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Noise Filtering, Channel Modeling and Energy Utilization in Wireless Body Area Networks

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Constant monitoring of patients without disturbing their daily activities can be achieved through mobile networks. Sensor nodes distributed in a home environment to provide home assistance gives concept of Wireless Wearable Body Area Networks. Gathering useful information and its transmission to the required destination may face several problems. In this paper we figure out different issues and discuss their possible solutions in order to obtain an optimized infrastructure for the care of elderly people. Different channel models along with their characteristics, noise filtering in different equalization techniques, energy consumption and effect of different impairments have been discussed in our paper. The novelty of this work is that we highlighted multiple issues along with their possible solutions that a BAN infrastructure is still facing.
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Noise Filtering, Channel Modeling and Energy
Utilization in Wireless Body Area Networks
B. Manzoor, N. Javaid, A. Bibi, $Z. A. Khan, M. Tahir
Department of Electrical Engineering, COMSATS
Institute of Information Technology, Islamabad, Pakistan.
$Faculty of Engineering, Dalhousie University, Halifax, Canada.
Abstract—Constant monitoring of patients without disturbing
their daily activities can be achieved through mobile networks.
Sensor nodes distributed in a home environment to provide
home assistance gives concept of Wireless Wearable Body Area
Networks. Gathering useful information and its transmission
to the required destination may face several problems. In this
paper we figure out different issues and discuss their possible
solutions in order to obtain an optimized infrastructure for the
care of elderly people. Different channel models along with their
characteristics, noise filtering in different equalization techniques,
energy consumption and effect of different impairments have
been discussed in our paper. The novelty of this work is that
we highlighted multiple issues along with their possible solutions
that a BAN infrastructure is still facing.
Index Terms—Health, Care, Monitoring, Wireless, Body, Area,
Sensor, Networks
I. INTRODUCTION
Global trends in growth of elderly people attracts re-
searchers to formulate wireless health monitoring systems.
The system use sensors for continuous monitoring of patients
with less interaction to doctors. Wearable Wireless Body
Area Networks (WWBAN) gained a tremendous attention in
providing such facilities to keep in view the elderly age’s,
as well as giving proper weight to the economical issues.
Moreover, they are providing a reasonable platform to the
caregivers in recognition of patterns of diseases through their
databases.
The data acquired by the central device is transmitted
to the medical servers or to the care givers through wire-
less network. This communication have many issues to be
addressed. 1) Channel models, 2) effect of different inter-
ferences/disturbances, 3) noise filtering and 4) energy con-
sumption are some of the issues which have been addressed
separately and there are a number of possible solutions are
proposed for these issues.
Proper estimation and equalization of transmitted signal is
a key concern in wireless communication. For this purpose,
different channel models are used which describes different
issues like; path loss, amplitude distortion, clustering and inter
arrival-time characteristics for proper estimation of the signal.
Signal can be degraded by Additive White Gaussian Noise
(AWGN). Therefore noise filtering is also required in different
equalization schemes for better performance.
Simultaneous communication of sensors yields in different
types of interferences and disturbances (ISI, MUI, and NOISE)
which affect strongly the performance of received signal.
These disturbances emerge to be a great hurdle in signal
reception and can destroy partially or fully the information
being sent to the destination. Mean while, retransmission of
signals for proper data acquisition can utilize more energy
which is not suitable in any case. Energy utilization in wireless
BAN is a critical issue due to its infrastructure.
II. RELATED WORK AND MOTIVATION
Rapid advancements in technology devices many possi-
bilities to address routine issues and hence making life as
simpler and cureable as it could be. WWBAN introduces a
unique solution for the cure of elderly peoples throughout the
world by continuous monitoring of the patients anywhere and
anytime . Although it’s a broad domain. However, there is a
lot, to be discussed and optimized for making this approach
more effective and goal oriented.
In WWBAN different sensors are working simultaneously
to attain information to transmit it to the desired destination.
Energy utilization in wireless transmission are the main issues
which should be given equal importance to extend the life of
sensors. In order to extract useful information in the presence
of different interferences e.g. Inter Symbol Interferences (ISI),
Multi User Interference (MUI), and noise certain algorithms
have been proposed. MUD receivers provide an appropriate
platform to mitigate the effect of MUI caused by wireless
nodes which are communicating asynchronously [1]. Thus,
energy utilization for retransmission is optimized.
Improved performance of the system by allowing nodes to
use Medium Access Control protocol (MAC) among different
modulation schemes gave birth to the concept of novel Link
Adaptation (LA) strategy [2]. By considering the effects like
large SNR and low SIR data rates can be adapted dynamically
by reducing the time required for transmission and conse-
quently the interference caused by it [2].
Full bandwidth utilization of the channel without transmis-
sion of the training signal presents an idea of blind equalization
scheme. Constant Modulus Algorithm (CMA) emerges to be
a suitable blind wireless channel equalizer. The equalizer is
used to remove ISI which is produced by dispersive channels
[3]. Further studies states that use of dither input signal to the
equalizer by a distributed signal before a sign operation being
applied can be more goal oriented.
Fig 1 defines the hierarchy of the Wireless Body Area Net-
works (WBANs), its related issues and some of the possible
2012 IEEE 14th International Conference on High Performance Computing and Communications
978-0-7695-4749-7/12 $26.00 © 2012 IEEE
DOI 10.1109/HPCC.2012.264
1746
2012 IEEE 14th International Conference on High Performance Computing and Communications
978-0-7695-4749-7/12 $26.00 © 2012 IEEE
DOI 10.1109/HPCC.2012.264
1754
Fig. 1. Issues and Possible Solutions in WWBAN Infrastructure
solutions. The information obtained from the sensor nodes
is transmitted to the personal digital assistant (PDA). This
information is then transmitted to the care givers or medi-
cal server for continuous monitoring of the patients through
internet for further processing. The transmission and reception
of this information through any medium may find certain
problems like MUI, ISI and noise which can be resolved
through different techniques like by using MUD receivers,
DES-CMA and LA techniques. In our work we present an
overview of different issues which a BAN infrastructure still
have to deal with. We highlighted those issues along with some
of the possible solutions.
III. NOISE FILTERING
This section describes noise filtering in different equaliza-
tion techniques for WBAN. We discuss and compare noise
filtering in three different selected scenarios; 1) MUD recievers
2) Novel Link Adaptation, 3) Blind Equalization.
A. Noise Filtering In Multiuser Detection Based Recievers
Signal are commonly effected by InterSymbol Interference
(ISI) and Additive White Gaussian Noise (AWGN). ISI is
minimized through equalization, whereas, noise is still a
problem [1].
𝑟=𝑠+𝑛(1)
where, 𝑟=recived signal, 𝑠=transmitted signal, =channel re-
sponse and 𝑛=noise. Equalizer parameters can be calculated
using Wiener-hopf equation, which is presented in [1] as:
𝑤
𝑟𝑟Γ1𝑟𝑟 (2)
There is a tradeoff between ISI and noise. Two types of
receivers are defined in [1];
A) Linear Multi User Detector based MUD receivers
B) Non-linear Multi User Detector based MUD receivers
1) Linear MUD Receivers: In MUD based receivers, there
is only fixed equalizer parameters [1]. Therefore, these are not
suitable for fast varying channels. By implementing Minimum
Mean Square Error (MMSE), ISI can be compensated. How-
ever, effect of noise is still there. Linear MUD tries to mitigate
the effect of MUI and ISI. Whereas, it does not distinguish
between noise and MUI. Therefore, another approach named
as non-linear MUD is introduced.
2) Non-linear MUD Receivers: In such MUD based re-
ceivers there is no fixed equalizer parameter. This is because
DFE is used to adjust equalizer parameter according to de-
tected signal quality. Hence, it is more suitable for fast varying
channels. Noise becomes less problem when someone try to
completely remove ISI (zero forcing) that enhances noise,
effect of noise is catered by implementing Minimum Mean
Square Error (MMSE) criteria through Non Linear MUD
Receivers.
B. Noise Filtering In Link Adaptation For IEEE 802.15.4
As, it is discussed in previous section that noise filtering is
a problem in interference environment. Link Adaptation is a
technique which is used in [2] to minimize the ISI. The basic
idea behind this technique is discussed in [2] which is based
on, the selection of modulation technique according to channel
quality. However, by increasing order of modulation high data
rates can be achieved. In wireless environment, degradation is
caused due to multi-path. Channel quality depends upon Signal
to Noise Ratio (SNR) and Signal to Interference Ratio (SIR).
If SNR and SIR is low then channel is not good. Therefore,
lower order modulation is chosen, however, this selection leads
to increase ISI. If SNR is below some threshold then lower
order modulation is chosen which causes decrease in data rate.
Otherwise, higher order modulation is selected which causes
increase in data rate. If S/N is above some threshold and Packet
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
generated random sequence
bit index
binary value
(a)
−5 0 5
−5
−4
−3
−2
−1
0
1
2
3
4
5
Quadrature
In−Phase
Received Signal
Received Signal
Signal Constellation
(b)
−5 0 5
−5
−4
−3
−2
−1
0
1
2
3
4
5
Quadrature
In−Phase
Received Signal
Received Signal
Signal Constellation
(c)
Fig. 2. Performance Evaluation of Channel Models in the Presence of AWGN
Delivery (P.F) is below threshold then higher order modulation
is avoided.
We compare performance evaluation of channel models
in the presence of AWGN. For this purpose Quadrature
Amplitude Modulation modulation (16-QAM) scheme along
with AWGN channel for the transmission of data stream is
used. The general idea is to change modulation or coding
schemes according to response of channel. The response can
be addressed through SNR. The channel is considered ’bad’
when SNR is low, due to path loss, shadowing, or fading
problems. Whereas considered ’good’ when SNR is high. The
ratio of bit energy to noise power spectral density is set to
10dB and is then converted to corresponding SNR. A random
bit is generated, as shown in fig 2a and then converted in to
random symbols that have to be transmitted through AWGN
channel.
The system Bit Error Rate (BER) is computed using 16-
QAM modulation scheme, as shown in fig 2b. Moreover, the
effect of changing S/N on the quality of received signal is
showninfig2c.
C. Noise Filtering in Blind Equalization
For equalization of biological signals using adaptive Blind
Equalization Algorithm called as Constant Modulus Algorithm
(CMA) [3] is not suitable for noise filtering. Because, training
sequence is not sent with information. Therefore, FEC cannot
be implemented. All the effect of multipath and noise is
on actual information. The detection of signal is based on
stochastic information of signal that is stored at receiver. In
case of blind equalization, if receiver does not know about
channel response then noise filtering becomes difficult. Setting
equalizer parameters periodically according to errors received
in previous is discussed in [3] minimizes noise. If we go
towards zero forcing solution than it leads to enhance noise.
Adaptive algorithm is defined in [3] as:
𝑓(𝑛+1)=𝑓(𝑛)+𝜇𝑟(𝑛)𝜓𝑑𝑠𝑒𝑐𝑚𝑎(𝑦(𝑛)) (3)
where, 𝑓(𝑛+1)=next equalizer parameter, 𝑓(𝑛)=current equal-
izer parameter, 𝜇=step size and 𝑟(𝑛)=received signal.
IV. CHANNEL MODELING
In this section, we discuss different channel models and
characteristics of these models for wireless communication
in different standards of WBAN. These models are used
for estimation and equalization of transmitted signals. We
compare and discuss three channel models for WBAN.
A. Channel Model for IEEE 802.15.4a
Channel model for MUD based receivers is recently pro-
posed for IEEE 802.15.4a is given in [1]. One of the rea-
sons for ISI is channel memory. Traditional receiver (DFE,
MMSE, MLSE) knows about channel characteristics. Analysis
shows that electromagnetic propagation at UWB range (3.1-
10.6) GHz is negligible through human body. Transmitted
signals reaches at receiver in two different ways: 1: Around
human body, 2: Through reflection from surrounding. There-
fore, WBAN channel posseses significantly different path
loss, amplitude distortion, clustering and inter arrival-time
characteristics. We summarize simulation results for WBAN’s
channel model as: (A) For an outdoor channel (Outdoor-
BAN), measurements [1] indicate that there are always two
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clusters of multipath components due to the initial wave
diffracting around the body, and reflections from the ground.
Therefore, number of clusters is always two and does not
need to be defined as a stochastic process as defined for
other scenarios. Moreover, depending upon exact position of
transmitter on human body intercluster arrival time distribution
is deterministic. (B) A human wearing WBAN devices in
an indoor environment is defined as an Indoor channel. Two
deterministic clusters due to stochastic nature of multipath
(reflections from the indoor environment) should be taken into
account, as discussed in [1]. Channel model corresponding to
the propagation for outdoor environment [1]:
𝑏𝑜𝑑𝑦(𝑡)=
𝑘=0
𝛽𝑘𝑒𝑥𝑝(𝑗𝜙𝑘)𝛿(𝑡−△𝑘)(4)
where, 𝛽𝑘is the 𝑘𝑡ℎ element of vector Y, 𝜙𝑘is uniformly
distributed random variable and is size of bin.
Model parameters are taken for ’front’, ’side’ and ’back’
positions of receiver on human body. Reflections from ground
introduces a delay 𝜏𝑔𝑟𝑜𝑢𝑛𝑑 in channel model and thus, overall
channel model is given as :
𝑔𝑟𝑜𝑢𝑛𝑑 (𝑡)=
𝑘=0
𝛽𝑘𝑒𝑥𝑝(𝑗𝜙𝑘)𝛿(𝑡−△𝑘𝜏𝑔 𝑟𝑜𝑢𝑛𝑑)(5)
By assuming that reflections from ground are uncorrelated
with components diffracting around body, then complete chan-
nel response and is represented in [1] as:
𝑜𝑢𝑡𝑑𝑜𝑜𝑟𝐵𝐴𝑁(𝑡)=𝑏𝑜𝑑𝑦 (𝑡)+𝑔𝑟 𝑜𝑢𝑛𝑑(𝑡)(6)
For an Indoor-BAN channel, reflections from surroundings
create multipath. Multipath distribution is stochastic in nature
therefore, clusters are modeled using Poisson or Weibull dis-
tributed random variables. Whereas, intra-cluster distribution
is dense, so multipath arrivals within a cluster are assumed
to be uniform [1]. The channel model due to reflections from
surroundings is given in [1] as:
𝑟𝑒𝑓 (𝑡)= 𝑋
𝐸
𝑙=0
𝐾=0
𝛽𝑙,𝐾𝑒𝑥𝑝(𝑗𝜙𝑙,𝑘)𝛿(𝑡−△([𝜏𝑙/Δ] + 𝑘)) (7)
20𝑙𝑜𝑔10(𝛽𝑙,𝑘)=Γ
𝜏𝑙(𝜏𝑙
𝑘)+𝜎Γ𝑛𝑙+𝜎Υ𝑛𝑘(8)
𝑝(𝜏𝑙/𝜏𝑙1) = 1
𝛽𝑒(𝜏𝑙𝜏𝑙1/𝛽)(9)
where, 𝛽𝑙,𝐾represents the amplitude in 𝑘𝑡ℎ bin and in 𝑙𝑡ℎ
cluster modeled in [1], 𝜏𝑙is the cluster arrival time modeled
using a Poisson distribution, 𝛽represent the mean arrival rate.
Average energy of the clusters decays exponentially at a rate
of Γ𝑑𝐵/𝑛𝑠 and the terms within the cluster decay at the rate
of 𝛾𝑑𝐵/𝑛𝑠. Lognormal fading in the clusters and within the
clusters is modeled using 𝜎Γand 𝜎𝛾, respectively. Here, 𝑛𝑙
and 𝑛𝑘represent uncorrelated normal random variables with
unit mean and unit variance. 𝜙𝑙,𝑘are uniformly distributed
random variables and Δis bin size [1].
If we assume that reflections from indoor environment and
ground are uncorrelated with components diffracting around
the body, then complete channel response from [1] is:
𝑖𝑛𝑑𝑜𝑜𝑟𝐵𝐴𝑁(𝑡)=𝑏𝑜𝑑𝑦 (𝑡)+𝑔𝑟 𝑜𝑢𝑛𝑑(𝑡)+𝑟𝑒𝑓 (𝑡)(10)
B. Channel Model for IEEE 802.15.4
Channel model that is used for a proposed novel Link Adap-
tation (LA) strategy [2], where nodes select their modulation
schemes according to the experienced channel quality and
level of interference. The general idea is to change modulation
and/or coding scheme in such a way that it lowers bit rates
(more robust modulation/coding when the overall bandwidth
is set) in case of ”bad” channel conditions. Whereas, high bit
rates when the channel is ”good”. The bad channel signifies
low SNR, due to path loss, shadowing or fading problems,
or when SIR is low. In both cases, the use of a more robust
modulation/coding is supposed to reduce probability of error.
The channel loss, denoted as 𝐴, is modeled according to 3GPP
proposal for WBANs, as discussed in [2]:
𝐴(𝑑)[𝑑𝐵]=𝐴0+10𝑛log(𝑑/𝑑0)+𝑠(11)
where 𝑑is the distance between transmitter and receiver,
𝐴0defines path loss in 𝑑𝐵 at a reference distance, 𝑑0,
𝑛specifies path loss exponent and 𝑠represents a random
Gaussian variable having zero mean and standard deviation
𝜎.
Algorithm 1 Novel Link Adaptation
1: 𝑆𝑒𝑡 𝑜𝑓 𝑛𝑜𝑑𝑒𝑠 𝑖𝑛 𝐵𝐴𝑁 𝑁
2: 𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑏𝑒𝑎𝑐𝑜𝑛 𝑝𝑜𝑤𝑒𝑟 𝐵𝑅𝑃
3: 𝐹 𝑎𝑖𝑙𝑢𝑟𝑒 𝑃𝑟 𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑟 𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑝𝑎𝑐𝑘 𝑒𝑡 𝑃𝐹
4: 𝑇 ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 𝑓𝑜𝑟 𝑓𝑎𝑖𝑙 𝑢𝑟𝑒 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙 𝑖𝑡𝑦 𝑝𝑐𝑘𝑒𝑡 𝑇𝐻
𝑃𝐹
5: 𝐵𝑖𝑡 𝑟𝑎𝑡𝑒 𝐵𝑅
6: 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑒𝑛𝑠𝑜𝑟 𝑛𝑜𝑑𝑒𝑠 𝑖𝑛 𝐵𝐴𝑁 𝑛
7: for all 𝑛𝑁do
8: 𝐹𝑖𝑛𝑑 𝐵
𝑅𝑃 𝑓𝑜𝑟 𝑒𝑣𝑒𝑟𝑦 𝑛;𝐵𝑅𝑃 (𝑛)
9: 𝑆𝑒𝑡 𝐵𝑅𝑃 (𝑛)𝑃𝑛
10: if 𝑃𝑛SNR <𝑇𝐻
𝑆𝑁𝑅 then
11: 𝐵𝑅=𝐵−−
𝑅
12: end if
13: if 𝑃𝐹>𝑇𝐻
𝑃𝐹 then
14: 𝐵𝑅=𝐵++
𝑅
15: else
16: 𝐵𝑅=𝐵𝑅
17: end if
18: end for
C. Geometrical Based Hyperbolically Distributed Scatters
channel model
Geometrical-Based Hyperbolically Distributed Scatterers
(GBHDS) channel model is discussed in [3], for macro cell
environments is considered in this section. A comprehensive
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study of this model proved to be more realistic than other
models, as tested against practical data [3]. GBHDS for
macro cell environment channel model assumes that scatterers
are arranged within a circle of radius R around mobile.
The distances 𝑟between Mobile Station (MS) and scatterers
are distributed according to hyperbolic Probability Density
Function (pdf). The Geometrical Scatterers Density Function
(GSDF) for this model, 𝑓𝑟(𝑟),isgivenin[3]as:
𝑓𝑟(𝑟)={𝑎
tanh(𝑎𝑟)cosh
2(𝑎𝑟)(12)
where, 𝑅is the radius of the circle enclosing the scatterers, and
applicable values of 𝛼lie in interval (0, 1) [3]. GBHDS results
show that there is a good match between model result and
measurement data which is reported for Outdoor environment.
Fig 4 shows a comparison of the results for the Direction
of Arrival (DOA) pdf for the GBHDS channel model, DOA
pdf for the Geometrical Based Single Bounce Macro Cell
(GBSBM) channel model and Gaussian Scatterer’s Density
(GSD) channel model. From fig 4 it is clear that there is a
good match between the GBHDS channel model result and
measurement data. Whereas, GSD model fails in the DOAs
close to Line-of-Sight (LOS).
(a)
Fig. 3. D.O.A Comparison for GBHDS, GBSBM AND GSD Channel Models
V. DATA BROADCAST IN IEEE 802.15.4 AND ZIGBEE
Energy consumption is an important issue in wireless sensor
network and same is the case associated with WBAN. Multiple
sensor nodes are working simultaneously for acquisition of
useful information from the patients in WBAN. The key idea
in this regard is to use IEEE 802.15.4 standard along with
ZigBee network which is considered to be a low data rate and
low cast infrastructure [4]. IEEE 802.15.4 defines two types
of devices: Full Function Device (FFD) which can serve as a
coordinator or a regular device and a Reduced Function Device
(RFD) which is a simple device that associate and commu-
nicate only with an FFD. Based on IEEE802.15.4, ZigBee
specifies the standards for network and application. ZigBee
network layer is responsible for assigning addresses and builds
a hierarchical tree topology [4]. Network parameters such as
NOISE FILTERINGCHANNEL MODELING
INDOOR
BODY+
GROUND
LINEAR
MUD
MINIMUM NOISE
FILTERED
LINE OF SIGHT MODEL LOW SNR/
L.o.m
REFLECTION
INSIDE CIRCLE
ABSENCE OF
TRAINED SIGNAL
MUD RECIECVERS LINK ADAPTATIONBLIND EQUALIZATION
OUTDOOR
BODY+
GROUND+
REFLECTION
NONLINEAR
MUD
MAXIMUM NOISE
FILTERED
NOISE FILTERINGCHANNEL MODELING NOISE FILTERINGCHANNEL MODELING
High SNR/
H.o .m
CHANNEL MODELING/NOISE
FILTERING
Fig. 4. Hierarchy for Channel Modeling and Noise Filtering
maximum allowable number of children’s, 𝑛𝑐ℎ𝑙 and maximum
level of logical tree, 𝑑𝑙, are managed by a coordinator. A
coordinator acts as a root of the tree with address zero.
On-tree self pruning broadcast algorithm keeps the energy
efficient broadcast in account for ZigBee network and decides
whether to rebroadcast or not after receiving a packet. When a
source node broadcasts a packet, then all its 1-hope neighbors
receives this packet. This may cause collision when multiple
nodes are communicating and accessing the channel at the
same time. To avoid such collisions, each node waits for
random period of time before rebroadcasting. During this
waiting state, forward node, 𝑥only needs to cover 1-hope
distance i.e., 𝑁(𝑥)𝑁(𝑌). If node 𝑥learns that all its 1-
hope neighbors are already covered before time out then it
does not need to rebroadcast [4]. This self pruning algorithm
can perform poorly when applied to zigBee networks because
of unavailibilty of 2-hope neighbor’s information.
An optimal On-tree forward node Selection algorithm
(OOS) resolves the problem and is assumed to guarantee
convergence of the whole network [4]. This algorithm tries to
reduce the size of the candidate forward node set 𝑆and to
be covered set 𝐶. If node 𝑥is the source or forward node, it
will rebroadcast and cover all its 1-hope neighbors, therefore,
it does not need to be selected as a forward node again, as
well as, all its neighbors need not to be covered again.
Fig. 6𝑎shows ZigBee tree topology with node 𝑣at the
centre and four tree neighbors v1, v2, v3 and v4. u1....u8 are
other eight 1- hope neighbors which can be located anywhere
on the logical ZigBee tree. Given the network addresses of v1
to v4 and u1 to u8. Node V can further identify their parent
and children.
Fig. 6𝑏shows principle OOS which describes set size of the
candidate forward node S and C. OOS algorithm goes through
level by level in a top-to-bottom direction and left to right at
each level.
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v1
v2
v
v4
u3
u8u5 u6 u7
u4
v3
(a)
`
v1
u1
v3
u5 u6 u7
u4
v4
v2
Construction of trees involving S and V
(b)
Fig. 5. OOS Algorithm in Local ZigBee using Tree Topology
VI. ENERGY UTILIZATION
In this section, we discuss and compare energy utilization
in selected three techniques; 1) MUD receivers, 2) Novel Link
Adaptation, and 3) Blind Equalization for modeling channels.
WWBANs use a central device (i.e., hand held computer) and
multiple sensors, simultaneously for communication. Multi
user interference is a phenomenon which leads in the degra-
dation of the received signal and certain receivers based on
RAKE do not cancel this effect [1]. In BAN environment Multi
user based receiver is considered to be an appropriate solution
for the BAN environment where several devices communicate
simultaneously [1].
Algorithm 2 Algorithm for MUD receivers
1: 𝐷𝑖𝑠𝑐𝑟𝑒𝑡𝑒 𝑒𝑞𝑢𝑣𝑎𝑙𝑒𝑛𝑡 𝑜𝑓 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑤𝑎𝑣𝑒𝑓 𝑜𝑟𝑚 𝑟1
2: 𝐹𝑟𝑎𝑚𝑒𝑠 𝑠𝑎𝑚𝑝𝑙𝑒𝑠𝑁𝑠
3: 𝑀𝑒𝑎𝑛 𝑠𝑞𝑢𝑎𝑟𝑒 𝑒𝑟𝑟𝑜𝑟 𝑀𝑆𝐸
4: 𝐹 𝑖𝑙𝑡𝑒𝑟 𝑡𝑎𝑝𝑒𝑠 𝑊
5: 𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑜𝑢𝑡𝑝𝑢𝑡 𝑅𝑜𝑢𝑡
6: if Choice = 1 then
7: 𝑈𝑠𝑒 𝑙𝑖𝑛𝑒𝑎𝑟 𝑀 𝑈 𝐷
8: 𝑟1=𝑁𝜇
𝜇=1
𝑘=−∞ 𝛼𝜇
𝑘𝜌𝜇(𝑙𝑘𝑁𝑠)+𝑛1
9: 𝐶𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒 𝑁
𝑠𝑓𝑟𝑜𝑚 𝑟
1
10: 𝑤=[𝑤1....𝑤𝑛]
11: 𝐶ℎ𝑜𝑜𝑠𝑒 𝑊𝑠𝑢𝑐ℎ, 𝑡ℎ𝑎𝑡 𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒𝑠 𝑀𝑆𝐸
12: 𝑅𝑜𝑢𝑡 =𝐼𝑆𝐼 +𝑀𝑈𝐼 +𝑁𝑜𝑖𝑠𝑒
13: else
14: 𝑈𝑠𝑒 𝑛𝑜𝑛 𝑙𝑖𝑛𝑒𝑎𝑟 𝑀𝑈𝐷
15: 𝐹 𝑜𝑟 𝐹 𝑒𝑒𝑑 𝐹 𝑜𝑟𝑤𝑎𝑟𝑑 𝐹 𝑖𝑙𝑡𝑒𝑟 (𝐹𝐹𝐹)
16: 𝑅𝑝𝑒𝑎𝑡 𝑠𝑡𝑒𝑝𝑠 810
17: 𝐹 𝑜𝑟 𝐹 𝑒𝑒𝑑 𝐵𝑎𝑐𝑘 𝐹 𝑖𝑙𝑡𝑒𝑟 (𝐹𝐵𝐹)
18: (𝑁𝑠(𝑁𝑠1)) 𝑆𝑡𝑎𝑡𝑢𝑠
19: 𝑅𝑜𝑢𝑡 = FFF + FBF
20: 𝑅𝑜𝑢𝑡=ISI +MUI
21: end if
A. Multiuser Detection Based Recievers
A signal consists of following components; a) desired signal
energy spread over several multiple symbols, b) ISI due to
channel memory, c) MUI due to other users signals, and d)
additive white Gaussian noise is received [1]. MUD based
receiver is used to extract effective energy in the presence
of all the components. To effectively extract energy from
different multipaths in the presence of ISI, MUI and AWGN.
MUD receiver is implemented in two phases; 1) Linear MUD
receivers and 2) non-Linear MUD receivers [1].
1) Linear MUD Recievers: A linear MUD receiver uses
Weiner-Hopf equation to minimize mean square error. The
effects of ISI, MUI and noise are mitigated jointly, however,
receiver cannot distinguish between MUI, ISI and noise.
To differentiate between noise and ISI, another approach is
proposed which is known as Non-linear MUD receivers.
2) Non-Linear MUD Recievers: Estimation of previous
symbol could be utilized while estimating the current symbol.
This could be achieved by exploiting decision feedback prin-
ciple. A Non-Linear MUD receiver consists of two filters: one
is Feed Forward Filter (FFF) and Feed Back Filter (FBF). FFF
resembles Weiner combiner, and the FBF has at its inputs the
sequence of decisions on previously detected symbols. FBF
is intended to remove the part of the ISI from the current
symbol which is caused by previously detected symbol. By
minimizing mean square error through Linear and Non-Linear
receivers, retransmission can be mitigated. Thus, minimizes
energy consumption.
B. Novel Link Adaptation
Novel Link Adaption (LA) mechanism improves perfor-
mance and achieves significant result while reporting measured
data to a coordinator with different modulation schemes [2].
17511759
A variable data rate scheme is proposed to reduce the aver-
age power consumption [2]. This scheme considers channel
conditions which would work in a better way. The usage of
lower bit rates (low SNR/SIR) and higher bit rates to reduce
probability of error is well solved in [2]. The use of dynamic
modulation/coding schemes (O-PSK, L-DSPK) by keeping
channel response in view can result beneficially in reduction
of interferences. Thus, minimizes energy consumption through
avoiding re-transmissions.
C. Blind Equalization Algorithm
Conventional equalization techniques rely on the transmis-
sion of training signals. This relay leads to a reduction of
channel bandwidth and sources allocation. Constant Modulus
Algorithm (CMA) is suitable for blind wireless channel equal-
izer, because of its robustness over the violation of perfect
blind equalization condition gives full bandwidth utilization
[3]. Transmission time is always an important factor in terms
of cost and transmission power being utilized. Large data
rate can be achieved through lossless compression algorithm.
In wireless communication systems, most of the energy is
consumed for transmission. Multiple interferences lead to the
effect of retransmission during signal transmissions. In this
study different techniques are discussed which addresses the
minimization of different interferences/disturbances.
VII. IMPAIRMENTS
Simultaneous communication among sensors and central
devices (hand held computers) yields multiple interferences
like ISI, MUI, and noise. RAKE based receivers do not cancel
these effects, therefore, MUD receivers are used. Whereas,
determination of the coefficients of the combiner is carried
through training sequence method.
A. Linear and Non-Linear Equalizers
By using MMSE criterion to determination of tap coeffi-
cients is given in [1] as:
𝑇𝑟
𝑇𝑛 =𝑁𝑠 > 1(13)
Moreover, considering the above condition for sampling rate
in [1] as:
𝑟1=
𝑁𝜇
𝜇=1
𝑘=−∞
𝛼𝜇
𝑘𝜌𝜇(𝑙𝑘𝑁𝑠)+𝑛1(14)
For u =1
𝑟1=
𝑘=−∞
𝛼1
𝑘𝜌(𝑙𝑘𝑁𝑠)+
𝜇=1
𝑘=
𝛼𝜇
𝑘𝜌𝜇(𝑙𝑘𝑁𝑠)+𝑛1(15)
Representing the MUI as 𝑚1given in [1] as:
𝑟1=
𝑘=−∞
𝛼1
𝑘𝜌(𝑙𝑘𝑁𝑠)+𝑚1+𝑛1(16)
Filter tapes 𝑤=[𝑤1....𝑤𝑛]minimize MMSE 𝐸(𝑎1
𝑘
𝑊𝑟
𝑘2)) are desired to be chosen, and are obtained through
Weiner-Hopf equation [1]. 𝑤=𝛾𝑎𝑟Γ
𝑟𝑟1where, Γ𝑟𝑟 =
𝐸[𝑟𝑘𝑟𝑇
𝑘]denotes received vector auto-correlation matrix and
Γ𝑟𝑟 =𝐸[𝑎1
𝑘𝑟𝑇
𝑘]represents cross correlation between known
transmitted symbol and the corresponding received samples.
The receiver tries to mitigate the effect of MUI, ISI and noise
jointly. However, it does not distinguishes MUI and ISI from
noise, thus, the output still contains some amount noise. In
order to mitigate the effect of ISI, a Non-Linear filtering
approach based on FFF and FBF is used. FBF has at inputs
the sequence of decisions on previously detected symbols. The
main aim of this decision is to remove ISI from the current
symbol caused by previous symbols.
MMSE criterion is applied to optimize the coefficients of
the filters. It is essential to note that input samples to FFF are
spaced 𝑇𝑟 seconds apart while input samples of the FBF are
spaced 𝑇𝑠 seconds apart. The equalizer output is represented
in[1]isgivenas𝑥𝑘=𝑤𝑓𝑓𝑟𝑘+𝑤𝑓𝑏 where, the row vector 𝑤𝑓𝑓
denotes 𝑁𝑠 length of FFF and 𝑤𝑓𝑏 is the 𝑁𝑏 length vector
representing FBF. The set of past decisions is represented by
𝑎1
𝑘=[𝑎1
𝑘, ....𝑎1
𝑘𝑁𝑏]𝑇as given in [1].
B. Novel Link Adaptation
Another optimized proposed solution for the removal of ISI
is use of dynamic modulation/coding schemes according to
channel response as given in [2]. Considering 3GPP proposal
for WBANs, the channel loss can be calculated from [2] as:
𝐴(𝑑)[𝑑𝐵]=𝐴𝑜+10𝑛𝑙𝑜𝑔(𝑑/𝑑𝑜)+𝑠(17)
where 𝑑is the distance between transmitter and receiver,
𝐴0is the path loss in dB at a reference distance; d0 (A0
= 35.2 dB for d0 = 0.1 m), 𝑛is path loss exponent (n
= 3.11), and 𝑠is a random Gaussian variable, having zero
mean and standard deviation 𝜎=6.1𝑑𝐵. for proper reception
of packet, we assume that following two conditions should
be achieved: a) 𝑃𝑅 > 𝑃𝑅
𝑚𝑖𝑛, where PR is the received
power given by: 𝑃𝑅[𝑑𝐵𝑚]=𝑃𝑡𝑥[𝑑𝐵𝑚]𝐴[𝑑𝐵],𝑃𝑡𝑥 and
𝑃𝑅𝑚𝑖𝑛 are transmit power and receiver sensitivity, respectively
[2]. Total power received from interfacing nodes is denoted
by 𝐼, whereas, 𝐶represents the power received from useful
transmitter. 𝐶/𝐼 𝑚𝑖𝑛 is the minimum SIR ensuring the correct
reception of a packet. These values are function of modulation
schemes being used.
C. Blind Equalization Algorithm
A bandwidth limited channel with high data rates is al-
ways effected by ISI. This is due to adjacent symbols on
the output of the channel which overlap each other and
causing degradation of error performance. An equalization
filter attempts to extract the transmitted symbol sequence
by de-convolving them with the inverse version of channel.
Meanwhile, multiplexing techniques emerge to be a powerful
source for minimizing system bit error rate.
Cyclic prefix (longer then channel length) within OFDM
systems by converting linear convolution into cyclic one
17521760
0 500 1000 1500 2000 2500 3000 3500 4000
−30
−25
−20
−15
−10
−5
8−QAM: SNR = 30dB
CMA: μ = 0.0008
Proposed: μ = 0.0006
−2 0 2
−3
−2
−1
0
1
2
3
CMA
−2 0 2
−3
−2
−1
0
1
2
3
β.CMA
0 500 1000 1500 2000 2500 3000 3500 4000
−30
−25
−20
−15
−10
−5
16−QAM: SNR = 80dB
CMA: μ = 0.00015
Proposed: μ = 0.0003
−5 0 5
−6
−4
−2
0
2
4
6
CMA
−5 0 5
−6
−4
−2
0
2
4
6
β.CMA
Fig. 6. Blind Equalization Using CMA, 8-QAM and 16-QAM
mitigates the effect of ISI significantly [3]. To utilize full
bandwidth of the channel adaptive blind equalization systems
are introduced in order to minimize the effect of ISI [3].
Transmission of training signals in conventional equalization
techniques leads to reduction in bandwidth. Whereas blind
equalization do not rely on the training sequence, hence,
results in a better consumption of allocated resources.
Because of robustness over violation of Perfect Blind Equal-
ization (PBE) conditions, CMA is an attractive choice for the
researchers. Dithered Signed-Error Constant Modulus Algo-
rithm (DSE-CMA ) changes the input signal to the equalizer
by a non-subtractive sinusoidally-distributed signal before a
sign operation is applied [3]. The output of the equalizer using
multi-rate model from [3] is given as:
𝑦(𝑛)=𝑟𝐻(𝑛)𝑓(𝑛)+𝑊𝐻(𝑛)𝑓(𝑛)
=𝑋𝐻(𝑛)𝐻𝑓(𝑛)+𝑊𝐻(𝑛)𝑓(𝑛)
where, 𝑋=[𝑥(𝑛),𝑥(𝑛1), ...., 𝑥(𝑛𝑁𝑥+1)]
𝑡denotes a
finite-length symbol vector. The length of this symbol vector
is given by 𝑁𝑥=(𝑁+𝑁𝑓1)/2,𝑓represents a column
vector of fractionally-spaced equalizer coefficients with a
length 𝑁𝑓. The column vectors 𝑟(𝑛)and 𝑤(𝑛)represent
the time decimated 𝑁𝑓received samples and white Gaussian
noise, respectively. A Hermitian operator is denoted by (.)H,
and matrix transposition is symbolized by (.)T. Matrix H
symbolizes a 𝑁𝑥𝑁𝑓time-decimated channel convolution
of the GBHDS channel model.
Signals when pass through a channel undergo various forms
of distortions. Most common is ISI. Most of the times, in prac-
tical systems, channel characteristics are not known as prior.
Therefore, adaptive equalizers are used. Blind equalization as
an adaptive equalizer can compensate amplitude and delay
distortion of a communication channel only by using channel
output samples and knowledge of basic statistical properties
of the data symbol. We compared simulated results for blind
equalization using CMA. We used picchiprati channel model
for our experiments. The transmitted signals are modulated
with 8-QAM and 16-QAM, respectively. From the fig. 8 given
it is clear that step size 𝜇=0.0006 and 𝜇=0.0003 for 8-
QAM and 16-QAM, respectively are sufficiently enough to
minimize MSE at steady-state.Thus, convergence is achieved.
VIII. CONCLUSION
In this paper, we addressed some of the problems that
a body area network still has to deal with for efficient
performance in order to provide an optimized solution for the
cure of elderly people. We investigated some of the issues and
tried to sort out their possible solutions. Channel modeling,
effect of interferences/disturbances, energy consumption, and
noise filtering are some of the issues which should be given
importance on the equal basis to attain better performance.
We also discussed some simulated results describing the use
of different modulation/coding schemes. Decision is taken on
the basis of channel characteristics.
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This paper proposes an improved Traffic Class Prioritization based Carrier Sense Multiple Access/Collision Avoidance (TCP-CSMA/CA) scheme for prioritized channel access to heterogenous-natured Bio-Medical Sensor Nodes (BMSNs) for IEEE 802.15.4 Medium Access Control (MAC) in intra-Wireless Body Area Networks (WBANs). The main advantage of the scheme is to provide prioritized channel access to heterogeneous-natured BMSNs of different traffic classes with reduced packet delivery delay, packet loss, and energy consumption, and improved throughput and packet delivery ratio (PDR). The prioritized channel access is achieved by assigning a distinct, minimized and prioritized backoff period range to each traffic class in every backoff during contention. In TCP-CSMA/CA, the BMSNs are distributed among four traffic classes based on the existing patient’s data classification. The Backoff Exponent (BE) starts from 1 to remove the repetition of the backoff period range in the third, fourth, and fifth backoffs. Five moderately designed backoff period ranges are proposed to assign a distinct, minimized, and prioritized backoff period range to each traffic class in every backoff during contention. A comprehensive verification using NS-2 was carried out to determine the performance of the TCP-CSMA/CA in terms of packet delivery delay, throughput, PDR, packet loss ratio (PLR) and energy consumption. The results prove that the proposed TCP-CSMA/CA scheme performs better than the IEEE 802.15.4 based PLA-MAC, eMC-MAC, and PG-MAC as it achieves a 47% decrease in the packet delivery delay and a 63% increase in the PDR.
... Moreover, the MAC also plays a key role to improve the overall performance of the network [14]. Therefore, many energy efficient MAC protocols such as [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] have been proposed. In addition, other MAC protocols are proposed to improve Quality of Service (QoS) through data classification such as [31][32][33][34][35][36][37][38][39][40], and data prioritization such as [41][42][43][44][45][46][47][48][49][50]. ...
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In Wireless Body Area Networks (WBANs), emergency traffic handling is essential for saving human life. The traffic must be delivered instantaneously without loss and with least delay since a delay may endanger human life. Because of the importance of emergency traffic handling, several researchers have explored and proposed different emergency traffic protocols. In this paper, we provide a thematic review of the emergency traffic Medium Access Control (MAC) protocols in WBANs. Zigbee standard and baseline MAC as used in WBANs are also analyzed in terms of emergency traffic handling. Furthermore, a comparative analysis of the existing emergency traffic MAC protocols is made and their performance analysis is performed based on delay, Packet Delivery Ratio (PDR), and energy consumption. Currently, no review work has been done on emergency traffic MAC protocols in WBANs. This paper, therefore, serves as the first, and adds enhancement to the emergency traffic handling at MAC layer in WBANs. We believe the paper will stimulate a better way of solving the emergency traffic handling problem.
... Path loss represents the signal attenuation and is measured in decibels (dB). Signal power is also degraded by Additive White Gaussian Noise (AWGN) [19]. Path loss is the difference between the transmitted power and received power whereas antenna gain may or may not be considered. ...
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