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A Communication Link Analysis Based on Biological Implant Wireless Body
Area Networks
Yangzhe Liao, Mark S. Leeson, Matthew D. Higgins
School of Engineering
University of Warwick, Coventry, CV4 7AL, UK
Email: {yangzhe.liao, mark.leeson, m.higgins}@warwick.ac.uk
Abstract─ The rapid growth in remote healthcare
services and biomedical demands has seen novel
developments in wireless body area networks
(WBANs). The WBAN can be seen as an integration of
intelligent networks, which permits devices and sensors
to work together to obtain a series of critical
physiological parameters, such as blood flow velocity
and heartbeat frequency. Analysis of WBAN radio
frequency communication systems is the key factor and
the critical research challenge that determines system
performance, such as achievable transmission distance,
data rate and so forth. The human head is an area of
particular potential in WBAN design that is worthy of
attracting more attention than its limited literature to
date. This paper is primarily focused on the one of the
most detailed comprehensive multi-modal imaging-
based anatomical human head models. This is a
multimodal imaging-based detailed anatomical model,
denoted by the acronym MIDA, this features 153
structures at a high resolution of up to 500 μm,
including numerous distinct muscles, bones and skull
layers in the license-free 2.4 GHz industrial, scientific,
and medical (ISM) band. It presents and compares a set
of advanced simulation methods and then proposes a
path loss simulation flat phantom, semi-empirical path
loss models for typical homogeneous tissues and the
anatomical human head MIDA model. The bit error rate
(BER) performances of the MIDA model fading
channel using Binary Phase Shift Keying (BPSK) and
Pulse-Amplitude Modulation (PAM) are obtained.
Furthermore, achievable transmission distances for
several data rates for predetermined acceptable BERs
are accomplished. The results show that PAM promises
longer transmission distances than BPSK when using
both high and low data rates. The proposed
communication systems can be applied to optimize
implantation communication system scenarios and
biotelemetry applications.
Index Terms ─ MIDA Human Head Model, Path Loss,
System Margin, WBANs.
I. INTRODUCTION
Wireless body area networks (WBANs) are
becoming increasingly significant for numerous
applications in e-health and biosensor technology for a
number of reasons, including low power consumption,
simple structure requirements and potentially fast
transmission data rates [1-3]. A typical WBAN can be
regarded as a healthcare network system, which
consists of sensors and other devices on, near or inside
the human body as shown in Fig. 1. Recently, implant
WBANs for biomedical applications have brought
about a revolutionary change due to the development of
antenna technologies and wireless communication
systems [4]. However, surprisingly little work has been
published to date on the proposed use of WBANs for
the human cephalic section, which is the most
significant and urgent area that can cooperate with
future telemedicine technology and electronic medical
services [4, 5].
An important feature in the development of
WBANs is the characterization of the physical layer [4-
7]. The majority of the literature has been concerned
with on-body propagation while fewer studies have
been focused on the modeling of intra-body
propagation subjects [8-10]. Since the human body area
is a natural lossy environment, signals propagating from
the transmitter are attenuated considerably before
reaching the receiver; hence an essential step in the
understanding of implant WBANs is to comprehend the
propagation loss process [11-15]. A path loss (PL)
model is an advantageous approach to help with the
design of wireless communication systems between
nodes located within the human body [13-18]. In [16],
the performance of PL in muscle and fat tissues
individually is obtained using insulated dipole antennas
at 2.45 GHz through a Method of Moments program,
FEKO from EMSS in South Africa. In [17], PL results
have been obtained for an in-body propagation model in
saline water with a Hertzian dipole. A multi-implant
setup at 2.4 GHz has been investigated in [18] using
insulated dipole antennas for specific locations
Fig. 1. Typical architecture of an implant WBAN
system.
including the liver, heart, spleen and kidneys.
Due to the difficulty of specific absorption rate
(SAR) measurements in an actual human head for
electromagnetic (EM) radio-frequency exposure, the
second challenge lies in the examination of human
tissue safety [19, 20]. This involves evaluating health
risks related to exposure to electromagnetic (EM) fields
and making sure that these satisfy the international SAR
compliance regulations, for example those of the
Institute of Electrical and Electronics Engineers (IEEE)
[21] and the International Commission on Non-Ionizing
Radiation Protection (ICNIRP) [22].
Implant WBAN analysis includes propagation loss,
energy consumption, transmission rate, and quality
transmission distance issues [1-5]. Shadow fading
characterizes the variations in the implant WBAN
channel power loss caused by obstacles in the
propagation path [23]. Also, essential requirements
within an implant WBAN channel model are the
capability to support long transmission distances and
high transmission data rates that can effectively connect
and work with future medical servers [24, 25].
The work described in the present paper is intended
to provide valuable insight into implantable
communication systems research. The rest of the paper
is organized as follows. Frequency selection and EM
simulation methods are discussed and selected in
Section II [23, 24, 26-28]. In Section III, a PL
simulation flat phantom is proposed and semi-empirical
PL models for typical homogeneous tissues as well as
the MIDA cephalic model are given. A flat phantom is
utilized to obtain the properties of various typical
homogeneous tissues, in which the signal propagation
attenuation is determined using a transmitting dipole
antenna (Tx) and receiving dipole antenna (Rx). Then
semi-empirical PL models for several typical human
tissues are proposed as well as the MIDA human head
model [29]. This is followed by calculation of the
maximum 10-g SAR distribution [21, 22]. In Section
IV, several performance indicators are determined
using Binary Phase Shift Keying (BPSK) and Pulse-
amplitude modulation (PAM), and the MIDA cephalic
model fading channel. The indicators are the bit error
rate (BER) performance, the minimal signal-to-noise
ratio (SNR) requirement, the system margins and the
achievable transmission distances for several data rates
at a given BER. Finally, Section V presents conclusions
and suggestions for further work.
II. Related work
Selecting the frequency for the WBAN system can
profoundly influence tissue dielectric characterization as
well as implanted antenna size [23-28]. WBAN channels
are primarily proposed in the Medical Implant
Communication Service (MICS), industrial, scientific,
and medical (ISM) and the Ultra Wideband (UWB)
frequency bands or in multi-bands [5]. The MICS
frequency band allows the transmitted signal to suffer
lower attenuation when propagating along an implanted
communication path than the UWB and ISM bands, and
was thus accepted for the IEEE 802.15.6 standard [29].
However, the MICS band is less likely to satisfy future
high data transmission demands; it results in antennas
that are too large and complex to employ in realistic
situations [6, 29-31]. The UWB frequency band is a
promising candidate due to its simple structure, multi-
path fading, and high data speed. Nevertheless, the
weaknesses of UWB are that it only offers short-range of
coverage and it experiences higher energy attenuation [2,
6, 26, 32]. In this paper, the 2.4 GHz band is selected
because it can support higher data rate applications and is
accepted worldwide [6]. The results indicate that higher
transmitted power within safety guidelines can be
obtained. It is also much more likely to be embedded
within the human body due to its small antenna size.
Numerous advanced electromagnetic simulation
technologies have been applied in WBANs [13, 15, 16,
33-35]. In [33], it was pointed out that the Method of
Moments (MoM) is the most effective technology for
planar antenna structures (for example, PCB layers). The
finite element method (FEM) is a useful scheme for
helical antennas along with some simple 3D
constructions [34]. Unlike the MoM and FEM
algorithms, the finite difference time domain (FDTD)
approach [35] is very efficient for solving complex
problems, such as the characterization of antenna
performance when embedded inside multi-layer
surroundings. In addition, FDTD 3D model meshes are
built from Yee Cells so as to deliver high processing
capabilities and reduced memory resources for large 3D
Fig. 2. MIDA 3D model structures of brain white matter (left) and brain gray matter (right). (units in millimeters).
structures [35]. It is important to note that, unlike much
other work, which uses conductivity and relative
permittivity to simulate tissues in the High Frequency
Structure Simulator (HFSS) and other commercial
software [4-5, 12, 16, 41-43], here we employ the CST
Studio Suite® 2015 (Computer Simulation Technology,
Darmstadt, Germany) [36] which takes the loss tangent
parameter into account. Moreover, at the same time
relatively permittivity is automatically considered in the
simulation process, thus allowing the simulation results
to be more accurate [3, 36].
Given the difficulty of experimental investigation of
signal power loss on a real human body, the CST
program has been investigated for solving
electromagnetic issues in this paper. The multimodal
imaging-based detailed anatomical or MIDA model [29]
was obtained by scanning a healthy 29-year old female
volunteer head and neck down to the level of the fifth
cervical vertebra at the Institute for biomedical
engineering laboratory (ETH, Zurich, Switzerland). It is
a detailed anatomical computer model, including 153
kinds of different organs and tissues, with a highest
resolution of 0.5 mm; it is thus more advanced and
accurate than the Virtual Family models [38-40]. The
advances of the MIDA model are not limited to
computational modeling research, but also can be applied
to computational simulations to investigate the safety and
efficacy of medical devices located in, on or around the
head [29]. Fig. 2 shows the brain white matter and brain
gray matter of the MIDA model.
III. PL modeling and human safety analysis
A. Simulation setup
For traditional wireless communication, radio
propagation refers to the process of radio waves suffering
from reflection, diffraction and scattering when they
propagate from the Tx to the Rx [1-4]. However, the
scenario of in-body communication channel is more
complicated and less predictable as the intensity varies
with the lossy environment at diverse locations. As we
are mainly aiming to design communication links within
a human head model, the antenna design issue is not the
central focus of this paper. To the best of our knowledge,
the majority of the proposed antennas for in-body
communication systems use a homogenous tissue (single
layer structure), and are unable to work accurately when
embedded in the brain, which is a multi-layer structure
[16, 17, 42]. Kurup et al. proposed a novel insulated
dipole antenna rather than bare dipoles for WBANs at
2.45 GHz [16]. The method improves the leakage of
conducting charge as well as reducing sensitivity of the
current distribution within the ambient medium.
However, the dielectric parameters of the insulator
material polytetrafluoroethylene are similar to those of
human muscle tissue and can thus affect the simulation
results. In this paper, two identical dipoles are selected
because dipole antennas are simple and well-understood
in free space communications. Additionally, the
dimensions of such antennas are appropriate for them to
be implanted in the body [16, 41]. The two arms of the
dipole antenna (shown in Fig. 3) are both made of perfect
electric conducting (PEC) material with a diameter =
1 mm. The resonance occurs when the antenna is equal
to a half wavelength in a transmission medium, and =
6.25 cm for 2.4 GHz. The simulations use a voltage
Fig. 3. Simulation platform design.
Fig. 4. (left) Conductivity of skin, muscle, brain gray matter and brain white matter from 1-10 GHz. (right) Loss
tangent of skin, muscle, brain gray matter and brain white matter from 1-10 GHz.
source, and the dimensions and simulation methods are
the same for all the cases examined. The proposed flat
phantom is beneficial to understand and compare the PL
performance between several typical human head tissues
Also, antenna design mechanisms can be applied to the
MIDA model. The dielectric parameters are frequency
dependent and can be obtained from thorough survey
published by Andreuccetti et al. [44]. This was compiled
using both data from a range of published papers and
comprehensive measurements by the authors using
several experimental techniques. Fig. 4 demonstrates that
tissue conductivity is monotonically increasing while the
loss tangent goes through a minimum close to 2.5 GHz.
The dielectric parameters of several typical human
tissues at 2.4 GHz, such as conductivity , loss tangent
and relative permittivity , are summarized in
Table. 1 [44].
B. PL analysis
We first investigate wave propagation at 2.4 GHz in
human homogeneous tissues, using simulations for the
proposed implantable antennas. The dielectric parameters
used are those summarized in Table. 1. The simulations
in this paper are all performed using the 3D CST FDTD
electromagnetic solver introduced above. The maximum
Table 1: Dielectric properties of human body typical
tissues at 2.4 GHz [44]
Tissue
Dry skin
1.441
38.063
0.2385
Muscle
1.705
52.791
0.2419
Brain gray matter
1.773
48.994
0.2710
Brain white matter
1.190
36.226
0.2460
grid step in the homogeneous tissues and heterogeneous
MIDA human head model is 1 mm. The simulations are
carried out using the implantable antennas up to 5 cm
apart for homogeneous tissues.
Fig. 5 shows that the direction of the Tx and Rx
dipole antennas are both set to be aligned in the MIDA
model. The Tx transmitting dipole is fixed in the skin
tissue while the Rx receiving dipole horizontally moves
from the reference location (d = 0.5 cm) up to a distance
of 7 cm (from the skin area to the deep head area). The
scenario of the simulated PL of MIDA (heterogeneous
model) is more complex than homogeneous tissues
because energy attenuation becomes considerable and
antenna coupling occurs when penetrating other tissues
alone. The PL is derived as a function of the distance
between Tx and Rx when the antennas are aligned for
homogeneous tissues and a heterogeneous human head
model. A semi-empirical in-body distance-based PL
model in dB based on the Friis formula [16, 24, 44] is
proposed:
Fig. 5. MIDA model and dipole antennas. (a) Side view,
(b) Front view.
Fig. 6. PL versus separation distance between antennas
for homogeneous tissues.
where α denotes the PL value at (set as 0.05 cm).
The variable d is the separation distance between the Tx
and Rx antennas. The PL model allows the receiving
antenna at the same distance d to have a different PL,
which varies with a Normally distributed, zero mean
random shadowing effect SN(0,). The value of the
variance for the MIDA model may be taken from the
standard deviations given in Table 2. This contribution
thus explicitly includes the stochastic effect of shadow
fading that is imposed on the deterministic contribution
to the PL. The parameter n represents the standard PL
exponent; this varies with the transmission medium, with
a value of two corresponding to free space and higher
values including situations with more obstructions. To
assess the accuracy of the parameter estimates, a least
square fit method and MATLAB curve fitting toolbox
were applied to determine PL [42] with the detailed
information summarized in the last row of Table 2. The
coefficients of determination values R2, representing the
fitting degree between PL and the antenna separation
distances, are higher than those mentioned in [16, 44]
indicating a better fit.
Table 2: Parameters for PL models for homogeneous
tissues and the MIDA model
Tissue
n
Dry skin
30.17
1.608
1.534
0.9941
Muscle
37.08
1.964
3.623
0.9911
Brain gray matter
37.97
1.631
0.658
0.9972
Brain white matter
36.97
1.644
1.101
0.9954
MIDA model
43.95
2.552
1.079
0.9728
Fig. 7. PL versus distance between antennas for
heterogeneous human body model.
Fig. 6 shows PL results illustrating that the highest
PL value is achieved by muscle, followed by brain gray
matter, brain white matter and then skin. The simulation
starts at d = 5 mm from the transmitting antenna and
ranges up to a distance of 5 cm for homogeneous tissues
in order to limit antenna coupling effects [42]. The
results are similar to those seen previously in the
literature [5, 6], although previous work used insulated or
helical antennas. As expected, the PL increases when the
separation distance between Tx and Rx increases. Fig. 7
shows the PL as a function of the distance of the
proposed heterogeneous MIDA model. The PL is derived
as a function of the distance between Tx and Rx when
the antennas are aligned for homogeneous tissues and a
heterogeneous human head model.
C. Human tissues safety
As the human head is an extremely sensitive
environment with tissues absorbing electromagnetic
power from the antenna [19], safety is paramount.
Therefore, we undertook numerical SAR calculations for
comparison to latest regulatory authority provisions. For
example, the IEEE standard regulates the SAR averaged
over 10g of tissue to no more than 1.6 W per kg in the
shape of a cube [21]. The ICNIRP regulations state that
the limit of the average SAR of 10g contiguous tissue
should be less than 2 W per kg [22].
It can be seen from Fig. 8 that the maximum SAR
10g W per kg changes with the distance. SAR values
have been calculated by moving the Rx antenna position
in the human head. The maximum value is 0.14 W per kg
at the nearest point of skin tissue and the lowest SAR
value is 0.045 W per kg at a distance of approximately 2
cm from the reference point.
Fig. 9 presents the relationship between absorbed
Fig. 8. Influence of the distance on the maximum SAR
over 10g.
that the maximum power is 3.8 mW at the reference
point while the minimum value of 1.9 mW occurs
between 2 and 2.5 cm from the reference point. These
SAR and absorbed power results reveal that the antenna
design and simulation methods are suitable and meet the
safety requirements of the ICNIRP and IEEE standards
[21, 22].
IV. Shadow fading channel modeling and
system margin
It is difficult to analytically derive the probability
density function of the combined SNR at the receiver
output. Therefore, we employ an approximation based on
curve fitting and the results are shown by the solid line
on Fig. 7 based on the least square error [44]. As can be
seen from that figure, there is a large fluctuation in the
simulated path loss values around the fitted mean path
loss. The fluctuation of the simulated path loss is mainly
due to the shadowing effect of the different brain tissues.
The shadowing is induced by the diffraction in the
shadowed regions of the body [45-46].
Since body motions are not considered in this paper,
a static human body model is assumed. Shadowing can
be regarded as directly resulting in the variations of the
received signal at the receiver front-end. The amplitude
variation caused by shadowing is often defined as the
difference between calculated path loss values and the
mean path loss. The mean PL is denoted by
, which
based on the empirical power decay law is a potential PL
model for fitting to the calculated results [6, 16, 24, 44].
where is the PL at the reference point (0.5 cm) and
Fig 9. The absorbed power versus antennas separation
distance.
equals 43.95 dB; the PL exponent n is here 2.552.
The shadow fading effect S can be derived from
Equations (1) and (2):
The in-body communication channel might have a
different PL since the surrounding environments may
vary with the location of the receiver in practice.
However, the majority of published PL models do not
take this particular situation into account [1-3, 16]. A log-
normal shadowing model is appropriate when dealing
with a more realistic situation and the shadow fading S
follows a lognormal distribution, which can be expressed
as [13, 44, 46-47]:
where S has mean μ and standard deviation σ. Here we
take and . The average BER of the
human head shadow fading channel can be expressed as
[13, 19]:
where is the mean signal-to-noise ratio, denotes
the BER performance of the additive white Gaussian
noise (AWGN) channel, and is the probability
density function of γ which follows a lognormal
distribution with the same standard deviation as the BER
performance for this shadow fading channel using BPSK
and binary orthogonal PAM using the MIDA model is
Fig. 10. SNR versus BER under the MIDA human head
model fading channel under the BPSK and binary
orthogonal PAM modulation schedules.
shown in Fig. 10.
In common with previous work on in-body
communication systems [16, 18], to provide acceptable
quality for communication, the predetermined threshold
BER is set as 10-3 for both BPSK and binary orthogonal
PAM optimum receivers [15-17, 44]. With this value of
BER, it can be seen from Fig. 10 that the minimum
required of binary orthogonal PAM is around
10.5 dB, while for BPSK it is nearly 17 dB.
It is then necessary to take into account the wireless
communication systems link budget. According to the
existing literature [3, 4, 9, 45], AWGN noise at the
receiver side is the only noise source which needs to be
considered, basically representing thermal noise. The
one-sided power spectral density of the noise in Watts
per Hertz (dimensionally equivalent to Joules) is given
by:
where and are the noise temperature of the
receiving dipole antenna and of the environment,
respectively, k is the Boltzmann constant and is the
noise factor at the receiver side. This is defined via the
noise figure in dB by:
where the noise factor of an intra-body device is related
to its noise temperature by:
We assume the temperatures of the Tx and Rx
antennas are the same since they are both located in the
human head. Noise temperature (thermal noise), which
Fig. 11. System margin versus distance at different data
rates.
depends on the temperature of the intra-body device,
thus can be seen as the same as.
The mean temperature of the human head over
baseline is 36.56 C (equal to 309K), with a standard
deviation of 0.36 C and a range from 36.16 C to
37.02 C [48]. Equation (6) can be rewritten in dB scale
as:
The SNR in dB thus can be expressed as:
where is the received power and is the
communication transmission data rate.
A system margin [49] is introduced to evaluate
the quality of the communication system channel; it is
also an effective way of evaluating system performance
when a predetermined BER (10-3) is selected. This
system margin can be taken as:
where is the minimal SNR value that promises
a reliable communication transmission in the
predetermined BER situation.
Fig. 11 illustrates the achievable quality
transmission distance at a number of data rates of 1
Mbps, 10 Mbps and 20 Mbps employing BPSK and
binary orthogonal PAM modulation. It can be seen that
the implanted communication link can achieve higher
data rates at shorter distances. For BPSK, 20 Mbps can
be reliably transmitted at a distance of around 4.5
meters and 10 Mbps can be transmitted at
approximately 5.5 meters; using 1 Mbps extends the
distance to more than 10 meters. The performance of
PAM at those three transmission data rates can reach
longer distances than BPSK, and 20 Mbps high speed
data transmission rate can be conveyed for up to 8
meters.
V. CONCLUSION
Implanted WBAN technology is one of the most
promising emerging applications in future healthcare
services that allows a wide range of applications to
function inside the human body. However, there is a
limited amount of literature focused on the
communication systems for within the human brain. In
this paper, a communication link for the human cephalic
area has been proposed for implant WBAN
communications and analysed using the FDTD method.
The BER performances have been obtained using binary
orthogonal PAM and BPSK modulation, and the minimal
required SNR values for predetermined BER conditions
of 10-3are 10.5 dB and 17 dB, respectively. The
achievable distances to deliver the target BER are greater
with PAM than BPSK for both a higher data rate of 20
Mbps and a relatively lower data rate of 1 Mbps. The
results show that a data rate of 20 Mbps to 8 meters can
be covered using binary orthogonal PAM but only up to
4.5 meters when employing BPSK. These results point
towards future work in the area of in-body cephalic area
circuit design and experimental validation.
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Yangzhe Liao received his B.S.
degree in Measurement and Control
Technology from the Northeastern
University, China in 2013 where he
was also an exchange student in the
Electrical and Computer
Engineering department of the
University of Illinois at Chicago
during his final year. Currently he is a PhD student in
the School of Engineering, University of Warwick, UK.
His research interests include Wireless body area
networks, Mathematical modeling, Error correction and
detection codes and communication systems, Multi-
input Multi-output systems.
Mark S. Leeson received the
degrees of BSc and BEng with First
Class Honors in Electrical and
Electronic Engineering from the
University of Nottingham, UK, in
1986. He then obtained a PhD in
Engineering from the University of
Cambridge, UK, in 1990. From
1990 to 1992 he worked as a Network Analyst for
National Westminster Bank in London. After holding
academic posts in London and Manchester, in 2000 he
joined the School of Engineering at Warwick, where he
is now a Reader. His major research interests are coding
and modulation, nanoscale communications and
evolutionary optimization. To date, Dr Leeson has over
230 publications and has supervised fifteen successful
research students. He is a Senior Member of the IEEE,
a Fellow of Both the Institute of Physics and the UK
Higher Education Academy.
Matthew D. Higgins received his
MEng in Electronic and
Communications Engineering and
PhD in Engineering from the
School of Engineering at the
University of Warwick in 2005 and
2009 respectively. Remaining at
the University of Warwick, he then
progressed through several Research Fellow positions
with leading defense and telecommunications
companies before undertaking two years as a Senior
Teaching Fellow. From July 2012 until February 2016,
he was an Assistant Professor in the School of
Engineering. As of March 2016, Dr Higgins holds the
position of Associate Professor in the Warwick
Manufacturing Group (WMG), University of Warwick.
His major research interests include the modelling of
optical propagation characteristics in underwater,
indoor and atmospheric conditions as well as
investigating new areas such as nano-communications;
Dr Higgins is a Member the IEEE.
