Interference Mitigation in Wireless Body Area Networks Using Modified and Modulated MHP

Wireless Pers CommunDOI 10.1007/s11277-013-1584-z

Interference Mitigation in Wireless Body Area NetworksUsing Modified and Modulated MHP

Deepak Kumar Rout · Susmita Das

© Springer Science+Business Media New York 2014

Abstract Wireless Body Area Networks (WBAN) is an emerging area in field of remotehealth monitoring and telemedicine. UWB is a preferred candidate for the WBAN as itprovides very high data rate at minimal cost and power consumption. Since the UWB-WBAN is wireless, it will be affected by interference from existing wireless personal andlocal area networks. Interference immunity is a major issue in wireless Body Area Networksas patients’ vital data containing details of functioning of vital organs and blood flow arecarried. The paper investigates the performance of modified and modulated hermite pulses(MHP) for narrowband interference mitigation in the 4,940–4,990 MHz band IEEE 802.11yPublic Safety band interference. This 50 MHz interfering band will be a critical interfererdue to the higher power levels of interfering system. Performance of the proposed techniquehave been shown in comparison with Gaussian pulse shapes and has been further validatedby transmitting ECG and MRI data by it in presence of strong interference.

Keywords WBAN · Interference mitigation · Jam resistance · WLAN ·TH-BPSK · Narrowband interference · UWB · MHP

1 Introduction

The world is witnessing an explosive growth of population in the recent times. Providinghealthcare facilities has been a cumbersome task in many countries. In the future governmentsof many developed as well as developing countries will struggle to provide health carefacilities to their inhabitants due to rapid rise in population. A solution to this problem canbe the use of information technology for medical applications. Many wired and wireless

D. K. Rout (B) ·Signal Processing and Communication Lab, Department of Electrical Engineering,National Institute of Technology, Rourkela, Indiae-mail: [email protected]

S. DasDepartment of Electrical Engineering, National Institute of Technology, Rourkela, India

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techniques for telemedicine are in use but they will not be enough in the future and hencewhole new infrastructure and methods are to be developed. One such potential candidate isthe WBAN or Wireless Body Area Networks.

Body Area Networks is an evolving area in the field of wireless communication, remotehealth monitoring and telemedicine. It presents whole new methods for acquisition of medicaldata from patients, its wireless transmission, reception and processing. In this technologymicro-sensors are implanted inside human body or embedded in the external clothing or elseworn on human body. These sensors collect important health parameters like blood pressure,blood sugar, heart rate, and other vital parameters [1]. The placement of these sensors willnot only enable detecting patients in case of emergency but also detect vital signs of diseasesin their early stage. Data collected by sensors is transmitted to the smartphones or hand heldhealth gadgets. Sensors to sensor communication in BAN will enable the transmission ofdata efficiently and save power which would otherwise be consumed while communicatingwith a far device directly. The smartphone or health gadget transmits the collected signals toa patient monitoring center [1]. Data collected at the monitoring center is closely monitoredby computer programs and medical personnel jointly. In case of anomalies in the receiveddata, the patient can be located and health services can be rushed. The advantage in this typeof monitoring, is the efficient utilization of medical personnel whose number is scarce inundeveloped nations.

The data communication in WBAN is wireless and since it is to be used in the vicinityof the human body and there exist many wireless and personal area networks using similarfrequency bands, there are strong possibilities of interference.

The IEEE 802.15.6 has recommended several frequency bands for BANs. For implantsthe 402–405 MHz band is used while for body surface nodes the 13.5, 50, 400, 600, 900 MHz,2.4 and 3.1–10.6 GHz bands are proposed. So each such frequency band is affected with adifferent type of interference. In our work we have the 3.1–10.6 GHz UWB band taken intoconsideration. The reason for usage of this band in WBAN is due to the fact that UWB useslow power pulses for communication yet provides a very high data rate [2]. This makes ithighly suitable for WBAN sensors whose battery life and energy sources are very limited.BANs using the UWB for communication are known as UWB WBANs. UWB WBAN usesa large bandwidth for communication so it will be affected by narrowband interference.

The IEEE 802.11y uses a portion of the UWB band from 4,940 to 4,990 MHz for itschannels 20–26. While the coverage of IEEE 802.11 is more than 500 m the UWB WBANis limited to only 10 m [3]. Hence this band is likely to interfere with the UWB BANs. Thismajor issue of interference inspires researchers to devise mitigation techniques.

UWB interference has already attracted the attention of several scientists and researchers.While the usage of Notch filters at the receiver end can be a possible solution as proposed by[4], an alternative is the use of pulse shaping which has been suggested in [5]. Energy detectorbased interference mitigation has been presented in [6]. In [7] the authors propose a multipleantenna selection diversity based Narrowband Interference Mitigation method. In [8] MBOFDM for UWB WBAN communications is proposed, where the available bandwidth isdivided into multiple bands.

The Notch filter based techniques and multiple antenna systems may not be suitable inWBANs due to the tiny size of the sensors. The pulse shaping based techniques may not besuitable due to system complexities. Energy detector based methods are proposed in [6] theyare based on complex algorithms, hence will not be suited in WBANs. While MB OFDMbased methods are a better option for combating NB interference in UWB communications,they do not suit the WBAN because of their complexity. OFDM requires complex FFToperations that add high level of complexity and consume computing power and hence is not

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suitable for use on tiny sensors which should consume less power and stay active throughouttheir useful lifetime. The IEEE 802.15.3 and IEEE 802.15.6 which are the correspondingstandards for UWB and WBAN respectively, imply that IR UWB and MB OFDM based radioswill coexist [9,10]. These facts inspire us to devise techniques of interference mitigationin this band for IR UWB. While pulse shaping, energy detection, antenna diversity etc.based techniques have already been proposed, we have approached just another dimensionin this context. In lieu of the existing methods we used modified hermite pulses whosespectral properties are such that they have inherent interference rejection characteristics.These characteristics make them suitable for UWB BANs. In the next section the WBAN,its frequency bands, modulation methods and channel models are discussed briefly, the nextconsecutive sections deal with MHP and UWB WBAN signals and systems, Simulation andValidation respectively.

2 Wireless Body Area Networks

The Wireless Body Area Network (WBAN), a type of wireless sensor network (WSN)presents state of art technologies in the field of wireless communication and remote healthmonitoring. Tiny sensors are implanted on or inside human body or embedded in the externalclothing or worn like ornaments to collect vital health data in form of blood pressure, bloodsugar, heart rate, ECG, EEG and other vital parameters [1].

The collected data is closely monitored by medical personnel for any signs of abnormality.This type of monitoring has advantages in form of early detection of diseases and the efficientutilization of medical personal, whose number is scarce in many developing nations. Figure 1

Fig. 1 Placement of sensors inBAN

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D. K. Rout, S. Das

Fig. 2 Placement of sensors inBAN

shows the placement of sensors on human body to collect the vital health data in WBAN.Sensors have been positioned in areas where they can sense the required signals efficiently.IEEE 802.15.6 standard defines three types of nodes for WBAN namely Implant node,Bodysurface node and External node [2].

Implant nodes are the sensors that are implanted inside the human body and this couldbe anywhere inside the human body where they can sense the required signals efficiently.Sometimes multiple sensors may also be present to sense a single parameter. For instance asmany as 12 sensors may be used to measure ECG alone. Body surface nodes are either placedon the body or they come as wearable devices and they are in direct contact with the skin orat most two centimeters away. External nodes are not in direct contact with the skin. They areplaced at few centimeters to around five meters away from the human body. Figure 2 showsthe different implanted and non-implanted sensor nodes.

2.1 Spectrum Allocation for WBAN

The IEEE 802.15.6 standard has suggested the usage of specific frequency bands for theWBANs. While 400, 600, 900 MHz and the 2.4 GHz bands are narrowband, 3.1–10.6 GHzis wideband.

2.2 TH-BPSK for UWB WBAN

In IR-UWB, Time hopped (TH) and Direct Sequence (DS) UWB techniques are used. Modu-lation techniques like Pulse position modulation (PPM), Pulse Amplitude Modulation (PAM),On-Off Keying (OOK) and Binary Phase Shift Keying (BPSK) are the most preferred forthis purpose [10–12].

In our experimentation we have used TH-BPSK, though studies can also be extended toDS BPSK. BPSK is chosen for the purpose because it is an antipodal modulation techniqueand has advantages in comparison to PPM and other orthogonal modulation.

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Fig. 3 Impulse response of CM 3 channel model for UWB BAN

The equation for TH-BPSK is given by

s (t) = √Endk p

(t − iT f − kTc

)(1)

where p(t) is the pulse waveform, dk is {−1,1}, T f is the frame interval and Tc is the hopduration.

2.3 Channel Models

In [2,13,14] and [15] channel models have been proposed for BAN. These models are a resultof extensive measurements and modelling. The IEEE 802.15 Task Group 6 has released chan-nel models for the various scenarios of BAN [2]. The TG6 defines the channel models fromCM1 through CM4. While CM1 and CM2 deals with implant nodes and their communicationthrough body tissues the CM3 and CM4 are suggested for body surface nodes. In this paperwe have suggested the narrowband interference mitigation for 3.1–10.6 GHz UWB band andhave limited our work to CM3 channel model. The channel model CM3 can be given by Eq.(2) as

h (t) =L−1∑

l=0

al exp ( j∅l) δ (t − tl) (2)

where al is the path amplitude for the l − th path, ∅l is the phase for l − th path and it ismodelled by uniform distribution, δ (t) is the Dirac function, L is the number of arrival pathsand tl is the path arrival time for the l − th path. The impulse response of the channel usedfor simulation is shown in Fig. 3.

3 Narrowband Interference Overview

The UWB Body Area Network uses the 3.1–10.6 GHz band. This provides a bandwidth ofabout 7.5 GHz. Since this bandwidth is too wide the BAN will be affected by narrowbandinterference. Narrowband interference can be modeled as a form of partial band interference[17,18].

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Narrowband interference mitigation for the UWB BAN may be categorized into two types,interference mitigation at transmitter side, and interference mitigation at the receiver side.The mitigation at receiver side involves mostly of using multiple antennas to receive multiplereplicas of the signal and using them to cancel the interference, notch filtering based methodsare also used at the receiver end. At the transmitter end pulse shaping, and other energylimiting methods are applicable. The method proposed in this paper is implemented only atthe transmitting end.

4 MHP Overview

Hermite polynomials were proposed by Charles Hermite (1822-1901). Hermite pulses area set of orthogonal polynomials that exist over a domain (−∞, ∞). It is expressed by thepolynomial Hn(x) with weighting function e−x2

where n = 1, 2, 3, . . .. Hermite polynomialsare defined by the Hn(z) by the integral [19,20]

Hn (z) = n!2π i

∮e−t2+r z t−n−1dt (3)

The contour encloses the origin and is covered in anticlockwise direction.Equation (1) can be expressed by the Rodriges’ formula as

hen (t) = (−τ)n et2

2τ2dn

dtn

(e

−t2

2τ2

)(4)

where n = 0, 1, 2, 3, . . . and −∞ < t < ∞, are the hermite polynomials. Parameter τ isthe time scaling factor.

Hermite pulses must be modified and modulated to be used in communication systems.A modified hermite pulse of order n can be given by the equation below [11]

hn (t) = kne− t2

4τ2 hen (t) = (−τ)net2

4τ2 τ 2 dn

dtn

(e

−t2

2τ2

)(5)

= kne− t2

4τ2 n![n/2]∑

i=0

(−1

2

)i( t

τ

)n−2i

(n − 2i)! i ! (6)

where

kn =√

En

τn!√2π(7)

En is the energy of the pulse, τ time scaling factor defines the width of the pulse.Figure 4 shows modified hermite pulse and its spectral plot. If we look at the spectral

properties of the MHP then it can be noted that notches are present in its spectrum. And thenumber of notches is equal to n i.e. the order of the pulse.

However to get full benefit of this notch we should have flexibility in the frequency domain.The position of the notch should be varied according to the requirements. For this reason wehave to multiply a function w( f t) and the equation becomes

hwn = w ( f t) hn (t) (8)

where w(ft) may be a function to shift the frequency of the notch. The notch can be shifted bysimply multiplying a sinusoidal carrier like sin(2π f t) or cos(2π f t) where f is the centerfrequency of the interferer. Another way is to multiply an exponential function to the timedomain to get a shift in the frequency domain.

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Fig. 4 Modulated modified hermite pulses of order 0 through 6 and their spectral plot

We have used a pulse of order n = 1 it is because we have to combat a single interference.The pulse used and its spectral plot is shown in Fig. 5. The pulse of order 1 can transformedto the frequency domain as

H1 ( f ) = k1τ (− j4π f τ) 2√

πe−4π2τ 2 f 2(9)

It can also be noted from the figure that a notch is positioned at 4.9 GHz. This can bebeneficial in rejecting the interference from WLAN which operates at this frequency.

5 The UWB BAN Signals, System Model and Signal PropagationThrough the System

In UWB BAN the pulse shape of choice has been the Gaussian pulse which can be expressedby Eq. (10) below

w (t) = 1√2πσ

[

1 −(

t − μ

σ

)2]

exp

[

−1

2

(t − μ

σ

)2]

(10)

Where the pulse duration is Tw = 7σ and pulse center is at μ = 3.5σ .

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D. K. Rout, S. Das

-3 -2 -1 0 1 2 3-10

-5

0

5

10

Time

Am

plit

ude

100 101-65

-60

-55

-50

-45

-40

Frequency (GHz)

Pow

er/f

requ

ency

(dB

/Hz)

Periodogram Power Spectral Density Estimate

Fig. 5 MHP of Order 1 and Spectral plot with the notch at 4.9 GHz

In our simulations we have compared it with MHP pulses that can be expressed by Eq. (6).Using binary phase shift keying modulation expressed in Eq.(1) with unit energy the signalcan be modelled as

x (t) = dkw(t − iT f − kTc) (11)

where dk is the binary data to be transmitted in bipolar NRZ format and w(t) is the pulse.T f is the time hopping period for TH-BPSK and Tc is the chip duration.

The basic UWB BAN channel can be presented by Eq. (2). Applying the channel impulseresponse in Eq. (2) the signal in Eq. (11) can be expressed as

r (t) = x (t) ∗ h(t) =L−1∑

l=0

al exp ( j∅l)

∞∑

m=−∞x (m) δ (t − tl − m) (12)

where m is shifting parameter for the convolution operation.For a Pth order RAKE receiver the received signal can be expressed as

z (t) =P−1∑

p=0

r (t − pTz) (13)

where r(t) is the received TH-BPSK modulated signal from Eq. (12).In a rake receiver with P fingers a total of P different replicas of the signal can be received

and hence the signal in Eq. (12), transforms to

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z (t) =P−1∑

p=0

ap exp(

j∅p) ∞∑

m=−∞x (m) δ

(t − tp − m

)(14)

Refering [18] the narrowband interference can be modelled as a special case of partial bandjamming. In narrowband interference a fraction of the total available bandwidth is affectedby the interference signal. If the total bandwidth is denoted by W, and it is being affected byinterference of bandwidth Wi , a parameter ζ may be used to present the ratio of the UWBsignal’s Power spectral density (PSD) to that of the interfering signal. Mathematically thismay be represented as

ζ = S( f j )

S( fc)≤ 1 (15)

where S( f j ) is the PSD of the interference of bandwidth Wi and S( fc) is the PSD of theUWB BAN pulse of bandwidth W .

In such a case the bit error probability of BPSK modulated signal in presence of narrow-band interference is hence given by [17,18]

Pe,bpsk = Q

(√Bit energy

Noise + interference

)

(16)

= Q

⎛

⎜⎝

√√√√

Eb(N02

)+ ζ · S( f j )

⎞

⎟⎠ (17)

For UWB WBAN channel the channel distortion will be added to the above equation andthe equation changes to

Pec,bpsk = Q

⎛

⎜⎝

√√√√

Eb(

N02

)+ Nc

2 + ζ · S( f j )

⎞

⎟⎠ (18)

where Eb is the energy of the pulse, N0 is the Gaussian noise, Nc is the channel distortionmodelled by uniform distribution [21], S( f j ) is the narrowband interference energy.

The basic UWB WBAN system can be designed as shown in Fig. 6. The signal acquiredfrom the human body are first sampled and converted to digital form using an A/D converter.An efficient source coding algorithm should be used for coding the acquired signal. Thedigital data is then modulated using the TH-BPSK modulation and transmitted through theWBAN channel. In the channel noise and interference get added to the already faded signal.This signal is then received through a RAKE receiver.

MRI image and ECG data have been transmitted through the system in order to validatethe performance of the system and the proposed techniques.

6 Experimentation and Simulation Results

6.1 Experimentation Procedure

The WBAN system used for experimentation consists of the blocks shown in Fig. 6. TheBAN channel model was constructed by studying the research literatures [2] and [15–17]. Thechannel impulse responses were compared to that present in the above research papers. Once

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WBAN Channel

A/D conversion

TH BPSK modulation

Signal acquisition

RAKE receiver

Maximum Likelihood Detector

IEEE 802.11y Interference Generation

Gaussian noise generator

Bit error rate calculation

Fig. 6 Block diagram representation of UWB WBAN system

the channel conditions were verified random data was generated and BPSK modulated usingboth Gaussian and MHP pulses. The data was filtered through the BAN channels and additivewhite Gaussian noise was added to it. Meanwhile narrowband interference was added to thesignal. Figure 7 shows the spectrum of the transmitted signal and added interference

The signal reception was done using (All RAKE) RAKE receiver with maximum likeli-hood detection. The received data was then compared with the transmitted data and the biterror rates were recorded.

The interference was added with different levels of signal to noise ratio. Initially theinterfering signal’s signal to noise ratio per bit was made equal and hence it was increasedfrom 10 to 40 dB. In the next phase the interference signal was kept fixed at its maximumlevel and the number of RAKE fingers was varied.

6.2 Simulation Results

Simulations were carried out to find the bit error rate at different levels of interference. It canbe noted in Fig. 8 that as we increase the SNR of the interfering signal, the interference tendsto be stronger and the Gaussian noise power starts decaying.

Fig. 7 Presence of NB interference with the MHP

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-20 -10 0 10 20 30 4010

-5

10-4

10-3

10-2

10-1

100

Interference level at 10-40dB SNR

Eb/N0 in dB

BE

R

gaussian 10dB

mhp10dB

gaussian 20dB

mhp20dB

gaussian 30dB

mhp30dBgaussian 40dB

mhp40dB

Fig. 8 Performance of MHP and Gaussian pulse in the presence of NB interference

0 5 10 15 20 2510

-5

10-4

10-3

10-2

10-1

100

Eb/N0

BE

R

MHP and Gaussian pulses in UWB BAN CM3 channel model with n finger

no rake MHP4 finger MHP8 finger MHP16 finger MHPno rake Gaussian4 finger Gaussian8 finger Gaussian16 finger Gaussian

RAKE receiver

Fig. 9 Performance of MHP and Gaussian pulses in UWB BAN CM3 channel model with n finger RAKEreceiver

This affects the Gaussian pulse and the bit error rate starts increasing. But in the MHP sincethere is an inherent notch, exactly at the center frequency of the interfering signal hence it isnot affected. It is also evident from the figure that as the SNR of the interfering signal increasesthe Gaussian noise reduces hence the performance of MHP pulsed TH-BPSK improves.

As the interference SNR level was increased it was further noted that after a particularlevel the bit error rate becomes constant. It is because the interferer affects a small portionof the total bandwidth occupied by our transmitted signal and then as we increase the SNRof the interfering signal after a particular level the Gaussian noise power is reduced almostto zero. Hence not further effect of it is observed.

In Fig. 9 MHP and Gaussian pulsed TH-BPSK were tested using an n-finger RAKEreceiver. The performance has been tested from no RAKE to 16 RAKE. It can be notedthat in WBAN our requirements will be to use fewer fingers in a RAKE receiver since theavailable resources in an implanted sensor are very limited. It can be seen from Fig. 9 that inthe presence of interference MHP performs better than Gaussian pulse. As we increase the

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number of RAKE fingers the performance of both the schemes improves. It can be furthernoted that the performance achieved using a 4 finger RAKE receiver can be achieved withoutRAKE in MHP.

7 Validation

The WBAN’s main objective is to transmit signals captured from the BAN sensors. Hencethe performance of designed WBAN using both MHP and Gaussian pulses on these signalsmust be observed. MRI finds application in detection of cancer and tumors in patients. Someworks presented by [22] and [23] present new insights into this. In future it may be desirableto send MRI images through the WBAN channel.

The effect of interference on MRI signals have been presented in Figs. 10 through 15.While fig a. in Figs. 10 through 15 show the transmitted MRI image, fig b. through h presented

Fig. 10 MRI image received using Gaussian pulse without RAKE and Eb/N0 0–30 dB

Fig. 11 MRI image received using MHP pulse without RAKE and Eb/N0 0–30 dB

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Fig. 12 MRI image received using Gaussian pulse with a 4 finger RAKE receiver with Eb/N0 0–30 dB

Fig. 13 MRI image received using MHP pulse with a 4 finger RAKE receiver with Eb/N0 0–30 dB

the received images at different levels of Signal to noise ratio per bit i.e. Eb/N0. Fig b. hasbeen received at the signal to noise ratio per bit of 0 dB, a successive increment of 5 dB hasbeen done for the next figures.

In Figs. 10 and 11 shows the received images for Eb/N0 of 0–30 dB for reception withouta RAKE receiver. It can be observed that due to the absence of RAKE receiver there are highlevel of distortions in the images. In Fig. 10 the images are a result of using the Gaussianpulse shape. The Gaussian pulse is affected by the interference and noise hence the receivedMRI image is degraded. The performance does not improve till it reaches Eb/N0 of around25 dB. In the Fig. 11 which uses the MHP pulse has a similar response but it performs better.Though it is affected by the channel fading and noise but is immune to the interference henceacceptable image quality is received in Fig. 11d, e. Similarly in Figs. 12 and 13 the MRIimage has been received using a 4 finger all RAKE receiver. It can be observed here thatagain MHP performs better than Gaussian pulse in combating interference.

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Fig. 14 MRI image received using Gaussian pulse with a 8 finger RAKE receiver with Eb/N0 0–30 dB

Fig. 15 MRI image received using MHP with a 4 finger RAKE receiver with Eb/N0 0–30 dB

In Figs. 14 and 15 the MRI signal has been received using an 8 finger all RAKE receiver.While it can be observed that on increasing the number of RAKE fingers the performance ofboth the techniques improve drastically, MHP is superior in interference mitigation comparedto the Gaussian pulse.

Cardiac diseases have become common and lethal in many developed and developingcountries. A fact is that these diseases can be easily detected by detecting anomaly inECG [24]. Hence the performance of our proposed pulse for carrying ECG signals must betested.

Figures 16 through 21 show the performance of MHP in comparison with GaussianPulse in ECG signal transmission through the UWB WBAN channel. While in Figs. 16,17, 18, 19, 20 and 21 subfigure a displays the transmitted ECG, subfigures b through hdisplay the received ECG at various levels of Eb/N0. Figure 16b–e shows that for no rakethe ECG signal is highly affected up to the Eb/N0 of 20 dB for Gaussian pulse. While inFig. 17 it can be seen that the ECG signal is noise free after it reaches an Eb/N0 of about

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Fig. 16 ECG signal received using Gaussian pulse without RAKE receiver with Eb/N0 0–30 dB

Fig. 17 ECG signal received using MHP without RAKE receiver with Eb/N0 0–30 dB

10 dB. In Figs. 18, 19, 20 and 21 the performance has been shown for 4 RAKE and 8RAKE. Here also as we increase the number of RAKE fingers the performance of boththe techniques improve drastically. The larger the number of RAKE fingers the better theperformance and more the complexity. A tradeoff between performance and complexityshould be chosen. It is recommended that a 4 RAKE receiver will be an ideal choice tosupport the tradeoff discussed. It can be observed here that MHP is far superior in perfor-mance in comparison with Gaussian pulse in the presence of interference for carrying ECGsignals.

It is proved from the simulations that the usage of MHP pulse in place of Gaussian improvesthe performance of a system in the presence of interference. The interference immunity ofthe MHP pulse is due to the inherent notch present in its spectrum.

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Fig. 18 ECG signal received using Gaussian pulse with a 4 finger RAKE receiver with Eb/N0 0–30 dB

Fig. 19 ECG signal received using MHP with a 4 finger RAKE receiver with Eb/N0 0–30 dB

8 Conclusion

Body Area Networks are being designed to provide healthcare facilities to mobile patients.The most important concern in the BAN is its reliability and immunity to interference whichis important in order to provide uninterrupted communications. This article presented per-formance of TH-BPSK in the BAN CM3 channel using MHP and Gaussian pulse shapes inpresence of IEEE 802.11y narrowband interference. It is proved from the above findings thatthe MHP is far superior to the Gaussian pulse due to its interference mitigation properties.Since in the BAN available resources are limited, this makes our proposed method not onlysuitable and reliable but also technically highly feasible.

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Fig. 20 ECG signal received using Gaussian pulse with a 8 finger RAKE receiver with Eb/N0 0–30 dB

Fig. 21 ECG signal received using MHP with 8 finger RAKE receiver with Eb/N0 0–30 dB

Future work in this area may involve methods to suppress multiple interferences usingMHP pulses of different order. The research may be extended to other areas involving wide-band communications.

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Deepak Kumar Rout received his Bachelor of Technology degreefrom BPUT, Rourkela, India in 2007 and Master of Technology degreefrom VSSUT, Burla (formerly University College of Engineering,Burla) with specialization in Communication Systems Engineering in2011. He is currently pursuing his Ph.D. in the Department of Electri-cal Engineering of National Institute of Technology, Rourkela, India.His research areas include Body Area Networks, Wireless Networks,Mobile Computing, Signal Processing and Telemedicine.

Susmita Das received her B.Sc. Engg in Electrical Engineering fromCollege of Engineering and Technology, Bhubaneswar, Orissa andM.Sc. Engg in Electrical Engineering with specialization in Electron-ics System and Communication from National Institute of Technol-ogy, Rourkela. She joined National Institute of Technology, Rourkelain 1991 and she is currently working there as associate professor in theDepartment of Electrical Engineering. Her research areas include Dig-ital Signal Processing, Digital communication, Artificial Neural Net-works, Fuzzy logic, Channel Equalisation, and System Identification.

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Interference Mitigation in Wireless Body Area Networks Using Modified and Modulated MHP