Analysis of Measurement Techniques Used in Channel

Characterization of UWB Wireless Body Area Networks

Bharat Waman Patil

and Ashwini S. Kunte

Dept. of Electronics and Telecommunication, Thadomal Shahani Engineering College, Mumbai, India

Keywords: Wireless Body Area Networks, Channel Modelling, Internet of Things, Ultra-Wide Band, Propagation,

Antennas.

Abstract: The Wireless Body Area Networks (WBAN) has evolved over the past two decades, making a significant

impact on the communication sector, specifically the Internet of Things (IoT) domain, giving new directions

to health monitoring, wellness and assisted living, entertainment, and many more. WBANs are networks of

interconnected devices which are worn, implanted, swallowed, or embedded i.e., in, on and around human

body. As a propagation medium, the human body is complex to study because of its partially conductive

nature and heterogeneous dielectric properties, which makes it a major challenge to characterize its behavior

as a communication channel in the WBAN environment. Further body movements, curvatures and

characteristics of the surrounding environment add further to its complexity. In the last two decades, different

properties of communication channels for radio links used in WBAN have been studied by researchers. The

major challenge for the WBAN is modelling the propagation channels of the radio links for the development

of more accurate and efficient systems. In this research, measurement is a key issue as it helps to quantify the

physical properties of available human body communication channels. Authors have used different methods

like experimentation, numerical techniques and statistical methods for characterizing and evaluation of on

and off-human body radio channels. In this paper, various measurement techniques used by the authors over

the last two decades for characterizing Human Body Channels have been analyzed and discussed their

evolution along with the issues, challenges and future research trends.

1 INTRODUCTION

A wireless network of sensor devices that are either

wearable, implanted, or carried over the human body

at different locations is referred to as a wireless body

area network (WBAN). WBAN development began

around the mid 90s due to its potential use in the area

of wireless personal area network (WPAN)

technologies. The main driving force behind WBAN

development is mainly due to the growing demand for

remote, efficient healthcare systems that are focused

on prevention and early risk detection of fatal

diseases which are normally diagnosed after people

start experiencing the symptoms (Zasowski, Althaus,

et al. , 2003). Moreover, the increasing demand for

low-cost assisted living solutions for the increasing

population of elderly people remains another driver.

WBANs are basically developing as a more advanced

and separate addition to wireless communication in

general. As a wireless communication system, its

basic operating characteristics remain the same as any

other wireless system, but it has many additional

features and specific problems that need to be

addressed separately from other wireless

communication systems. Significant variations

observed in the WBAN radio channels are due to

scattering and dispersive effects along with changes

in the geometry of the human body. Moreover, the

human body is also subjected to many types of

movements, right from breathing to larger

movements during vigorous activity like sports. This

results in significant degradation of WBAN radio

channel behavior. Thus, accurate characterization and

modelling of the communication channels, both in

static and dynamic conditions, is the key issue in the

WBANs, as it not only affects system performance

but also the reliability of the system. Apart from this,

WBAN radio channel link quality also gets affected

due to changed radiation characteristics of the

antenna when located in proximity to the human

body. Hence, these issues have to be well understood

and explored while designing wireless

Patil, B. W. and Kunte, A. S.

Analysis of Measurement Techniques Used in Channel Characterization of UWB Wireless Body Area Networks.

DOI: 10.5220/0013733000004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futur istic Technology (INCOFT 2025) - Volume 3, pages 795-803

ISBN: 978-989-758-763-4

795

communication systems for WBAN, allowing

reliability, maximum channel throughput, and energy

efficiency. Over the last two decades, different

authors have used different methods for

measurement, data processing, and parameter

extraction for evaluation and characterizing on- and

off-body Ultra-Wide Band (UWB) communication

channel models.

2 MEASUREMENT

TECHNIQUES

Presently most of the engineering research is

dedicated to the development of various applications

in IoT domains. Advances in microelectronics

fabrication technologies moving from micro to

nanoscale, along with inexpensive signal processing

systems, cloud computing are already aiding it.

Because of the advances in IoT, further research is

dedicated to the development of personalised

healthcare services for individuals and patients. In

healthcare research, the factors like accuracy, sensing

unreliability, and standardisations are of utmost

importance due to underlying reasons. Wrong results

due to erroneous measurements can affect the

efficiency and reliability of the system under

development. Many factors, like sensors,

measurement equipment, and data processing

techniques used, contribute to actual research in the

development of these systems. The key area of

research in the development of personal healthcare

monitoring and assisted living is the development of

more accurate WBAN channel models. Measurement

is basically a quantitative comparison between the

known and unknown quantity. It helps to quantify the

physical properties of the available WBAN

communication channels. The data obtained with this

can be further processed with different statistical and

numerical techniques for more accurate modelling of

WBAN channels. The following diagram gives the

information about different measurement techniques

used by researchers for characterisation of WBAN

channels.

Figure1: Various methods used for development of UWB-

WBAN channel Models.

The Methods used by the researchers in development

of Channel Models for UWB WBAN networks can

be mainly categorized as:

1) Measurements using Vector Network Analyzer

and post-processing.

2) Measurements using Impulse generator and

Sampling oscilloscope.

3) Simulation study:

a) Finite integration technique (FIT) and

Finite Difference Time Domain (FDTD)

techniques with

i) Heterogeneous voxel-based phantoms.

ii) Homogeneous Layered phantoms.

b) Numerical Simulations with MATLAB.

These methods are discussed in detail in the following

sections.

2.1 Vector Network Analyser

Measurements

Vector Network Analyzer, more popularly known as

VNA, is a very important test and measurement

instrument used in the design and development of RF

and high-frequency systems. VNAs are mainly used

in processes for accurate measurements at high

frequencies and validation of simulation results. It

basically measures network parameters of electrical

networks and is hence named a Vector Network

Analyzer (VNA). Key specifications of it include

frequency range, dynamic range, measurement speed,

and trace noise. It measures scattering parameters,

giving information about transmission and reflection

coefficients of devices under test (DUT). i.e. S

and

parameters, and after reversing the DUT, one can

measure S

and S

also. There are several calibration

standards that can be used in user calibration, which

are short, open, load, and SOLT. It has a built-in

source and receiver also.

This method is a very popular method among the

researchers working on the development of channel

models for UWB WBAN channel characterization,

and most of the literature published uses this method.

After an extensive survey of the published literature,

it is found that there are different techniques that are

used by the authors for the characterization of channel

models. The mostly used technique is after obtaining

the S

data from the measurement campaigns, which

is in the frequency domain, converting it into the time

domain using the inverse Fourier transform (IFT).

(Zasowski, Althaus, et al. , 2003), Thomas

Zosovoski et al. obtained an impulse response using

inverse Fourier transform for the frequency range of

3-6 GHz, and then from this impulse response,

parameters like mean delay spread and path loss are

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796

extracted. The similar approach is used for the

different scenarios for channel characterization in

both line of sight (LOS) and non line of sight (NLOS)

environments. In (Yamamoto and Kobayashi, 2009),

authors examined the effect of the size of rooms on

the channel properties using parameters like

cumulative distribution function (CDF), root mean

square delay, and spatial distributions. In (Chen, Lu,

et al. , 2011), authors used a Hamming window for

sideband reduction, and a complex baseband inverse

Fourier transform is used for obtaining the impulse

response. Parameters like path loss and power delay

profile are used, and a study was carried out in an

anechoic chamber for different body types and 10

sitting positions. Here, 1001 frequency points are

used with full 2-port calibration. In (Särestüniemi,

Tuovinen, et al. , 2013), (Särestöniemi, Tuovinen, et

al. , 2012), authors did analysis in both time and

frequency domains. S

data obtained from VNA is

transformed into impulse response using inverse fast

Fourier transform (IFFT) and compared with

simulated results obtained using the FIT technique.

The results obtained were in good agreement,

validating the applicability of FIT in analysis, which

is similar to the FDTD method but uses the integral

form of Maxwell's equations to solve them in a given

scenario. In (Chen, Ye, et al. , 2012), authors have

used the inverse chirp Z-transform for converting to

the time domain, and the parameters used are time of

arrival, distance measurement error, received signal

strength, and total path loss. Total frequency points

are 1601; over 3-10 GHz, transmitter power used is 0

dBm. study focused on angle-based channel models.

In (Kumpuniemi, Tuovinen, et al. , 2013), authors

used IFFT to transform S

to time domain frequency

2-8 GHz, using 4 test ports, 1601 test points, and 100

sweeps, with an IF bandwidth of 100 MHz and

transmitter power of +10 dBm. Main emphasis on the

effect of different types of antennas, distance error for

large-scale path loss parameters, and detection of all

arriving signals instead of the first arriving path study

carried out in anechoic chambers on one subject for

static positions. In (Gao, Peng, et al. , 2012), the

author tried to evaluate the performance in the

presence of Wi-Fi and WiMAX; here too, initial S

measurements taken with VNA are then transformed

into Channel Transfer Function (CTF) using IFFT,

and then two performance metrics are studied with

the obtained AP model. number of frequency points:

1001 measurements taken for 3.1 to 5.1 GHz. Here

Full two-port calibration is used in VNA. In (Xu,

Gao, et al. , 2013), the same technique is used by the

author with a focus on developing a combined

channel model with realistic combined effects in the

presence of WiMAX. Only standing posture with

fixed transmission distance is considered by the

author in this paper. In (Kumpuniemi, Hämäläinen,

et al. , 2014), on body measurements for 2-8 GHz,

total frequency points taken are 1601 per sweep for

dipole and loop antennas. VNA-based Measurements

were done for Walking sequence with five static

postures and obtained results are transformed to the

time domain with IFFT using MATLAB. A two-

phase study approach is proposed by the author to

develop three different channel models for 2

antennas. In (Yang, et al. , 2014), authors used VNA

measurements taken for 3-10 GHz with a sampling

time of 50 ps. Channel impulse response was

obtained from S

with the inverse discrete Fourier

transform. Main emphasis on development of

nonparametric probability model for NLOS link. In

(Hirose, Kobayashi, et al. , 2014), authors used this

method for a frequency range of 3-10.6 GHz with 751

frequency points. Transmitter power is 0 dBm, and IF

bandwidth is 100 Hz. S

data transformed to time

domain with inverse Fourier transform. Main

emphasis on the effect of room volume on channel

model. In (Serna, Pardo , et al. , 2015), Ruben

Gregorio used this method to study off-body channel

model characterization (3-8 GHz) with two types of

measurement campaigns. One with on body for

different genders for standing posture and the other

without a body. Measured data is converted to the

time domain using inverse Fourier transform. Path

loss and RMS delay spread extracted from channel

impulse response. In (Goswami, Sarma, et al. , 2015),

D. Goswami used the same method for studying the

effect of different size subjects with a different

approach. He averaged measured VNA data for 10

sweeps. Path loss is calculated from attenuation and

IFFT used for obtaining impulse response. Total 1601

sampling points were considered by the authors.

Timo Kumpunemi did the experimentation with VNA

(Kumpuniemi, Hämäläinen, et al. , 2015) and

obtained Channel Impulse Response (CIR) using

IFFT without windowing. Absolute values used for

results. Path loss and standard deviation measured

link by link. First arriving paths data considered.

Christopher Robin used the same approach to

estimate the effect of the surrounding environment on

channel characteristics with a focus on the dense

multi-path component. He extracted reverberation

time from Average PDPs (Roblin, Yunfei, et al. ,

2015). In (Sangodoyin and Molisch, 2017),

(Sangodoyin and Molisch, 2017), authors carried out

VNA measurement campaigns for subjects of

different Body Mass Index (BMI) for investigating

the effect of BMI on channel characteristics and

Analysis of Measurement Techniques Used in Channel Characterization of UWB Wireless Body Area Networks

797

channel capacity with a MIMO sounder and time

domain conversions carried out with IFT with a

Hamming window to reduce side lobes. Parameters

like path loss, frequency-dependent decay factor, and

shadowing gain were considered for results. In

(Kumpuniemi, Mäkelä, et al. , 2017), Timo

Kumpuniemi carried out a study on shadowing

effects on dynamic off-body channels using two

dissimilar antennas with VNA measurements.

Channel impulse response was obtained without

using windowing by the author with IFFT. Authors

cited reasons for not using windowing as loss of

resolution and signal power.

Authors have also used statistical methods for

model characterizations, which are discussed here.

For evaluating the diffraction effects after time

domain conversion with IFFT, curve fitting is done

with correlated lognormal variables, which gives the

effect of reflection and diffraction effects both (Fort,

Desset, et al. , 2006). In (Hao, Alomainy, et al. ,

2006), authors have focused mainly on the

generalization of the model for LOS and NLOS using

the sub-band FDTD technique. A BMI-specific study

was carried out by authors in (Takizawa, et al. , 2008),

where impulse responses obtained with windowing

were then normalized using MATLAB. Based on

actual VNA measurements, two different stochastic

channel models were proposed by authors for average

path loss and PDP. (Takizawa, Aoyagi, et al. , 2009).

Statistical model based on actual measurements

proposed by authors where CTF is obtained from S

later BER and PER examined for different

modulation schemes (Takizawa, Aoyagi, et al. ,

2009). A combinatory approach is used by authors for

qualitative study based on CTF obtained from

sampled S

data from actual VNA measurements

(Roblin, 2010). Statistical parameter estimation was

done by authors based on Poisson's process after

converting measured S

data with real passband

IFFT with a Hamming window (Lu, Chen, et al. ,

2011). Authors used a modified Friis transmission

formula for channel modeling. Calibration was done

at the connectors of the cables for more accurate

results (Ruckveratham, Teawehim, et al. , 2011).

Effect of room volume evaluated by authors in terms

of delay profile and path loss; S

data converted to

time domain with inverse Fourier transform with

rectangular window (Koiwai, Yamamoto, et al. ,

2012). The applicability of the FIT technique is

validated by authors through CST simulations and

actual measurements through comparisons of

Frequency and Impulse Response (Särestüniemi,

2013). Koiwai et al. investigated the effect of

variation due to subjects positions in the room and

ceiling height with spatial distributions and power

delay profiles for both LOS and NLOS using VNA

measurements and post-processing processing

(Koiwai and Kobayashi, 2011).

Along with experimental measurements, authors

have carried out investigations in the frequency

domain. In (Kovacs, Pedersen, et al. , 2004), Estvan

Covas et al. investigated the impact of the human

body on the impedance and radiation characteristics

of the antenna using the FDTD technique. The

investigation of the effect of different sizes of human

arms with the effect of skin layers was done with

measurements and simulation using the FDTD

technique (Lim, Baumann, et al. , 2011). Another

approach used by Ruijun Fu et al. to investigate the

effect of three continuous body motions on

characteristics of dynamic on-body channels. For

VNA measurements, a single-tone waveform is used

for different frequency bands, including UWB, and

later investigated for Doppler shift using Doppler

spread and RMS Doppler bandwidth. This gives good

insight into the effects of motions on propagation

model characteristics (Ruijun, Yunxing, et al. , 2011).

Four on body channels were investigated for body

shadowing effects in real time for multiple time

intervals (Mallat, Zia, et al. , 2018). Here the

experimental model is built with 2X2 MIMO

antennas with Inverted F antenna elements and

measurements were carried out using VNA.

Shadowing effects introduced by body movements

were effectively countered in UWB by MIMO

antennas which were validated with numerical

simulations in terms of improved capacity and

throughput in low SNR scenarios. In (Suwan and

Promwong, 2022) shadowing effects were

investigated using a measurement based model of

impulse radio transmission in an indoor environment.

Here study was based on extracted multi-path

impulse parameters like log normal deviation, decay

factor and cluster and ray arrival rates. Influence of

human breathing on high bandwidth radio channels

was investigated for in, on and off body channels

(Serna, Pardo, et al. , 2020). Effect of relative

movement between the nodes due to breathing was

addressed in terms of Doppler frequency shift effects.

Experimentation was carried out in liquid Phantom.

2.2 Impulse Generator and Sampling

Oscilloscope

Jianqui Teo et al. and Yifun Chen et al. proposed a

new approach for modelling UWB radio channels in

their work (Teo, Chan, et al. , 2007), (Chen, et al. ,

2009). Instead of using the popular method of VNA

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798

measurements (S

) and later converting to the time

domain with post-processing, they used a UWB pulse

generator along with a sampling oscilloscope for

directly making measurements in the time domain.

The reasons cited by authors are that careful

calibration is required for accurate measurements,

and frequency-to-time domain conversion with the

inverse discrete Fourier transform gives a band-

limited version, whereas full bandwidth study is

possible for available paths with the proposed

approach. Mainly focused on the effect of static

postures (Teo, Chan, et al. , 2007) and later extended

to additional postures with an extension to

cooperative schemes. Results obtained are in good

agreement.

2.3 Simulation Study

Angelos Goulians et al did the simulation study for

evaluating multi-path combining effects using first

order non homogeneous Markov model. Numerical

simulation is carried out by the author after doing

measurements of inter-arrival times using spread

spectrum sliding correlator sounder. This is mostly a

qualitative study for limited bandwidth in UWB

range with new parameters like path occurrence

probabilities and clustering coefficients (Goulianos

and Stavrou, 2007). Takeyosh tayamachi et al did the

numerical simulation study for the entire UWB band

using frequency dependent finite difference time

domain method and statistical curve fitting with

MATLAB. This is done using high precision Human

body models for on body LOS channels.

Computational complexity is higher for FDTD

methods (Tayamachi, Wang, et al. , 2007). Qiong

Wang et al carried out a numerical simulation

approach with limited experimentation for validation

in their work which investigated body links on a

human body model derived from average statistical

values of body parameters. Different Body postures

statistical characteristics were considered by authors

in their study (Wang, Tayamachi, et al. , 2009). A

Khaleghi et al used a new approach in numerical

simulation with Finite Integration technique (FIT).

Voxel Human body model with 4mm resolution used

by the authors to study various LOS and NLOS links

where the antennas were located to obtain different

wave polarisations. Channel characteristics for

different polarisation were evaluated in this

qualitative study (Khaleghi and Balasingham, 2009).

Angelos Goulianos et al proposed statistical off-body

channel with the numerical simulation for indoor

environment in a limited frequency band of 3.5-6.5

GHz. Authors modelled signal power gain with a log-

linear dual breakpoint model. Parameters like ray and

cluster arrival times, inter and intra cluster decay rates

extracted for particular antennas are used for model

development (Goulianos, Brown, et al. , 2009).

Masafumii Fujii et al investigated channel models for

static positions with numerical simulations with

FDTD technique based on 2 pole Debye dispersion

model. Human body model for 50 tissues obtained

from Gabriels Cole ole data. Authors used least

square fitting technique over the limited frequency

range with the emphasis on computational efficiency

(Fujii, Yotsuki, et al. , 2010). Tommi Tuovinen et al

did the simulation study on the effect of human body

tissues in terms of impedance and reflection

coefficient with Debye's dispersion model. This was

later extended to other near field parameters like

antenna efficiency radiation pattern in the near field

reactive region by the authors (Tuovinen, Berg, et al.

, 2012), (Tuovinen, Berg, et al. , 2012).

Heterogeneous Voxel based FDTD simulations were

used by authors to investigate dynamic WBAN

channel model characteristics while walking for on,

off and body to body communication in an empty

environment (Mohamed, Joseph, et al. , 2019).

Channel characteristics were studied with reference

to Fade variations and correlated amplitude

distributions. Fade variations for on body channels

were found periodic whereas for off body channels

show constructive and destructive interference with

distance variations. In (Intissar, Sofiane, et al. , 2023)

based on incident Electromagnetic (EM) wave

authors investigated channel characteristics in simple

layered tissue models. Field distributions were

calculated for MOM method and results were

obtained for Path loss and Power density function.

Further validation was done using FEM

computations. In another paper authors have

investigated parametric statistical path loss model for

WBAN for indoor environments (Youssef, Roblin, et

al. , 2024). Simplified Ray tracing code in MATLAB

was used for calculating input parameters for the

environment which was used in CST simulations with

Voxel models. Overall combined approach was used

by authors i.e., EM simulations with RT simulations

which can be helpful in building local models which

can further expanded by adding scattering objects in

an indoor environment.

3 ANALYSIS AND DISCUSSION

After referring to the literature published in the last

two decades on the characterization of channel

models for UWB WBAN systems, using VNA for

Analysis of Measurement Techniques Used in Channel Characterization of UWB Wireless Body Area Networks

799

frequency domain measurements remains the most

popular method among researchers. VNA is highly

sensitive equipment that is widely used in RF and

microwave experimentation, but its calibration and

synchronization are very important before any

measurement campaign for more accurate channel

model development. Time domain conversion of

frequency domain data is done with different inverse

Fourier transform techniques like IFT, IFFT, and

chirp Z transform with and without windowing.

Windowing suppresses side lobes but also drops

signal power and gives band-limited information.

Several statistical methods are used for extracting

different parameters like large-scale path loss and

path gain, which give information about the

attenuation, but their accuracy depends on the

accuracy of the measurement of distance or

separation between the two antennas. Other effects,

like reflection and multi-path effects, are studied in

terms of delay spread, power delay profile, and

amplitude distortion, which are obtained from

channel impulse response. The Doppler technique

with VNA is used by one author where Doppler shift

is measured for phase changes, giving more accurate

results. This approach is new and needs to be

explored more.

Another approach used by authors is

measurements using a UWB pulse generator and time

domain sampling oscilloscope. This method

facilitates direct measurements in the time domain,

which can be later processed to get a CDF based on

power distribution measurements. A parametric study

can be done using it for channel characterization. This

method gives a full-bandwidth view, which is not

possible with the VNA measurement method.

Table1: Measurement techniques used for UWB channel characterization.

Sr. No

Measurement

Technique

use

References Remarks

VNA

Measurements

(Zasowski, Althaus, et al. , 2003), (Yamamoto and Kobayashi,

2009), (Chen, Lu, et al. , 2011), (Särestüniemi, Tuovinen, et al. ,

2013), (Särestöniemi, Tuovinen, et al. , 2012), (Chen, Ye, et al. ,

2012), (Kumpuniemi, Tuovinen, et al. , 2013), (Gao, Peng, et al. ,

2012), (Xu, Gao, et al. , 2013). (Kumpuniemi, Hämäläinen, et al. ,

2014), (Yang, et al. , 2014), (Hirose, Kobayashi, et al. , 2014),

(Serna, Pardo , et al. , 2015), (Goswami, Sarma, et al. , 2015),

(Kumpuniemi, Hämäläinen, et al. , 2015), (Roblin, Yunfei, et al. ,

2015), (Sangodoyin and Molisch, 2017), (Sangodoyin and

Molisch, 2017), (Kumpuniemi, Mäkelä, et al. , 2017), (Fort,

Desset, et al. , 2006), (Hao, Alomainy, et al. , 2006), (Takizawa, et

al. , 2008),

(Takizawa, Aoyagi, et al. , 2009), (Takizawa, Aoyagi, et al. ,

2009), (Roblin, 2010), (Lu, Chen, et al. , 2011), (Ruckveratham,

Teawehim, et al. , 2011), (Koiwai, Yamamoto, et al. , 2012),

(Koiwai and Kobayashi, 2011), (Kovacs, Pedersen, et al. , 2004),

(Lim, Baumann, et al. , 2011), (Ruijun, Yunxing, et al. , 2011),

(Mallat, Zia, et al. , 2018), (Suwan and Promwong, 2022), (Serna,

Pardo, et al. , 2020)

Most popular and

widely used

method.

VNA has higher

sensitivity but

careful calibration

and synchr-

onization is

required.

Frequency domain

data obtained has to

be converted to

time domain data

with different IFT

methods.

With Post

processing

parameters like

Path loss, Path

gain, Delay spread,

Power gain, PDP

extracted.

Impulse

Generator and

Sampling

Oscilloscope

(Teo, Chan, et al. , 2007), (Chen, et al. , 2009) Time domain data

directly measured

which provides full

bandwidth

information.

Using cross

correlation path

loss, PDP, power

variation can be

obtaine

Numerical

Simulations

More flexible for

dynamic models as

different

ostures

INCOFT 2025 - International Conference on Futuristic Technology

800

Sr. No

Measurement

Technique

use

References Remarks

(Goulianos and Stavrou, 2007), (Tadamichi, Wang, et al. , 2007),

(Wang, Tayamachi, et al. , 2009), (Khaleghi and Balasingham,

2009), (Goulianos, Brown, et al. , 2009), (Fujii, Yotsuki, et al. ,

2010), (Tuovinen, Berg, et al. , 2012), (Tuovinen, Berg, et al. ,

2012), (Mohamed, Joseph, et al. , 2019), (Intissar, Sofiane, et al. ,

2023), (Youssef, Roblin, et al. , 2024)

can be easily

simulated.

No need for human

subjects.

High resolution

multipath results

can be obtained

FIT, FDTD or

MATLAB codes

are used.

Time required for

simulation is more.

Surrounding effects

cannot be included.

Several authors have used numerical simulation

techniques with MATLAB curve fitting tools

whereas another approach used is simulation using

either FDTD or FID techniques based on

heterogeneous Voxel based Phantoms or

homogeneous layered Phantoms. Simulation

techniques provide greater flexibility as different

body postures can be easily simulated and

investigated using Voxel based models. Using

statistical average human models works better in

development of generalised channel models without

taking measurements over the number of human

subjects. It is also possible to obtain higher resolution

multi-path results with simulation techniques.

Computational complexity of simulation remains

high and requires more processing time and high end

processing machines. Other drawbacks are

assumption of ideal point sources as radiators and

limitation of considering surrounding effects in

simulations.

4 CONCLUSIONS

Over the last two decades, researchers have used

different methods for evaluation and characterization

of UWB WBAN channel models, like actual

measurements in frequency and time domains,

Doppler shift measurements, and numerical

simulations with MATLAB and other commercially

available simulators. More emphasis was placed on

developing static and pseudo-dynamic channel

models with more accuracy. Different parameters like

path loss/gain, shadowing, multipath fading, delay

spread, power delay profile, average fade duration,

level crossing rate, Doppler frequency shift, etc., are

used by authors for analyzing associated large- and

small-scale effects. In view of developments

happening in the Internet of Things domain,

developing generalized dynamic WBAN radio

channel models still remains the challenge for

researchers and needs to be explored and studied

further for enabling a variety of services and

applications in wellness, medicine, safety, security,

and many more. More realistic study is possible with

experimentation and hence will remain the popular

choice among the researchers for the characterization

of UWB WBAN on- and off-body channels.

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