrance Telecom [10]4.1—3.1–10.61–20OfficeVirginia Tech [11]5.415.19Time domain5–49LaboratoryIntel [12]932–81–11ResidentialSamsung [14]12.48–145.01–5.883.1–10.61–20ResidentialNew Jersey Inst. [15]17.3411.862–61–10LaboratoryMitsubishi [16]28–31—3–102–8IndustrialCEA-LETI [20]10.076.422–64LaboratoryIKT, ETH Zurich [22]1.4–2.1,
2.2–7.51.2–1.4,
4.7–11.30.5–1.53–6BodyAquila Univ. [28]5–111–4.5Time domain1–25ForestLRCS11.822.612–51–12Underground
minesTable 4
Results about time-domain parameters
in previous measurement campaigns for NLOS case.
SourceτrmsτmFrequence range (GHz)Range (m)EnvironmentAT&T [9]8.2—4.375–5.6251–15ResidentialFrance Telecom [10]9.9—3.1–10.61–20OfficeVirginia Tech [11]13.716Time domain2–9LaboratoryIntel [12]11.5102–84–15ResidentialSamsung [14]26.51–38.6124.95–36.093.1–10.61–20ResidentialCEA-LETI [20]14.7816.012–64–10LaboratoryIKT, ETH Zurich [22]1.4–2.1,
7.3–9.91.1–1.3,
5.5–18.13–60.15–0.28BodyLRCS29.0749.422–55–12Underground
mines


## 4.3. Comparisons with Narrowband and
Wideband Channel in Underground Mines

Other results
have been published for the characterization of narrowband and wideband channel
in the underground mine environment. Nerguizian et al. [35] found that the path
loss is equal to 2.16 with a standard deviation of 6.13; the shadow fading was
not presented. The authors also showed that the RMS delay spread is about 27
nanoseconds with a 23.5-standard deviation. Liénard and Degauque [36]
have observed RMS delay spreads of 19 nanoseconds in LOS conditions in their
underground mine gallery and of 25 nanoseconds to 42 nanoseconds in NLOS
conditions. Hämäläinen et al. [37] have observed that the total delay spread
does not exceed 500 nanoseconds.


## 5. Summary and Conclusion

In this paper, an analysis of large-scale fading and
time dispersion parameters (RMS delay spread and mean excess delay) of the UWB
channel in an underground environment was presented.The measurements were made in a laboratory mine. We
have seen that in the case of large-scale modeling, propagation in an
underground mine can be modeled (path loss exponent, and shadow fading) as
other channel model found in the literature. However, the parameters are quite
different. The path loss exponent was found to be lower than the free-space
attenuation (i.e.,n=2) for the LOS situation because of the
additional received power caused by reflections.Furthermore, we found that the variation of the
received signal power around the theoretical model follows a log-normal
distribution. The mean standard deviation of shadow fading (in dB) was found to
beσS=1.1 for LOS and σS=2.94 for NLOS, respectively.The results show that the values of RMS delay spread
are 11.8 nanoseconds (29.07 nanoseconds) for LOS (NLOS). In both cases, these
values are much smaller compared to those of conventional narrowband systems in
similar environments. This means that high-data rates can be achieved in
underground mines using UWB. However, unlike some interesting applications
using UWB (e.g., tracking and geolocation) which need low to moderate date
rates, others applications (e.g., IP video, wireless camera) which need
high-date rates can be deployed in underground mines. Hence, UWB is considered
as a good candidate for all these applications.In the next few years, UWB will become a viable, competitive wireless technology. In Fact,
UWB-wireless communication can potentially improve process operations, product quality, and productivity,
boost the safety of mining and improve rescue operations during disasters.We have also  seen that there is no correlation between
the RMS delay spread and the Rx/Tx separation. This particular property can be
explained by the direct effect of scatterers in underground mines (summarized
in Section2). These last ones have a much more significant impact on the time
dispersive nature of the channel than the distance between transmitter and
receiver. Hence, for the  underground gallery considered, random reflections
have the effect of flattening the relationship between RMS delay spread and
distance.The results presented herein (with other works) are
currently exploited in the design of wireless local area networks communication
and for localization applications in underground mining environments.



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*Source: 806326-2008-07-21.xml*