UWA Communication using MIMO OFDM · transmission rate then it's called MIMO-OFDM. MIMO-OFDM is the efficient solution for transmitting and receiving the data over the long distance

www.ijemr.net ISSN (ONLINE): 2250-0758, ISSN (PRINT): 2394-6962

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



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



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.



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




Figure 3- BER vs. SNR graph of SWC with MIMO-

OFDM System using 256-Bit FFT Size





The third result (see Fig. 4) graph shows Bit Error






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










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



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-






The second result (see Fig. 7) graph shows Frame




Figure 7- FER vs. SNR graph of SWC with MIMO-






The third result (see Fig. 8) graph shows Frame










The fourth result (see Fig. 9) graph shows Frame




.









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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Documents

UWA Communication using MIMO OFDM · transmission rate then it's called MIMO-OFDM. MIMO-OFDM is the efficient solution for transmitting and receiving the data over the long distance