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346 Copyright © 2017. Vandana Publications. All Rights Reserved.
Volume-7, Issue-3, May-June 2017
International Journal of Engineering and Management Research
Page Number: 346-452
UWA Communication using MIMO OFDM
Sanjay K. Sharma1, Utkarsha Sharma
2
1Assistant Professor,
2Dual Degree Scholar,
1Department of Electronics & Communication Engineering UIT, RGPV, INDIA
2Department of Electronics & Communication Engineering UIT, RGPV, INDIA
ABSTRACT Various researches has been carried out to explore the
effective ways of communication inside the water between the
submarines or to collect information from sensors inside the
water. A methodology proposed in this paper uses combination
of Multiple Input Multiple Output (MIMO) and Orthogonal
Frequency Division Multiplexing (OFDM) along with QAM and
BPSK Modulation Schemes and LDPC Coding which increases
the reliability of UWAC. Both Radio and Under Water
Communication are almost similar. The difference lies in
Density and Speed of Water. Transmission Speed of water is
generally 1500 m/sec and its density is high.
Keywords-- Shallow Water Communication, LDPC, MIMO-
OFDM, QAM and BPSK
I. INTRODUCTION
The ability to communicate effectively underwater
has various applications for marine researchers and industrial
operators, oceanographers, and different organizations.
Electromagnetic waves cannot propagate over long distances
in sea water, therefore acoustic waver are preferred for
communication in under water. Underwater acoustic (UWA)
communications has been a troublesome problem because of
unique channel characteristics like the fading, extended
multipath and also the refractive characteristics of the sound
channel [1, 2].
In this work we introduce a combination of multiple
input multiple output (MIMO) and orthogonal frequency
division multiplexing (OFDM) with low complex
modulation techniques and also less complex coding
techniques to communicate our signal in shallow water
acoustic communication. We analyzed various modulation
technique on the basis of two parameters, BER and FER. The
Binary phase shift key (BPSK) technique is less complex in
comparison with other modulation techniques as we know
that in underwater the bandwidth is very less which is 5 khz
that‟s why BPSK consume low bit error rate (BER) as well
as low signal to noise ratio (SNR). The proposed technique
will be simulated with the help of MATLAB R2013b.
Since OFDM provides support
of additional antennas and larger bandwidths as it
simplifies equalization in MIMO systems, combination of
MIMO-OFDM is very advantageous for communication. By
conjointly using Multiple-Input Multiple-Output (MIMO)
and Orthogonal Frequency-Division Multiplexing (OFDM)
technologies, data rates up to hundreds of M bits/s could be
reached by indoor wireless systems and they can also
attain spectral efficiencies of several tens of
bits/Hz/s, that are undoable for typical single-input single-
output systems. This enhancements of data rate and
spectral efficiency is due to the fact that MIMO and OFDM
schemes are so parallel transmission technologies in
the space and frequency domains, respectively.
When OFDM signal is transmitted through variety of
antennas in order to attain diversity or to achieve higher
transmission rate then it's called MIMO-OFDM.
MIMO-OFDM is the efficient solution for
transmitting and receiving the data over the long distance.
The sub-carrier frequency has been chosen in our proposed
OFDM transceivers so that cross-talk between the sub-
channels are eliminated, hence the inter carrier guard bands
are not required and we have also used such type of guard
band for eliminating the cross-talk between channels. This
greatly simplifies the design of both the transmitter and the
receiver; unlike conventional FDM, a separate filter for each
sub-channel is not required.
FER= 1- (1-BER)4
1.1 (in this case)
FER=
1.2
Correlation between Es /No and Eb /No (SNR)
Es /No = Eb /No (dB) +10log10(k) 1.3 Where k is the number of information per symbol
Es/No =ratio of symbol energy to noise power spectral density
Eb/No = ratio of bit energy to spectral power density
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II. PROPOSED METHODOLOGY
In the upcoming years MIMO has drifted enormous
amount of attention of researchers in the field of wireless
communication. Multipath fading is main factor in increasing
the data rate and reliability of transfer of information over
wireless channel. To improve reliability, channel coding
techniques which are used to meet the requirements of
today‟s multimedia communications is insufficient.
Figure 1: Block diagram of the proposed shallow water communication system
Increased spectral potency for a given total transmit
power is obtained through wireless communication using
multiple-input multiple-output (MIMO) systems. That
enhanced the capacity that's achieved by introducing further
spatial channels which are attained by using space-time
coding. The environmental factors have an effect on MIMO
capacity. Those factors embrace channel complexity,
interference, and channel estimation error. If multiple
antennas are used at transmitter or receiver, it will improve
data rate and reliability.
In Fig.1 the block diagram of the proposed approach
is provided. Modulation of data using BPSK and QAM
followed by Serial to parallel conversion of modulated signal
are the major blocks. Before transmission of signal cyclic
prefix are added, the signal has been coded with LPDC and
modulated by Orthogonal Frequency Division Multiplexing
(OFDM). FER means the combination or group of bits
referred to as frames.
During transmission through channel, signal reaches
the receiver end before encountering with the various noises.
AWGN is generally a basic noise model to imitate the effect
of many random processes that take place in nature. On the
receiver the reverse method of transmitter is taken place and
the data will be taken out.
The above described block diagram of the proposed
methodology is then implemented on simulation tool. The
execution of the simulation algorithm is explained step by
step as follows:
i. Start simulation
ii. Create simulation environment using variable
initialization
iii. Generate random data for transmission over system
iv. Modulate data with BPSK and QPSK Modulation
v. Convert signal from serial to parallel
vi. Code signal with LDPC coding
vii. Perform OFDM Modulation i.e. IFFT
viii. Add cyclic prefix
ix. Transmit channel and add noises
x. Remove cyclic prefix
xi. Perform OFDM demodulation that is FFT
xii. Decoding with LDPC coding
xiii. Convert parallel data to serial
xiv. Demodulate data with BPSK/QAM modulation
xv. Calculate Error Rate
xvi. Compare and display results
xvii. End of Simulation
III. MATHEMATICAL MODDELING
During the simulation, we have taken the equivalent
model with parallel flat-fading sub channels. Ignoring
intercarrier interference (ICI), the signal in the k-th sub
channels can be represented as
. 1,2......k k pZ H k d k v k k 3.1
Where d[k] is the data symbol to be transmitted
over k-th subcarrier, kp is the number of subcarrier and H[k]
is the channel frequency response of the k-th subcarrier, vk is
the additive noise.
On the SWA multipath channel the coefficient H[k]
can be related to discrete time base-band channel
parameterized by Lp+1 complex value coefficients
{* + } through
2
0
p p
j pk
L k
ppH k e
3.2
BPSK / QAM
Modulation
Serial To
Parallel
Conversion
LDPC CodingOFDM
Modulation
(IFFT)
Adding Cyclic
Prefix
Remove Cyclic
Prefix
OFDM
Demodulation
(FFT)
LDPC
Decoding
Parallel to
Serial
Conversion
Demodulation
Data
Input
Data
Output
Channel with
Noises
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As long as kp≥ Lp+1, we can rewrite as
⌊
⌋=⌊
⌋ [
, - , -
]
[
]
[
] 3.3
Then, after introducing MIMO-OFDM the transmitted
OFDM signal is defined as –
i
s
N
k
iTtfj
ki iTtfectsSC
sk )()(1
)(2
3.4
ttt
tttf
G
sG
,,0
,1)( 3.5
ss
kt
ft
kf
1,
1
3.6
sGs tT 3.7
s
SC
TN
TR
1
3.8
Where
NSC -Number of subcarriers
Ki- ith
information symbol at the kth
subcarrier
f k- Frequency of the kth
subcarrier
Ts- OFDM symbol period
ts- Guard interval length, the observation period often called
„„useful symbol length,‟‟ and
f(t)- Rectangular pulse waveform of the symbol
R (1/T) is the total symbol transmission rate.
When we limit our interest only within binary PSK (BPSK)
or QPSK at all the subcarriers, the information symbol is
given by
1,....1,0
2
Mmec M
mj
ki
3.9
WherekMM 2 , so kM= 1 for BPSK and kM= 2 for QPSK.
IFFT/FFT
IFFT-
( )
∑ ( ) 3.10
Where
X(k)-frequency domain samples
X(n)-time domain samples
N-FFT size
k-0,1,2…….,N-1
FFT-
( )
∑ ( )
3.11
Where
X(n)-time domain samples
X(k)-frequency domain samples
N-FFT size
k-0,1,2……..,N-1
ADDITION OF CYCLIC PREFIX
When a cyclic prefix with length = Ncp is added to OFDM
symbol. Channel output ( r ) is given as-
3.12
Where h=channel impulse response
And in frequency domain
3.13
IV. SIMULATION RESULTS
The proposed methodology for UWAC channel is
simulated in the previous section and the results of the
analyzed system are shown in this section. The results are
calculated as Bit Error Rate (BER) vs. Signal to Noise Ratio
(SNR) and Frame Error Rate (FER) vs. Signal to Noise Ratio
(SNR) for various combinations of data.
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BER
The First result (see Fig. 2) graph shows Bit Error
Rate vs. Signal to Noise Ratio graph for 64 bit FFT size of
proposed system. We applied MIMO-OFDM system and
then modulated with BPSK and QAM techniques.
Figure 2- BER vs. SNR Graph of SWC with MIMO-
OFDM System Using 64-Bit FFT Size
From the above results it can be concluded that the
shallow water communication better works with the
combination of MIMO-OFDM technology, the BPSK
modulation, LDPC coding as compared to QAM counterpart.
The second result (see Fig. 3) graph shows Bit Error
Rate vs. Signal to Noise Ratio graph for 256 bit FFT size of
proposed system. We applied MIMO-OFDM system and
then modulated with BPSK and QAM techniques.
Figure 3- BER vs. SNR graph of SWC with MIMO-
OFDM System using 256-Bit FFT Size
From the above results it can be concluded that the
shallow water communication better works with the
combination of MIMO-OFDM technology, the BPSK
modulation, LDPC coding as compared to QAM counterpart.
The third result (see Fig. 4) graph shows Bit Error
Rate vs. Signal to Noise Ratio graph for 512 bit FFT size of
proposed system. We applied MIMO-OFDM system and
then modulated with BPSK and QAM techniques.
Figure 4- BER vs. SNR graph of SWC with MIMO-
OFDM System using 512-Bit FFT Size
From the results it can be noticed that the shallow
water communication system is better work with the MIMO-
OFDM technology and the BPSK modulation and using
LDPC coding technique than QAM counterpart.
The fourth result (see Fig. 5) graph shows Bit Error
Rate vs. Signal to Noise Ratio graph for 1024 bit FFT size of
proposed system. We applied MIMO-OFDM system and
then modulated with BPSK and QAM techniques.
Figure 5- BER vs. SNR graph of SWC with MIMO-
OFDM System using 1024-Bit FFT Size
From the above results it can be concluded that the
shallow water communication better works with the
combination of MIMO-OFDM technology, the BPSK
modulation, LDPC coding as compared to QAM counterpart.
0 5 10 15 20 25 30 35 4010
-5
10-4
10-3
10-2
10-1
100
X: 27
Y: 0.156
Eb/No (dB)
Bit E
rror
Rate
Underwater Acoustic System with MIMO-OFDM and 64 FFT Size
BPSK Modulation
4-QAM Modulation
8-QAM Modulation
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FER
The first result (see Fig. 6) graph shows Frame
Error Rate vs. Signal to Noise Ratio graph for 256 bit FFT
size of proposed system. We applied MIMO-OFDM system
and then modulated with BPSK and QAM techniques.
Figure 6-FER vs. SNR graph of SWC with MIMO-
OFDM System using 256-Bit FFT Size
From the above results it can be concluded that the
shallow water communication better works with the
combination of MIMO-OFDM technology, the BPSK
modulation, LDPC coding as compared to QAM counterpart.
The second result (see Fig. 7) graph shows Frame
Error Rate vs. Signal to Noise Ratio graph for 512 bit FFT
size of proposed system. We applied MIMO-OFDM system
and then modulated with BPSK and QAM techniques.
Figure 7- FER vs. SNR graph of SWC with MIMO-
OFDM System using 512-Bit FFT Size
From the above results it can be concluded that the
shallow water communication better works with the
combination of MIMO-OFDM technology, the BPSK
modulation, LDPC coding as compared to QAM counterpart.
The third result (see Fig. 8) graph shows Frame
Error Rate vs. Signal to Noise Ratio graph for 1024 bit FFT
size of proposed system. We applied MIMO-OFDM system
and then modulated with BPSK and QAM techniques.
Figure 8-FER vs. SNR graph of SWC with MIMO-
OFDM System using 1024-Bit FFT Size
From the above results it can be concluded that the
shallow water communication better works with the
combination of MIMO-OFDM technology, the BPSK
modulation, LDPC coding as compared to QAM counterpart.
The fourth result (see Fig. 9) graph shows Frame
Error Rate vs. Signal to Noise Ratio graph for 2048 bit FFT
size of proposed system. We applied MIMO-OFDM system
and then modulated with BPSK and QAM techniques.
.
Figure 9-FER vs. SNR graph of SWC with MIMO-
OFDM System using 2048-Bit FFT Size
From the above results it can be concluded that the
shallow water communication better works with the
combination of MIMO-OFDM technology, the BPSK
modulation, LDPC coding as compared to QAM counterpart.
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V. CONCLUSION AND FUTURE SCOPE
Under Water Acoustic Communication is the
challenging task for researchers. Previous section shows the
simulation results of the proposed Under Water Acoustic
Communication model. From the simulation results we
conclude that the system with the BPSK modulation gives
better results as compare to 4 QAM and 8 QAM modulation
with respect to Bit Error Rate and Frame Error Rate even
when FFT size is varied. And when data efficiency is
considered, QAM is better than BPSK. The simulation
results are also compared with previous research work which
verifies low BER and FER of proposed methodology.
In future there are different ways to enhance this
work. Firstly, results can be further enhanced by improving
the coding technique. Advanced coding techniques will
provide good result as compare to LDPC coding technique
and others. Secondly, implementing the proposed
phenomenon on VLSI-FPGA design. Further it can be
demonstrated on physical phenomenon in under water, under
sea, under lake and can realize the actual SNR, BER, FER
and also ISI and ICI. Thirdly, we can also expand this topic
with the help of different modulation techniques in the
frequency domain.
.
Table 1- Shows the result Comparison of proposed method [FER] with previous research
Table 2- Shows the result Comparison of proposed method [BER] with previous research
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