International Journal of Engineering Trends and Technology (IJETT) – Volume 19 Number 1 – Jan 2015
Performance Evaluation of LDPC Based MIMOOFDM System in Shallow Water Communication
Using BPSK
1
Shailee Soni1, Prof. Kirtivardhan Jha2, Prof. Soni Changlani3
M-Tech Research Scholar, 2Research Guide, 3HOD, Department of Electronics & Communication Engineering
Lakshmi Narain College of Technology & Science, Bhopal (M.P.)
Abstract - Underwater communication is the recent area of
research among researchers to explore the underwater
activities. To achieve this there should be a kind of
communication technique needed which do not need wired
connection because it is very difficult to maintain such. The
wireless communication inside water is quite similar to
outside of the water of this a little effect of rarer vs denser
factor affects the communication. In this paper such kind
of communication system is being analyzed where to
achieve better system performance multiple input and
multiple output (MIMO) is established with OFDM system
using LDPC and BPSK modulation.
communication systems. The article is organized into three
sections that address attenuation and noise, multipath
propagation, and the Doppler Effect.
Keywords-LDPC, Shallow Water Communication, MIMOOFDM and BPSK.
I.
INTRODUCTION
Fig.1.1. Underwater Acoustic Communication [12]
Underwater acoustic channels are generally recognized as
one of the most difficult communication media in use today.
Acoustic propagation is finest supported at low frequency,
and the bandwidth available for communication is extremely
limited. For example, an acoustic system may operate in a
frequency range between 10 and 15 kHz. Even though the
total communication bandwidth is very low (5 kHz) and the
system is wideband, the bandwidth is not negligible with
respect to the center frequency. Sound propagates underwater
at a very low speed of around 1500 m/s, and the propagation
occurs on multiple paths. Delay spreading over tens or even
hundreds of milliseconds results in frequency-selective
signal distortion, as motion creates an severe Doppler effect.
The negative properties of radio channels poor physical link
quality of a mobile terrestrial radio channel and high latency
of a satellite channel are combined in an underwater acoustic
channel. In this research article we take a tutorial overview
of the channel properties, aiming to reveal those aspects of
acoustic propagation that are relevant for the design of
ISSN: 2231-5381
Implications of acoustic propagation extend beyond the
physical layer and the article in the final section by
considering their impact on the design of future underwater
networks.
As electromagnetic waves propagate poorly in sea water,
acoustics provides the most important medium to enable
underwater communications. High-speed communication in
the underwater acoustic channel is challenging due to limited
bandwidth, extended multi-path, refractive properties of the
medium, strict fading, rapid time-variation and large Doppler
shifts.
Communication techniques originally developed for
terrestrial wired and wireless channels need significant
modifications to suit underwater channels. A series of review
papers provide an excellent history of the development of the
field until the end of the last decade. A recent literature
review paper presents an overview of the development in the
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International Journal of Engineering Trends and Technology (IJETT) – Volume 19 Number 1 – Jan 2015
field from the start of the decade. In this paper, we aim to
provide a brief overview of the key developments, theoretical
and applied, in the field in the past two decades.
We also hope to provide an insight into some of the open
problems and challenges facing researchers in this field in
the near future. We do not attempt to provide an exhaustive
survey of all research in the field, but instead concentrate on
ideas and developments that are likely to be the keystone of
future underwater communication networks. This paper is
divided into three main sections – one on historical
perspective, another on underwater communications and a
final one on underwater networking.
II.
In the recent years MIMO has drawn significant attention of
researchers in the field of wireless communication. Multipath
fading is main block in increasing the data rate and reliability
of transfer of information over wireless channel. Channel
coding Techniques which are used to improve reliability is
insufficient to meet the requirements of modern multimedia
communications.
Figure 1.2. Reflection of signals in underwater
communication environment [12]
Data
Input
BPSK / QAM
Modulation
Serial To
Parallel
Conversion
PROPOSED METHODOLOGY
LDPC Coding
OFDM
Modulation
(IFFT)
Adding Cyclic
Prefix
Channel with
Noises
Data
Output
Demodulation
Parallel to
Serial
Conversion
LDPC
Decoding
OFDM
Demodulation
(FFT)
Remove Cyclic
Prefix
Figure 2.1 Block diagram of the proposed shallow water communication system
Wireless communication using multiple-input multipleoutput (MIMO) systems enables increased spectral efficiency
for a given total transmit power. Large capacity is achieved
by introducing additional spatial channels that are exploited
by using space-time coding and the environmental factor
which can affect the capacity of MIMO system. These
factors contain channel complexity, the external interference,
and channel estimation error. It was shown that, if multiple
antennas are used at transmitter and or receiver can improve
data rate and reliability.
modulated signal. Now the signal is being coded with LDPC
and modulated by orthogonal frequency division
multiplexing (OFDM) and before transmission of signal
cyclic prefix are added.
During transmission through channel signal is encountered
with the noises and reached at the receiver. AWGN is
generally a basic noise model to mimic the effect of many
random processes that occur nature. On the receiver the
reverse process of transmitter is taken place and the data will
be taken out.
In Fig. 2.1 the block diagram of the proposed approach is
given. The major blocks are modulation of data using BPSK
and QAM followed by Serial to parallel conversion of
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International Journal of Engineering Trends and Technology (IJETT) – Volume 19 Number 1 – Jan 2015
The above describe block diagram of the proposed
methodology is then implemented on simulation tool and the
its step by step flow is shown in the given Fig. 2.2.
iv.
v.
vi.
vii.
viii.
ix.
x.
xi.
xii.
xiii.
xiv.
xv.
xvi.
xvii.
Modulate data with BPSK and QPSK Modulation
Convert signal from serial to parallel
Code signal with LDPC coding
Perform OFDM Modulation i.e. IFFT
Add cyclic prefix
Transmit channel and add noises
Remove cyclic prefix
Perform OFDM demodulation (FFT)
Decoding with LDPC coding
Convert parallel data to serial
Demodulate data with BPSK/QAM modulation
Calculate Error Rate
Compare and display results
End of Simulation
III.
SIMULATION RESULTS
The proposed underwater acoustic communication is
simulated in the previous section and the results of the
analyzed system are shown in this section. The results are
estimated as Bit Error Rate (BER) vs Signal to Noise Ratio
(SNR) for various combinations of data.BER or probability
of error depends only on the energy contents of the signal.
The first result (see Fig. 3.1) graph shows BER vs SNR
graph for 128 bit FFT size of OFDM system by applying
MIMO-OFDM system and the modulated with BPSK and
QAM techniques. From the results it can be analyzed 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.
10
Underwater Acoustic System with MIMO-OFDM and 128 FFT Size
0
BPSK Modulation
4-QAM Modulation
Bit Error Rate
10
Figure 2.2 Flow chart of the simulation algorithm of proposed
shallow water communication system
The execution of the simulation methodology has been
explained step by step which are as follows:
i. Start simulation
ii. Create simulation environment using variable
initialization
iii. Generate random data for transmission over system
ISSN: 2231-5381
10
10
10
10
-1
-2
-3
-4
-5
0
5
10
15
Eb/No (dB)
20
25
30
Figure 3.1 BER vs SNR curve of shallow water communication
with MIMO-OFDM System using 128-Bit FFT Size
The second result (see Fig. 3.2) graph shows BER vs SNR
graph for 256 bit FFT size of OFDM system by applying
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International Journal of Engineering Trends and Technology (IJETT) – Volume 19 Number 1 – Jan 2015
MIMO-OFDM system and the modulated with BPSK and
QAM methods. As of the results it can be says 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.
10
MIMO-OFDM system and the modulated with BPSK and
QAM techniques. From the results it can be says 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.
Underwater Acoustic System with MIMO-OFDM and 256 FFT Size
0
.
BER=
× 100
BPSK Modulation
4-QAM Modulation
10
Relationship between EsNo and EbNo (SNR)
-1
Bit Error Rate
/
10
=
/
(
) + 10 log ( )
-2
Where k is the number of information per symbol
10
10
-3
Es/No = ratio of symbol energy to noise power spectral
density
-4
And Eb/No = ratio of bit energy to spectral power density
10
-5
0
5
10
15
20
Eb/No (dB)
25
30
35
10
Figure 3.2BER vs SNR curve of shallow water communication with
MIMO-OFDM System using 256-Bit FFT Size
10
BPSK Modulation
4-QAM Modulation
10
Bit Error Rate
10
10
10
10
10
10
10
10
10
Underwater Acoustic System with MIMO-OFDM and 512 FFT Size
0
10
Bit Error Rate
The third result (see Fig. 3.3) graph shows BER vs SNR
graph for 512 bit FFT size of OFDM system by applying
MIMO-OFDM system and the modulated with BPSK and
QAM techniques. From the results it can be says 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.
10
Underwater Acoustic System with MIMO-OFDM and 1024 FFT Size
0
-1
-2
BPSK Modulation
4-QAM Modulation
-3
-4
-5
-6
0
5
-2
IV.
-3
-4
-5
-6
5
10
15
20
Eb/No (dB)
25
30
15
20
Eb/No (dB)
25
30
35
40
Figure 3.4 BER vs SNR curve of shallow water communication
with MIMO-OFDM System using 1024-Bit FFT Size
-1
0
10
35
CONCLUSION AND FUTURE SCOPE
The simulation results of the proposed communication model
is calculated and shown in the previous section. From the
results we can analysis that the system with the BPSK
modulation and having higher FFT size gives better results
than the lower FFT size and QAM modulation. In the
imminent technologies better transmitters and the efficient
modulation technique will help to achieve more better results
than current performance.
Figure 3.3 BER vs SNR curve of shallow water communication
with MIMO-OFDM System using 512-Bit FFT Size
The fourth result (see Fig. 3.4) graph shows BER vs SNR
graph for 1024 bit FFT size of OFDM system by applying
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International Journal of Engineering Trends and Technology (IJETT) – Volume 19 Number 1 – Jan 2015
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