Mimo and cooperative mimo comparison in energy constrained wireless sensor networks

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 04 | Apr-2015, Available @ http://www.ijret.org 566
MIMO AND COOPERATIVE MIMO COMPARISON IN ENERGY
CONSTRAINED WIRELESS SENSOR NETWORKS
Meethu Abraham1
, Sudhansu Sekhar Singh2
1
M.Tech Student, School of Electronics Engineering, KIIT University, Bhubaneswar, India
2
Professor, School of Electronics Engineering, KIIT University, Bhubaneswar, India
Abstract
In Wireless Sensor Network commonly referred as WSN, the hubs or nodes are operated by batteries so that the energy utilization
should be diminished, while fulfilling the given throughput and given requirement. The paper studies about the performance and
energy consumption of cooperative MIMO and MIMO (multi input multi output) based communication. The average energy usage
comprises circuit energy and transmission energy consumption. The comparison between the multi-input- multi-output (MIMO)
and cooperative MIMO techniques help us to choose the best scheme for energy constrained wireless sensor network application.
The simulation result shows that energy efficiency of MIMO (multi-input-multi-output) and SISO (single-input-single-output) is
better for longer distances and thud increase the system life time.
Keywords: Cooperative MIMO, MISO, SISO, SIMO, wireless sensor network, energy efficiency, BER performance
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1. INTRODUCTION
In the recent advance in science and technology, the
communication system has been marching towards quick
development. Wireless sensor network is one of the
important blocks for communication. The major limiting
factors in wireless sensor network are power consumption
requirements, life time of network, data integrity and data
confidentiality. A sensor network is a remote system
comprising of spatially appropriated self governing sensors
to monitor physical or ecological conditions for examples
temperature, sound, and so forth and to pass their
information through the system to a principle area. Each
sensor nodes is accumulation of sensor hardware ,
microcontrollers, few limit of RAM and program memory ,
a remote handset and the power supply i.e. battery. The
sensor hubs used are small in size and cheap. WSN can be
utilized as a part of numerous courses in modern industrial
facility computation. Some of the application is monitoring
of equipment, military surveillance and machinery health
.WSN can be used for leaking or radioactive monitoring in
chemical plant. The two main core challenges in WSN are
energy efficiency and scalability[1]. In many cases, the
replacement of battery is not possible so that energy
utilization should be decreased and for the data transfer
energy saving transmission scheme should be used in the
sensor networks.
With using highest diversity gain the transmission power
can be lessened thus lower the transmission rate and
increase the reliability. The idea of cooperative MIMO
presented in WSN using cooperative nature of sensor node
to achieve higher reliability link of communication and
lessen transmission puissance. In traditional MIMO, the
multiple antenna are appended to one node or other hub but
in cooperative MIMO, multiple nodes cooperate to receive
or transmit the signal. The numerous hubs or nodes were
physically gathered together to receive or transmit .
In [2] it presented that with equal transmit power and similar
BER specification MIMO scheme exploits at data rate and
thus requires less transmission energy when compared to
SISO system. Due to the restricted physical size of node, the
direct application of multiple antenna to the system is
impossible. The cooperative MIMO can be developed by
permitting single antenna node to coordinate on
transmission or gathering so that energy efficient scheme
can be deployed. The techniques of energy efficiency
dissipation mainly concerned on reducing the transmission
energy and it used in larger domain applications. On the
other hand, in small range applications the consumption of
circuit energy is identical which governs the transmission
energy. The total energy dissipated in the circuit includes
the energy dissipated in circuit blocks with signal way i.e.
analog to digital converter (ADC), filter, mixer, low noise -
amplifier (LNA), frequency synthesizer, intermediate -
frequency amplifier (IFA) and power amplifier. In MIMO
systems though the complexity of the circuit will little
higher than that of SISO system but it outperforms it in
terms of energy efficiency.
Under same BER ( bit error rate) and throughput, first we
compare the energy usage of simple MIMO system with
reference SISO systems and then the cooperative MIMO.
The energy consumption is compared with the transmission
distances.
The remaining part of the paper is organized in the
following order . The analytical model for the energy
consumption of MIMO system is discuss in the section 2 of
the paper while the energy consumption model for
cooperative MIMO scheme is explained in section 3. All the
attributes consider for the simulation and the obtained
results are presented in section 4. The conclusive remarks
and the scope for future work is projected in section 5.

_______________________________________________________________________________________
2. ENERGY EFFICIENCY OF MIMO SYSTEMS
Here, the communication link which connecting between the
wireless nodes or hubs may be MIMO (multi input-multi-
output), MISO (multiple-input–single-output), SIMO
(single-input–multiple-output), or SISO (single-input–
multiple-output). The average energy consumption is
calculated considering each signal processing unit at the
receiver and the transmitter . In this model, baseband
processing signal sections or blocks (i.e. pulse-shaping,
source coding and digital modulation) are excluded and
error-correction-code (ECC) blocks are also omitted. The
figure 1 and 2 represents path of transmitter and receiver
side thus Nt and Nr denotes the transmitter and receiver
antennas respectively[3]. In SISO case, Ntx = Nrx = 1.
Fig 1. Transmitter Blocks
Fig 2. Receiver Blocks
The average power ultization or consumption for signal path
is comprises of two primary parts and they are power
consumption for the PAMP i.e. power amplifier and power
consumption for PC i.e. circuit blocks. The power amplifier
PAMP is subject to transmit power Poutput which is described
below:
Poutput = Ebit Rbit x (4πd2
) Mlk Nfk (1)
GtxGrx λ2
Here Rbit is the bit rate , Ebit is energy per bit for given BER
(bit error rate) requirement at receiver side, transmission
distance is d, Gtx and Grx is transmitter (TX) and receiver
(RX) antenna gain respectively, the carrier - wavelength is
λ, Mlk is link margin and other added background
interference or noise, and then Nfk is receiver (RX) noise
figure defined as Nfk = (Nrk / N0 ) and thus N0 = -172
dBm/Hz thermal noise power spectral density at room
temperature and then Nrk is the power spectral density of the
average effective noise seen at receiver input.
The power amplifier average power consumption is
PAMP = (1+αl) Poutput (2)
Here, αl = (ξl / ηl) - 1, where ξl is PAR (peak to average
ratio) and ηl is drain efficiency of radio frequency power
amplifier.
The power consumption for circuit block PC is calculated by
PC = Ntx (Pmixer + PDAC + Pfiltx) + 2Psync + Nrx (PLNA + Pmixer +
PADC + PIFA + Pfilrx) (3)
where PDAC , PLNA , PADC , Pmixer ,Pfiltx , PIFA , Pfilrx , Psync are
power consumption rate for digital to analog converter
(DAC) ,low noise - amplifier (LNA), analog to digital
converter (ADC), the mixer , transmitter side active filters,
the intermediate frequency- amplifier (IFA), active filters at
receiver-side and frequency-synthesizer, respectively.
The average energy consumption per bit of system can be
written by
Ebtotal = (PAMP + PC) / Rbit (4)
The Alamouti scheme are used in this paper to exploit
diversity from MIMO schemes. To employ Alamouti code
in the MIMO scheme, a pair of antenna are used with two
separate symbols s1and s2 which are transmitted all the
while amid the first image period from antenna 1 and 2, took
after by signs –s2*and s1*from antennas1 and 2,
individually , amid next symbol period. Rayleigh fading
channels MIMO scheme based can accomplish lower
normal likelihood of mistake than SISO scheme under same
transmit energy spending plan because of the diversity gain.
At the same bit error rate and throughput prerequisite, multi-
input-multi-output(MIMO) schemes require less
transmission vitality than single-input-single-output (SISO)
schemes.
As per the Chernoff bound (at high SNR- regime)
Pbit ≤ -Ntx
(5)
the the upper bound for energy per bit is
Ebit = Ntx N0 (6)
Pbit
1/Ntx
By using the above bound as equality, we have estimated
average energy consumption of MISO scheme and the SISO
scheme as per equations (1) and (4). Thus, we can get
Filter FilterFilterLNA IFA ADCMixer
LO
x Nrx
Filter FilterMixer PA
DAC
LO
x Ntx

_______________________________________________________________________________________
Ebtotal = (1+αl) Ntx N0 x (4πd2
) Mlk Nfk + PC (7)
Pbit
1/Ntx
GtxGrx λ2
Rbit
3. MIMO WITH MULTINODE COOPERATION
The primary concern of the sensor networks is to expand the
network lifetime .Since sensor systems are chiefly intended
to chip on some -joint task where every hub reasonableness
is not underscored, outline plan is to reduce the aggregate
energy utilization in system as opposed to reducing energy
utilization of individual hubs or nodes. Here, we suggest a
methodology for reducing the average energy utilization or
consumption of multiple hubs from the system viewpoint.
The information which colllected by numerous
neighborhood sensors is transmitted to remote processor in
sensor network system. The data collected will be first
transmit it to the relay node if remote controller is far away,
after that multi - hop based routing is used to send
information to its last destination. This situation is outlined
in figure 3.
Fig 3. Cooperative MIMO scheme for WSN
The additional amount of energy used for the local co-
operative information exchange is based on number of
antennas as well as the inter node distance between the
cooperative hubs or nodes at transmitting and receiving
sides. The inter-node distance is required to fluctuate from 1
to 7m based on the geological design of system. We can
consider that there is Nbt bits to broadcast from the hub
source to hub destination . Here, we have Nt number of hubs
and Nr number of hubs to cooperate at transmitter and
receiver sides .
At transmitter side, source node broadcasts its Nbt bits to
(Ntx − 1) agreeable nodes. The SISO scheme is the most
energy effective for the short range nearby separation dm
.Here expect, there are simply single hop single-input-
single-output (SISO) scheme transmissions between two
agreeable hubs and there an un-coded 16-QAM (quadrature
amplitude modulation) is utilized over the channel with K
law path loss considering it only for AWGN environment.
The 16 QAM modulation scheme permits the reduction of
circuit utilization. According to SISO non co-operative
scheme, one can estimate the energy spent per bit for the co-
operative transmission EbcoopTX ( for that d = dn and (Ntx − 1)
receiving nodes).
In transmission side, the additional co-operative energy
utilization or consumption ETXbtcoop is based on energy
consumption per bit ETXbcoop and estimated as
ETXbtcoop = Nbt ETX bcoop (8)
After getting Nbt bits from hub S, then cooperative-nodes Ntx
modulates and accordingly they will align their bits to
QPSK- STBC images and afterward broadcast all while to
the destination hub over the MIMO scheme Rayleigh fading
channel.
At the receiver side, then cooperative nodes Nrx −1 firstly
gets the MIMO generated symbols and then do quantization
for the STBC symbols to Nss bit after that it transmit again
the quantized bits for the destination hub utilizing un-coded
16 QAM modulation for SISO. At receiver side extra co-
operative energy utilization EbtcoopRX is based on Nrx , Nss
and ERXbcoop the energy consumption of SISO scheme that
can be computed by utilizing the SISO 16 QAM
transmission scheme for dn distance. ERXbtcoop is estimated as
ERXbtcoop = Nss (Nrx −1) Nbt ERXbcoop (9)
The average energy consumption of cooperative MIMO is
ETOTAL = Ebtotal + ETXbtcoop + ERXbtcoop (10)
4. SIMULATION RESULTS
The result is based on the MATLAB simulation. The
simulation were done using the parameters present in the
table 1. Findings predicated on simulation utilizing
mathematical model provide subsidiary insights into certain
performance aspects and identifying promising solutions for
the energy-efficient WSNs.
Table 1. System parameters
fcr = 2.5 GHz η(eff) = 0.35
Gtx Grx = 5 dBi N0 = -172dBm/Hz
BW = 10 KHz Ts = 1/BW
Pmixer = 30.4 mW PSYNC = 50.3 mW
Pbt = 10 -3
ɮ = 1
Pfiltx = Pfiltx= 2.5mW PLNA = 20 mW
Nfk = 10 dB MLk = 40 dB
From figure 4 shows that M-ary Quadrature Amplitude
Modulation (MQAM) modulation schemes is preferred for
better BER performance when Rayleigh fading is present in
wireless sensor networks.
TX
Destination
RXd
dn

_______________________________________________________________________________________
8 10 12 14 16 18 20
10
-3
10
-2
10
-1
10
0
Eb
/No
(dB)
BER
QAM
DPSK
FSK
Fig 4. BER performance comparison of different
modulation techniques in Rayleigh fading channel
0 10 20 30 40 50 60 70 80 90 100
0
1
2
3
4
5
6
x10
11
distanceinm
totalenergy
MISOVSSISO
NT=4NR=1
NT=3NR=1
NT=2NR=1
NT=1NR=1
Fig 5. Energy consumption of MISO and SISO system
The figure 5 compares average energy consumption per bit
along the transmission distances between the MISO scheme
and the SISO scheme in WSN . According to the figure 5, it
can analyze MISO scheme requires the less transmission
energy for long range application, then average energy
consumption will becomes lesser when compared with SISO
scheme. When the distances between transmission increases
then respectively the energy consumption also increases.
0 100 200 300 400 500 600 700 800 900 1000
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
x10
13
distanceinmtotalenergy
MIMOVSMISO
NT=4NR=4
NT=2NR=2
NT=3NR=2
NT=4NR=2
NT=4NR=1
Fig 6. Energy consumption for MISO and MIMO
The figure 6 compares average energy consumption along
the transmission distances that between the MIMO scheme
and the MISO scheme in WSN .According to the figure 6, it
can analyze MIMO scheme requires less transmission
energy for long range application, then average energy
consumption will becomes lesser when compared with
MISO scheme. From the figure we can also determines that
incrementing the transmission nodes is a good option than
incrementing the receiving nodes.
0 50 100 150 200 250 300 350 400 450 500
0
0.5
1
1.5
2
2.5
3
3.5
x10
17
distanceinm
totalenergy
CMIMOVSMIMO
CMIMO
MIMO
Fig 7. Energy consumption for MIMO and Cooperative
MIMO

_______________________________________________________________________________________
The figure 7 compares average energy consumption with the
transmission distances between MIMO scheme and
cooperative MIMO scheme in WSN. According to the figure
7, it can analyze cooperative MIMO scheme requires less
transmission energy from distance greater than 200m, then
average energy consumption will becomes smaller when
compared with MIMO. When the transmission distance is
less than 200 m then the MIMO energy consumption is low.
5. CONCLUSION
The paper analyzed the average energy consumption
performances of different diversity schemes antennas. The
result shows that, Cooperative MIMO performs better than
MIMO scheme. And the MISO scheme and MIMO scheme
are more energy saving than SISO scheme . For determining
the best option of Ntx and Nrx for the given transmission
distance, the MIMO schemes selection can be performed.
The introduction of cooperative MIMO scheme for 2 x 2
antennas configuration, that requires the less amount of the
network source so it had better energy efficiency when
compared with 3x2 and 4x2 antenna configuration. The
cooperative MIMO scheme appears to be preferable than
SISO schemes, but it is very much prone towards channel
estimation errors and precise transmission synchronization
remains highly essential for MIMO system.
ACKNOWLEDGEMENTS
The authors appreciated the support provided by school of
Electronics Engineering, KIIT University, Bhubaneswar for
the technical assistances.
REFERENCES
[1]. S. Mishra, H. Thakkar, " Features of WSN and Data
Aggregation techniques in WSN : A Survey", International
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April ,2011.
[2]. A. Paulraj, R. Nabar, and D. Gore, "Introduction to
Space Time Wireless Communications", Cambridge, U.K
Cambridge Univ. Press, 2003.
[3]. S. Cui, A. J. Goldsmith, and A. Bahai, “Energy-
Efficiency of MIMO and Cooperative MIMO Techniques in
sensor networks,” IEEE journal on selected areas in
communication, vol.22, August 2006.
[4]. S. Cui, A. J. Goldsmith, and A. Bahai “Energy-
constrained modulation optimization,” IEEE transactions on
wireless communications, vol. 4, NO. 5, September 2005.
[5]. S. Cui, A. J. Goldsmith, and A. Bahai, “Modulation
optimization under energy constraints,” in Proc. ICC’03,
AK, May 2003.
[6]. S. K. Jayaweera, “Energy analysis of mimo techniques
in wireless sensor networks ,” in 38th Annual Conference on
Information Science sand System, Princeton University,
USA, March2004.
[7]. Duc Nguyen, Olivier Berder ,Olivier Sentieys,
"Cooperative MIMO schemes optimal selection for wireless
sensor networks " IEEE, 2007.
[8]. Vibhav Kumar Sachan1,Syed A. Imam,M. T. Beg, "
Design of Energy-Efficient Wireless Sensor Networks
Using Cooperative MIMO Techniques ", International
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[9]. Yongxian Song , Rongbiao Zhang , Zhuo
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BIOGRAPHIES
Meethu Abraham is pursing M.Tech in
Communication System Engineering
from KIIT University, Bhubaneswar,
India. She had obtained B.Tech in
Electronics and Communication
Engineering from Maharshi Dayanand
University, Rohtak, India in the year
2013. She has published paper in IJCA.
Dr. Sudhansu Sekhar Singh has
received a PhD in Engineering
(Mobile Communication) from
Jadavpur University, Kolkata, India
and M.E. in Electronic System and
Communication Engineering from
NIT Rourkela, India. He is working as
a Professor in School of Electronics
Engineering, KIIT University,
Bhubaneswar, India. He is also the academic council
member and board of studies member of different schools of
the university. More than forty five publications in
International journals and reputed international conference
proceedings are to his credit. He has also guided more than
twenty PG thesis and examined several of doctoral
dissertations. His broad research area includes but not
certainly limited to wireless and mobile communication,
multicarrier CDMA, MIMO-OFDM, Wireless Sensor
Networks.

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