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Articial Neural Network for Estimating Millimeter
Wave Channel Sounding Data inside a Bus
Rajeev Shukla ( rs.20ec1103@phd.nitdgp.ac.in )
National Institute of Technology Durgapur https://orcid.org/0000-0002-4693-9799
Abhishek Narayan Sarkar
National Institute of Technology Durgapur
Aniruddha Chandra
National Institute of Technology Durgapur
Jan M. Kelner
Wojskowa Akademia Techniczna im Jaroslawa Dabrowskiego
Cezary Ziolkowaski
Wojskowa Akademia Techniczna im Jaroslawa Dabrowskiego
Tomas Mikulasek
Brno University of Technology: Vysoke uceni technicke v Brne
Ales Prokes
Vysoké učení technické v Brně: Vysoke uceni technicke v Brne
Research Article
Keywords: power-delay-prole, tap delay gain, intra-vehicle communication, articial neural network,
mmWave channel sounding
Posted Date: August 23rd, 2022
DOI: https://doi.org/10.21203/rs.3.rs-1880257/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
RESEARCH
Artificial Neural Network for Estimating
Millimeter Wave Channel Sounding Data inside a
Bus
Rajeev Shukla1* , Abhishek Narayan Sarkar1, Aniruddha Chandra1, Jan M Kelner2
, Cezary Ziolkowski2, Tomas Mikulasekt3and Ales Prokes3
*Correspondence:
rajeev2504s@gmail.com
1ECE Department, National
Institute of Technology, M. G.
Avenue, 713209 Durgapur, India
Full list of author information is
available at the end of the article
Abstract
Radio wave propagation in an intra-vehicular environment is markedly different
from other well studied indoor scenarios such as an office or a factory floor.
Millimeter Wave (mmWave) based intra-vehicular communications promises large
bandwidth and can achieve ultra-high data rate with lower latency. However,
exploiting the advantages of mmWave communications largely relies on proper
characterization of the propagation channel. Channel characterization is most
accurately done through an extensive channel sounding, but due to hardware and
environmental constraints, it is impractical to test channel condition for all
possible transmitter and receiver locations. In this paper, we use artificial neural
network to aid channel sounding. Based on some real-world sounding data we
show that it is possible to accurately estimate channel transfer function (CTF)
and power delay profile (PDP) in an intra-bus scenario. Such artificially generated
models can help in extrapolation in other relevant scenarios for which
measurement data is unavailable. The proposed model can also be used for
tapped delay line based bit-error-simulations as well.
Keywords: power-delay-profile; tap delay gain; intra-vehicle communication;
artificial neural network; mmWave channel sounding
Introduction
Intra-Vehicular Communication (IVC) is categorized under two categories: wired
and wireless. With the advancement in technologies and increase in the consumer-
friendly market, number of features and technologies incorporated inside the vehi-
cle has increased considerably. In this scenario, wired connections between different
modules of communication in the vehicles get cumbersome as it raises design, man-
ufacturing, and installation issues. Wireless IVC systems have high-speed duplex
data links, which are better equipped to handle multiple tasks through one system,
thus providing more convenience, simplified design, and hassle-free installation. But
with scurrying advancements in IC fabrication more and more technologies are now
available to incorporate inside a vehicle. Ranging from GPS to centralised enter-
tainment and infotainment are now indigenous inside a vehicle.
With fuel prices, global warming on the rise, and so is the increasing number
of vehicles on the road, restrictions on private vehicles and increased use of pub-
lic vehicles can be a new normal. Such restrictions will bring a check on pollution
levels and traffic congestion. These restrictions will increase the demand for public
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Shukla et al. Page 2 of 11
vehicles for daily commute. While commuting through these public vehicles, people
like to spend their time on mobile devices on various platforms like gaming, media,
entertainment, news, and infotainment in the modern-day scenario. With advance-
ments in data networks, these platforms have become more engaging, increasing the
demand for on-demand high data-rate for various data network services [1].
Among the public transport vehicles, a bus is one of the most trivial modes of
commute in urban and semi-urban areas. Considering this fact, this paper provides
an artificial neural network (ANN) based channel sounding model for evaluating
various channel parameters helpful in designing a reliable channel model inside a
bus. With the evolution of the fifth generation wireless network, millimeter wave
(mmWave) bands from (24 - 300 GHz) promise to provide the required high data
rate, high bandwidth, and low latency network[2]. Intra-vehicular mmWave com-
munication has excellent prospects in providing uninterrupted data services in a
high traffic density environment without compromising bandwidth, data rate, or
spectral efficiency [3]. Consequently, the paper comprises mmWave-based channel
sounding using ANN in the intra-bus scenario.
Personal vehicles and large vehicles were the prime focus for mmWave channel
measurement campaigns. The current article is an extension of the comprehensive
channel sounding measurement campaign by researchers at Brno University, Czech
Republic [1]. It was the first mmWave channel measurement campaign inside a bus
at 60 GHz millimeter wave band. Other measurement campaigns inside a bus are
reported in [4,5,6] at narrowband and in [7] at ultrawideband.
Contributions
•We propose an ANN approach to synthetically generate channel transfer func-
tion (CTF) for a given set of input parameters. The CTF, in turn, is used to
obtain power delay profile (PDP) which can be readily used for characterizing
intra-bus mmWave propagation.
•We derived a tapped delay-line (TDL) channel model from the synthetically
generated PDPs. Different error measures show that the simulated model
tallies well with the TDL model directly obtained by sampling the measured
PDP.
Background
Channel Measurement
As per our knowledge, till 2015, the intra-bus channel measurement campaign was
concentrated basically in the sub-6 GHz range. In 2016, an extensive 60 GHz mea-
surement campaign inside the bus was carried out at Brno University, Czech Re-
public, and was reported in [1,8,9].The bus being the most common and cheap
mode of public transport for intercity and intra-city travel,it is the primary focus of
our channel sounding case study. As illustrated in 1, the basic idea behind channel
measurement was to enable the commuting public with onboard gigabyte wire-
less networks using a common vehicular access point (AP) inside the bus, which
is wirelessly connected to roadside units or base stations through the vehicle-to-
infrastructure (V2I) mobile networks.The AP will act as a transceiver and connects
the passenger devices to outside world giving access to various on-demand high data
rate services over static intra-vehicular channels.
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Measurement Set-up
Here the experimental setup consists of an analog signal generator (model: Agilent
E8257D), a scalar signal analyzer (model: Rohde and Schwarz FSUP50), a pair of
mmWave antennas, a power amplifier (PA), a mixer, a DC power supply, adapters,
and connecting cables(Fig.1). The signal generator sweeps all the frequencies from
55 GHz to 65 GHz in the transmitter section. This frequency bandwidth keeps
a step size of 10MHz, generating 1001 data points in each sweep. The generator
output power is set to 13 dBm. The output signal is carried through a 2.5m long
phase-stable coaxial cable(model: MegaPhase TM67) to the power amplifier (model:
Quinstar QPW 50662330) through standard 1.85 mm adapters. The power amplifier
has a gain of 31 dBm, which is high enough to compensate for the cable loss as
well as it also boosts the signal fed to the open-waveguide antenna(OWGA). A DC
power supply powers the Power amplifier and its accessories. A substrate-integrated-
waveguide slot antenna (SIW SA) intercepts the signal and sends the received signal
to the signal analyzer at the receiving end. An external mixer converts the signal
to an intermediate frequency; it also helps in avoiding high cable loss at mmWave
frequencies. The GPIB cables connect the two hardware at the transmitting and
receiving sections for synchronization. To extend the Tx and Rx distance, we can
cascade several GPIB cables together.
The whole measurement setup is placed inside a 50-seater Mercedes Benz
Tourismo bus to conduct the measurement experiments. Performance evaluation
of the 60 GHz downlink channel is carried out over 10 GHz (55-65 GHz) wide band-
width. To create a real-time scenario transmitter is placed near the ceiling at the
front end of the bus, and the receiver is placed in the seat’s drop-down tray to
imitate a hand-held device. The receiver location was changed to different seats to
cover the entire interior space of the bus in the presence and absence of passengers.
Deep Learning
Communication channel models are used for designing cum optimizing communi-
cation systems and also evaluating their system performance via simulations. For
radio channel modeling, empirical or deterministic models are used conventionally.
Empirical channel models, such as the COST-231 model , are derived by giving mea-
surement campaign data as input to the simplistic mathematical models. Although
these models can be used practically, they show very significant deviations from the
actual received channel output parameters such as path loss values , making them
unreliable. Commonly used deterministic models include ray tracing, the Finite-
Difference Time-Domain method or the vector parabolic equation method. Among
the various deterministic models, ray tracing has been widely used to calculate the
radio channel characteristics due to its very high accuracy compared to other de-
terministic models [10]. But these site-specific-physics-based models requires large
computational resources and highly skilled experts for real-time scenarios. This cre-
ates huge bar-barrier along with substantial simulation time and memory required
to trace all the ray paths, when the number of scattering objects and ray intersec-
tions within the simulated space increases [11].
Modern channel modelling aim to overcome these limitations by integrating DL
algorithms which are capable of learning and estimating radio propagation chan-
nel parameters [12]. More particularly, artificial neural networks (ANNs) have been
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Shukla et al. Page 4 of 11
widely used in an effort to replace ray tracing simulators. Artificial neural net-
works help us to imitate human brain by allowing simple learning components to
perform certain computational operations and connect it to other learning compo-
nents. The learning components are referred to as nodes,artificial neurons or just
neurons. These neural networks ”learn” or specifically train themselves from the
data from the measurement campaign,after this for a given input channel parame-
ters the approximate channel behaviour can be estimated [13].
ANN Based DL Model
In ANN or Multi-Layer Perceptrons(MLPs) neurons are arranged into layers, where
each neuron has input connections originating from the previous layer and output
connections pointing towards the next layer. Any typical MLP consists of an input
layer, a number of hidden layers, and an output layer. Here in our ANN there
are four hidden layers between the input and output layer.The input layer has six
neurons for accepting and representing the six input parameters x1, x2, x3, x4, x5, x6
and the output layer has two neurons for two outputs y1, y2.Commonly used input
features of DL channel propagation models are the operating frequency and the Tx-
Rx distance. Other than this there are other categorical input features used here
which are Seat Number where the receiver is present, whether the receiver is present
at the window or aisle seat, whether the receiver is present at the left or right seat
and other miscellaneous information. The hidden layers as well as the output layer
consist of neurons that apply transformations to their input data. Specifically, the
output, s(l)
j, of the j-th neuron in the l-th layer is computed by applying a activation
function, ρh(x) or ρo(x) ,to the weighted sum of the previous layer neuron outputs,
plus a bias term, b(l)
jas shown in (1)
s(l)
j=ρ(
n(l−1)
X
i
wij s(l−1)
i+b(l)
j) (1)
where s(l−1)
iis the output of the i-th neuron of the previous layer, wi,j is the weight
associated with s(l−1)
iand n(l−1) is the number of neurons in the previous layer.
The number of the neurons in the first, second, third and fourth hidden layers
are 20, 50, 100, 200 respectively. the activation function which we are using in the
hidden layer neurons is “ReLU”(Rectified Linear Units) activation function ρh(x)
while the activation function used in the output layer neurons is “Sigmoid” ρo(x)
as shown in (2) and (3)[13].
ρh(x) = (x, if x≥0
0,if x < 0)(2)
ρo(x) = 1
1 + e−x(3)
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Learning Process
Here we are using the ”Adam” optimizer.Adam optimizer is the extended version
of stochastic gradient descent which could be implemented in various deep learning
applications.In Adam instead of adapting learning rates based on the average first
moment as in RMSP, Adam makes use of the average of the second moments of
the gradients. Adam. This algorithm calculates the exponential moving average of
gradients and square gradients. And the parameters of β1 and β2 are used to control
the decay rates of these moving averages. Adam is a combination of two gradient
descent methods, Momentum, and RMSP.
The method is really efficient when working with large problem involving a lot of
input data parameters. It requires less memory and is efficient.We run this model on
the train dataset (X-train, Y-train) and validation dataset(X-val,Y-val) to train our
neural network and also for tuning its hyperparameters. The training and validation
datasets are run with 100 epochs[14].During each epoch the weights are updated by
using (4)
wt+1 =wt−ˆmt(α
√vt+ǫ) (4)
where
α=learning rate(10−3)
ǫ=a small positive constant(10−8)
ˆmtand ˆvtare the bias-corrected values which are calculated by using (5),(6) and
(7),(8) respectively,
mt=β1mt−1+ (1 −β1)[ δL
δwt
] (5)
ˆmt=mt
1−β1
(6)
and,
vt=β2vt−1+ (1 −β2)[ δL
δwt
]
2
(7)
ˆvt=vt
1−β2
(8)
Here,
mt=aggregate of gradients at time t [current] (initially, mt= 0)
vt=sum of square of past gradients(initially, vt= 0)
β1=Moving average parameter (constant, 0.9)
β2=Moving average parameter (constant, 0.999)
δL=derivative of Loss Function
δwt=derivative of weights at time t
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Proposed Methodology
For the measurement campaign in [1,8,9], due to distance constraints, instead
of using a virtual network analyzer (VNA), the authors used a signal analyzer.
The resulting output was the channel transfer function (CTF) amplitude with-
out phase information. As the generation of the power delay profile is needed, the
phase information of CTF is necessary; Hilbert Transform (HT) was used to convert
amplitude-only information into complex information H(f) which is represented as
shown in (9) and (10) [15]
loge(|Hc(f)|)Hilber t T r anfor m
−−−−−−−−−−−−→ 6Hc(f) (9)
Hc(f) = |Hc(f)|6Hc(f) (10)
Using inverse fast Fourier transform (IFFT), time domain channel response can be
procured easily by using (11)
Hc(f)IF F T
−−−−→ hc(t)(.)2
−−→ |hc(t)|2(11)
In addition to the above methodology, we propose a deep learning-based (Fig.2)
for generation of CTFs. Here, dynamic parameters extracted from the measurement
campaign like frequency, transmitter-receiver distance, receiver position, and pres-
ence of passengers are fed as input to an ANN model. The ANN model constitutes
a decoder-like Multi- Layer Perceptron(MLP) which is trained with the extracted
parameters(Fig.3). The model generates simulated values of amplitude and phase
information of the CTFs from the given input parameter values. In post-processing
stage the simulated and measured CTF values are compared. The measured and
simulated PDPs are calculated from respective CTFs and comparison is drawn be-
tween them.To furthur validate the results an average PDP trend is generated which
is then sampled at different delays to get tap gains for the TDL model.
Model Validation
The measured and simulated PDPs respectively. To further validate our model, we
sample the continuous measured PDPs into discrete PDPs to derive an equivalent
TDL model for the measured data and compare it with the simulated PDP trend.
Results and Discussion
Sources of Errors
Intra-vehicular scenarios are highly clustered and complex environment. Application
of ANN (or any other ML algorithms) in such an environment are prone to high
amount of errors [16] . For example, the presence or absence of line-of-sight(LOS)
component greatly influence the performance of ANN models [17] . Higher number
of LOS components means more accurate are the ANN based models. Similarly,
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Algorithm 1 Proposed Algorithm
Require: Pre-Trained ANN Model
Input:
Model Parameters
1 Frequency
2 Tx-Rx Distance
3 Seat Number
4 Window or Aisle
5 Left Side or Right Side
6 Miscellaneous/Others
Training Parameters
1 Measured value of Amplitude of H(f)
2 Measured value of Phase of H(f)
Initialization: Trained Model.
for i=1:length(”Merged Dataset”) do
current −datatset ←ith −dataset
T rained −Model ←current −dataset
Output −P arameter s ←T r ained −M odel
end for
Output:
1 Simulated Values of Amplitude of H(f)
2 Simulated Values of Phase of H(f)
performance of ANN model get affected by the distance and frequency for which
the model is being used. It has been found ANN model are prone to errors when the
receiver is in the vicinity of transmitter [18]. Also, for higher frequency small-scale
fading affects the accuracy of ANN model [19].
Simulation Results
We used the proposed ANN Model across 15 sets of different transmitter-receiver
distance. To investigate the accuracy of ANN based model, comparative analysis
between CTFs extracted from measured data after applying the Hilbert transform
and from simulated CTFs calculated from the proposed ANN model is done. As
shown in Fig.4the trend of simulated CTF extracted from ANN model is approxi-
mately following the upwards and downwards trend of measured CTF values across
the whole bandwidth for a given set of transmitter and receiver(Tx-Rx) distance.
and observed that due to limited number of available datasets it was observed that
the curve tends to overfit at some points.
Tapped Delay Line Model
The dense physical environment inside a bus give rise to highly clustered wireless
channels resulting in distinctive multipaths. Tapped Delay Line models allows to
differentiate between these scattered multipath components with each tap repre-
senting a single component. The PDP obtained from such multipath channels can
be assumed as time average of tap gains at corresponding tap delays.
As evident from Fig.6when a time-average trend for measured PDP was simu-
lated, the average trend closely follows the simulated PDP trend. This time-averaged
PDP trend when sampled at different tap delays and compared with sampled simu-
lated PDPs we found that the difference between the tap gains for average measured
PDP and simulated PDP is quite low(Fig.7). Thus, we can advocate that the sim-
ulated PDP can be considered as an average trend of measured PDP. We further
validated our results with goodness of fit parameters (Table.1) like root mean square
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error (RMSE) and mean absolute error (MAE) by using (12) and (13).
MAE(i) = PNs
i=1|Ppred (:,i) −Pmea(:,i)|
Ns
(12)
RM SE (i) = s|Pmea(:,i) −Ppred(:,i)|2
Ns
(13)
Understanding the Errors
As discussed earlier in the section generating channel characteristics synthetically
using ML algorithms is prone to errors due to the complex and cluttered environ-
ment. Thus, we analysed synthetic channel measurements with measured channel
measurements. Table 1shows the RMSE and MAE error metrics for varying distance
between transmitter and receiver. Table 2shows the variation in channel parame-
ters when the passengers were present inside the bus as well as when passengers are
absent. Here, we would like to add that due to low availability of datasets where
the passengers are present error is high compared to when passengers are absent. It
might also reflects that with increase in number of passengers the number of multi-
path components increased thus showing high errors. We also analysed the trends
for errors with change in distance (Fig.8). Here we observed that the trend for a
particular tap is consistently following the trend of time-averaged error (or differ-
ence) trend which signifies that the model remains consistent across the distance
and time vectors used in the study.
Conclusions
This article proposed a six-layer ANN based framework for 60 GHz wireless commu-
nication link inside a bus. The comprehensive study of frequency domain channel
sounding for mmWave measurement campaign inside a bus led to development of
simulated PDP using dynamic channel parameters. The highly comparable trend
between measured and simulated PDP supports that PDPs can be synthetically
generated using ANN. Comparing the simulated PDP trend with the time-averaged
tap delay gains of the measured PDP indicates that proposed ANN based model
can synthetically generate PDPs for given channel parameters. We also found that
with more availability of measurement datasets (especially in presence of passen-
gers) can significantly improve the model performance. From our observations we
can further implies that the ANN based model can extrapolate the PDP values for
the cases where the channel measurement data is unavailable. Such ANN models
are also helpful for Bit-error rate simulation using tapped delay models.
Abbrevations
mmWave: Millimeter Wave; CTF : Channel Transfer Function; PDP : Power
Delay Profile IVC : Intra-Vehicular Communication; IC : Integrated Circuits; GPS
: Global Positioning; ANN : Artificial Neural Network; GHz : Giga Hertz; TDL :
Tapped Delay Line; AP : Access Point; V2I : Vehicle-to-Infrastructure; DC : Direct
Current ; PA : Power Amplifier; MHz : Mega Hertz; dBm : Decibel-Milliwatts
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OWGA : Open-Waveguide Antenna; SIW SA: Substrate-Integrated-Waveguide
Slot Antenna; GPIB : General Purpose Interface Bus; DL : Deep Learning; MLP
: Multi-Layer Perceptron; ReLU : Rectified Linear Units; RMSP : Root Mean
Sqaure Propagation; VNA : Vector Network Analyzer; ML : Machine Learning;
LOS : Line of Sight; MAE : Mean Absolute Error; RMSE : Root Mean Square
Error;
Availability of data and materials
The data that support the findings of this study are available from the corresponding author, upon reasonable
request.
Competing interests
The authors declare that they have no competing interests.
Funding
This work was developed within a framework of the research grants: project no. 17-27068S sponsored by the Czech
Science Foundation, grant no. LO1401 sponsored by the National Sustainability Program, grant no.
GBMON/13-996/2018/WAT sponsored by the Polish Ministry of Defense, grant no. UGB/22-854/2021/WAT
sponsored by the Military University of Technology, and grant no. CRG/2018/000175 sponsored by SERB, DST,
Government of India.
Author’s contributions
RS conceptualized the work, performed simulations and derived comparatives. ANS wrote the ANN based algorithm
and jointly wrote the manuscript with RS. AC supervised the project. JK, CZ, TM, AP are the foreign collaborators
responsible for obtaining measured datasets along with AC.
Acknowledgements
Not Applicable.
Author details
1ECE Department, National Institute of Technology, M. G. Avenue, 713209 Durgapur, India. 2Institute of
Communications Systems, Faculty of Electronics, Military University of Technology , Warsaw, Poland. 3Department
of Radio Electronics, Brno University of Technology, 61600, Brno, Czech Republic.
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Figures
Figure 1 Experimental Set-up for Bus Measurement Campaign
Figure 2 Block Diagram for Proposed Methodology
Figure 3 Flow Chart for Pre-processing
Figure 4 Measured and Simulated CTF Comparison at 3.66 meter distance inside bus
Figure 5 Measured and Simulated PDP Comparison at 3.66 meter distance inside bus
Figure 6 Average Trend for Measured PDP vs Simulated PDP Trend
Figure 7 Tap Gain Comparison for Measured POP and Simulated PDP Trend
Figure 8 Comparison between Average Tap Gain Difference and Tap Gain Difference at 30ns
Tables
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Table 1 Error Metrics measured and simulated PDPs.
Tx-Rx
Distance
(in meters)
RMSE
(in dBm)
MAE
(in dBm)
1.18 2.737 2.304
1.47 4.083 2.804
1.66 5.094 3.660
2.24 3.617 2.954
2.35 4.264 3.108
3.66 4.060 2.820
3.7 2.800 2.273
5.12 2.313 1.429
5.16 2.757 1.756
6.66 3.213 2.200
6.76 2.522 1.654
8.18 2.263 1.400
8.36 2.664 1.791
9.72 3.821 2.698
9.75 3.439 2.271
Table 2 Effect of presence of passengers
Passenger
Status
MAE
(in dBm)
RMSE
(in dBm)
Empty 2.27 2.80
Half Filled 4.76 5.12
Fully Filled 12.07 12.27
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Measured
Data ANN Model Trained Model Simulated CTF
Test Dataset
TDL Model
(Measured)
PDP
(Measured)
CTF
(Measured)
PDP (Simulated)
Raw Data
Training
Parameter
Extraction
Parameters:
1. Frequency
2. Tx-Rx Distance
3. Seat Number
4. Window/Aisle
5. Left Side/Right
Side
6. Miscellaneous /
Others
Amplitude
input:6
hidden:
100
hidden:
50
hidden:
20
hidden:
200
output: 2
Activation: Relu
Activation:
Sigmoid
Phase
Figure No. 2
