Impact of Environmental Conditions on Underwater

Communications

Rahul GoswamiAdvisor: Dr. Ali AbdiHelen and John C. Hartmann Department of Electrical & Computer EngineeringJuly 30, 2015

Abstract Acoustic propagation can be characterized by attenuation that

increases with signal frequency, time-varying multipath propagation and low speed of sound. Underwater acoustic channels are considered to be one of the most difficult communication media in use today. Sound propagates through water at a speed of 1500 m/s and propagates along several paths. The effect of such multipath propagation is interference at the receiver end which hampers reception of the correct information. There also exists ambient and site-specific noise in underwater fading channels. The aim of this study is to focus on the analysis of the Bit Error Rate (BER) with varying Signal-to-noise ratio (SNR) for Frequency Shift Keying (FSK) modulated signals over various conditions in underwater acoustic channels. With a primary objective of reducing the BER, different sediments are introduced to the underwater acoustic channel.

ObjectivesThe project deals with the following factors:  

Frequency Shift Keying (FSK) modulation of randomly generated signals.

Non-coherent detection of the transmitted signals at the receiver end.

Underwater acoustic channels Analysis of multipath fading, absorption,

scattering by the water-bed sediments Comparing different water-bed conditions for

better detection of signals

Underwater Acoustic ChannelSpeed of sound underwater

Absorption

Attenuation

Noise

Multipath

Doppler effect

Sound attenuation in sediment

Speed of Sound Underwater

Sound speed as a function of depth and ocean cross-section

Speed of sound underwater(1500 m/s)> Speed of sound in air (340 m/s)

Absorption & AttenuationPath loss = Absorption Loss + Spreading Loss

The overall path loss is given by:

Doppler Effect

Sediment type K (spreading factor)

Very fine silt 0.17

Fine sand 0.45

Medium sand 0.48

Coarse sand 0.53

Sound attenuation in sediments

Experimental Process

Transmitter/Receiver Working Principle

BFSK Modulation

Non coherent Demodulation

Simulation Results

Perfect detection at high SNR (30 dB)

Incorrect detection at low SNR (-5 dB)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-0.5

00.5

11.5

ampl

itude

(vol

t)

time(sec)

transmitting information as digital signal

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-20

0

20

time(sec)

ampl

itude

(vol

t)

waveform for binary FSK modulation coresponding binary information

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-50

0

50

time(sec)

ampl

itude

(vol

t) waveform after passing through channel

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-0.5

00.5

11.5

ampl

itude

(vol

t)

time(sec)

recived information as digital signal after binary FSK demodulation

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-0.5

00.5

11.5

ampl

itude

(vol

t)

time(sec)

transmitting information as digital signal

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-20

0

20

time(sec)

ampl

itude

(vol

t)

waveform for binary FSK modulation coresponding binary information

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-50

0

50

time(sec)

ampl

itude

(vol

t)

waveform after passing through channel

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-0.5

00.5

11.5

ampl

itude

(vol

t)

time(sec)

recived information as digital signal after binary FSK demodulation

Graphical Analysis

-10 -5 0 5 10 15 20 25 3010

-3

10-2

10-1

100

Theoretical BER vs SNR Curve for BFSK over Rayleigh Fading channel

Eb/No (dB)

BE

R

-10 -5 0 5 10 15 20 25 3010

-300

10-200

10-100

100

BER vs SNR Curve for BFSK over AWGN channel

BE

R

• BERAWGN < BERRayleigh

• BER decreases for high SNR

Experimental Observations

PLAIN WATER

SNR(i

n dB)

BER

-

0.35070.5028

0.1921 0.4868

0.208 0.4965

0.5053 0.2413

0.5523 0.0168

0.5655 0.0152

1.1123 0.0029

1.3421 0.0004

1.8143 0.00041

2.5 0.0003

PLAIN WATER

SNR(in

dB)BER

-0.3507 0.5028

0.1921 0.4868

0.208 0.4965

0.5053 0.2413

0.5523 0.0168

0.5655 0.0152

1.1123 0.0029

1.3421 0.0004

1.8143 0.00041

2.5 0.0003

ROCK-BED

SNR(in

dB)BER

-0.2829 0.5018

0.0262 0.4945

0.2081 0.4948

0.21 0.4528

0.2143 0.2253

0.2176 0.1698

0.221 0.0665

0.4186 0.0142

0.4295 0.0028

0.5134 0.0017

0.5567 0.0009

0.6601 0

0.6616 0.000166

ROCK-BED

SNR(in

dB)BER

0.7528 0.0003

0.7615 0.0003

0.762 0.000166

0.7827 0.000166

0.7916 0.000166

0.7926 0.0003

0.8078 0.000166

0.8134 0.000166

0.821 0.000166

0.9127 0.000166

0.9213 0

0.9304 0.000166

0.9921 0.000166

SAND-BED

SNR(in

dB)BER

-0.1458 0.4858

-0.0141 0.488

0.0315 0.3213

0.0642 0.2143

0.0932 0.2202

0.1816 0.0664

0.2597 0.0247

0.1121 0.1013

0.3023 0.0011

0.3142 0.0009

SAND-BED

SNR(in

dB)BER

0.3219 0.0003

0.3803 0.0043

0.4119 0.0115

0.4213 0.000166

0.7135 0.000166

1.2137 0.000166

1.3121 0

1.5213 0

2.4316 0

-1 0 1 2 3 4 5 6 710

-4

10-3

10-2

10-1

100

SNR(dB)

Bit

Erro

r Rat

e

BER vs SNR plot for underwater communication scenarios

Rock-bedPlain waterSand-bed

-1 0 1 2 3 4 5 6 710

-4

10-3

10-2

10-1

100

SNR(dB)

Bit Erro

r Rate

BER vs SNR plot for underwater communication scenarios

Rock-bedPlain waterSand-bed

Data Transmission Rate: 100 bits per second

Number of bits per transmission: 6000

Frequency used for FSK modulation: 6.9 kHz (for 0) and 7 kHz(for 1)

Sampling rate: 96000 samples per second

Experimental Analysis

Conclusion

-1 0 1 2 3 4 5 6 710

-4

10-3

10-2

10-1

100

SNR(dB)

Bit

Erro

r Rat

e

BER vs SNR plot for underwater communication scenarios

Rock-bedPlain waterSand-bed

-1 0 1 2 3 4 5 6 710

-4

10-3

10-2

10-1

100

SNR(dB)

Bit Erro

r Rate

BER vs SNR plot for underwater communication scenarios

Rock-bedPlain waterSand-bed

Data Transmission Rate: 100 bits per second

Number of bits per transmission: 6000

Frequency used for FSK modulation: 6.9 kHz (for 0) and 7 kHz(for 1)

Sampling rate: 96000 samples per second

BERwater>BERrocks>BERsand

Absorptionsand >Absorptionrocks

Scatteringrocks>Scatteringsand

1] Milica Stojanovic (Northeaster University) & James Preisig (Woods Hole Oceanographic Institution): Underwater Acoustic CommunicationChannels: Propagation Models and Statistical Characterization, January 2009 [2] F. De Rango, F. Veltri, P. Fazio, D.E.I.S. Department, University of Calabria, Italy, 87036 : A Multipath fading Channel model for Underwater Shallow Acoustic Communications [3] S. Anandalatchoumy & G. Sivaradje, Department of Electronics & Communication Engineering, Pondicherry Engineering College, Pondicherry, India : Comprehensive Study of Acoustic channel models for Underwater wireless communication networks, International journal on Cybernetics & Informatics (IJCI), Vol 4 , No 2, April 2015 [4] K. Saraswathi, Netravathi K. A., Dr. S. Ravishankar, Asst. Prof., RV College of Engineering, Bangalore : A Study on channel modeling of underwater acoustic communication, International Journal of Research in Computer andCommunication Technology, Vol 3, Issue 1, January- 2014 [5] Emerson de Sousa Costa, Eduardo Bauzer Medeiros & Joao Batista Carvalho Filardi : Underwater Acoustics modeling in finite depth shallow waters (Chp 22 of Modeling and Measurement Methods for Acoustic Waves and for Acoustic Microdevices)  [6] Dr. Aoife Moloney, School of Electronics & Communications, Dublin Institute of Technology: Non Coherent Detection (Lecture 26), April 2005 [7] Yoo Jung Kim : The Underwater Propagation of sound and its applications, Dartmouth Undergraduate Journal of Science, March 11, 2012

References