� Abstract—This research is focused on the effect of the most

important environmental conditions (temperature, salinity and wind speed) and distance between transmitter and receiver for the underwater acoustic communication (UWAC) system. These conditions may change the characteristics of the communication channel and need to be taken into account during implementation process. As an example, we chose different regions from Turkey to figure out the most suitable areas for UWAC.

Keywords—Depth, Salinity, Temperature, Underwater acoustic channel, Wind speed

I. INTRODUCTION nderwater wireless communication is a rapidly developing topic which is used in off-shore oil industry, remotely monitoring underwater environment and

communication between divers and submarines. There are many communication methods for underwater data transmission such as acoustically, optically, electromagnetically and electrically [1]-[3]. Because of its remarkable attenuation in sea water, electromagnetic waves may not a good solution and can not propagate further. On the other hand electrical waves do not propagate in sea water efficiently and additionally this method is harmful for sea creatures. Therefore the optimal solutions are acoustical and optical methods. Due to the cost of the cable and the set up procedure, optical method is not always suitable for underwater communication in spite of its high data rates. Even if optical wireless communication is used, not only the beam is scattered between bottom and top of the sea but also data transmission is effected by the turbidity of water. For the low data rate communication, an acoustic system is the best choice.

For this research we built a simulation platform which has different environmental condition inputs to understand their effects on UWAC. Shallow water characteristics and restrictions are used in this paper. As a modulation technique we used quadrature phase shift keying (QPSK) modulation in the transmitter. For the channel estimation, least mean square and decision feedback equalizer (LMS-DFE) are combined at the receiver side.

Manuscript received January 29, 2013. We would like to acknowledge the

financial support of the Karadeniz Technical University OSA Student Chapter

Y. Mahmutoglu, A. Yazgan, E. Tugcu and I. Hakki Cavdar are with the Electrical Electronics Engineering Department, Karadeniz Technical University, 61080, Trabzon, Turkey (corresponding authors phone: +904623772978; fax: +904623257405; e-mails: {ymahmutoglu, ayhanyazgan, emintugcu, cavdar}@ktu.edu.tr)

UWAC system can be divided into two parts which are environmental and design. Dealing with the environmental part, path loss is very important. On the other hand underwater medium is very sophisticated due to the different kind of noise sources such as wind noise, thermal noise, rain noise, ship noise, turbulence noise and snapping shrimp noise [4]-[6]. Additionally at the frequencies between 1 to 10 KHz, surface agitation and ship traffic are generally the important sources of ambient noise [7].

In design part, different modulation techniques may be used to increase the data rates such as orthogonal frequency division multiplexing (OFDM), QPSK–CDMA (code division multiple access) [8], [9]. To obtain better and reliable channel profiles, error correction method and LMS-DFE combination was utilized [10], [11]. Besides, in order to improve the receiver performance, authors also classified different receiver combinations [12]. Stochastic simulation of acoustic communication was also studied and a whole UWAC system was established [13], [14]. Some researches tried to design a hybrid system and it could be also possible to communicate from land to underwater environment [15].

In underwater environment detection and positioning can be done by passive listening. To realize this, it is necessary to determine the signal characteristics which are propagated by underwater vessels. Specifications of different speeds and number of fan of underwater vessels affect the signal characteristics. By analyzing these characteristics, detection and identification of sea vessels were also examined [16]. A relative positioning system with characterization of UWAC channel was studied in [17]. Before the positioning, the water tank was analyzed and modeled. Then, this important information was used to improve the accuracy. This positioning system was set up for some specific areas [17].

In our study, it is examined how the environmental conditions (salinity of seawater, temperature of seawater and wind speed over seawater) change the UWAC performance of seas of Turkey by modeling a communication channel. QPSK modulation and LMS-DFE are combined in this model. Doppler shift and bottom type of sea are not considered in this study.

The rest of paper is organized as follows. In Sec. II, the channel model and channel geometry are given. Sec. III describes LMS-DFE. Simulation results are presented in Sec. IV, and conclusions are summarized in Sec. V.

II. UNDERWATER ACOUSTIC COMMUNICATION SYSTEM In shallow water environment, considering an UWAC

system model in non-line-of-sight (NLOS) links there are different beams such as refracted-surface-reflected and

The Effects of Sea Environmental Conditions on the Underwater Acoustic Communication Systems

Yigit Mahmutoglu, Ayhan Yazgan, Emin Tugcu, and I. Hakki Cavdar

U

133978-1-4799-0404-4/13/$31.00 ©2013 IEEE TSP 2013

refracted-bottom-reflected. Transmission loss and communication performance in shallow water environment depends on signal frequency, distance between transmitter and receiver, speed of sound and grazing angles [18].

In our model, the data is firstly QPSK modulated and sent to underwater acoustic channel as given in Fig.1. Propagating along the channel, it is reflected and refracted by bottom and top of water. In our communication model the ambient noise is a combination of wind noise, thermal noise, rain noise, ship noise, turbulence noise and snapping shrimp noise. The transmitted signal is attenuated by chemical structure of the sea and spherical spreading. Additive white Gaussian noise (AWGN) is added on the signal because of receiver circuitry. Signal is estimated by LMS-DFE and then demodulated by using a QPSK demodulator.

A. Underwater Acoustic Channel In this section the channel geometry and channel model are

explained. 1) Channel Geometry: In Fig. 2, h is depth of water

column, R is horizontal distance between transmitter and receiver, zv is depth of the transmitter and za is depth of receiver. D is direct path, Y is surface reflected path, T is bottom reflected path, YT is surface to bottom reflected path, and TY is bottom to surface reflected path. In this channel geometry, n is multipath degree, TX and RX are the transmitter and receiver respectively.

2) Channel Model: Generally, in digital communication systems, the two main distortions are intersymbol interference (ISI) caused by time dispersion of the multipath channel and Doppler spread (it is not considered in this paper) due to the movement of the mobile receiver. To achieve high speed reliable communications channel, identification and equalization are necessary to overcome the effects of ISI. The pulse shaping filter, multipath fading channel, receiving filter and the sampling can be represented by a tapped delay-line baud-rate model [19]. For simplicity, the channel may be assumed to be time-invariant within a data block. The corresponding outputs of the equivalent discrete time channel model is given in (1) where, x(k) is transmitted data sequence, hl, 0� l �L, are the coefficients of the discrete the channel model, L is the effective channel memory length, and �(k) is a sequence of additive noise samples.

( ) ( ) ( )0 l

Ly k x k l h k

l�� � � ��

� (1)

Fig. 1. A whole system for UWAC channel.

Fig. 2. Multipath ways and direct way of beam. In UWAC system, if there are four multipath beams in the

channel, the multipath degree is assumed as one. On the other hand selecting eight multipath beams, the multipath degree is two (n=2) [20], [21]. In our study although the multipath degree is two, in the representation of equations, the multipath degree is selected as one just for the simplicity. According to the distance between transmitter and receiver, optimal center frequency fc is calculated in (2) where R is in km and fc is in KHz [22].

2/3200 /f Rc � (2)

Here B (in meters) which is calculated for D, Y, YT, TY and T and given in (3)-(7) respectively represents the vertical part of the total distance. B1 is the vertical distance of the LOS beam.

1 , & ; b h z a h z For D B b av a� � � � � � (3)

(2 ( 1) 4) 2 ; 2 ; 2 nFor Y B nh a b B h a b� � � � � � � � � (4)

(3 ( 1) 4) 3; 2 ; 2 nFor YT B nh a b B h a b� � � � � � � � � (5)

(4 ( 1) 4) 4 ; 2 ; 2 nFor TY B nh a b B h a b� � � � � � � � � (6)

� (5 ( 1) 4) 5 ; 2 1 ; nFor T B n h a b B a b� � � � � � � � � (7)

In (8), Up is the total distance between transmitter and

receiver in meters. U1 is the direct distance between transmitter and receiver. U2, U3, U4 and U5 are the distances reflected, just by surface, firstly by surface and then by bottom, firstly by bottom and then by surface and just by bottom respectively as given in (9). �p and A(Up,f) are the combined reflection coefficient and propagation losses along the pth propagation way respectively. Rbottom [23] and Rsurface [24] are bottom and surface reflection coefficients respectively. kb and ks are the number of reflections from bottom and top respectively. In this study spherical spreading (k=2) is used. In this model � is the attenuation coefficient, �p is delay time, p is the number of rays and pmax is highest number of p [25].

; 1, 2, 3, 4, 52 2 pp pU R B �� �

(8)

1 1

1 1

; ;1 2 3

; 54

U U U U U UD Y YT

U U U UTY T

� � �

� � (9)

� � � � /10, 10 paUkp pA U f U� (10)

z a

h

R

�

� a

b

z v

Bottom

TXRX

Surface

TY

YT Y

T

D

134

TABLE I THE RELATIONSHIP BETWEEN DISTANCE AND BANDWIDTH FOR UWAC

CHANNEL IN SHALLOW WATER

R, Distance[Km] f, Bandwidth [KHz] Very long R�20 f�10 Long 5�R�20 5�f�10 Medium 1�R�5 f�20 Short 0.1�R�1 20�f�50 Very short R�0.1 100� f

� � kskbbottom surfacep R R� � (11)

�

( ) 1/2( , )

ph fp

A U fp

�� (12)

1max( 2 )

1

pj f p

pp

h eH � ��

�

�� � (13)

� �

� �

� �

� �

Y11 1/2

2

YT12 1/2

3

TY13 1/2

4

T14 1/2

5

,

,

,

,

hA U f

hA U f

hA U f

hA U f

��

��

��

��

(14)

( 2 ) ( 2 ) ( 2 ) ( 2 )1 2 3 4

1 2 3 4

j f j f j f j fH h e h e h e h ec c c c� � � � � � � �� � � �� � � � (15)

The main channel equation is given in (10-15) [21]. In this

equations h1, h2, h3 and h4 comprise each way of the model. �Y1, �YT1, �TY1 and �T1 are the combined reflection coefficients which are composed of bottom and surface.

/ 2 1R h n�� � (16)

The distance-bandwidth relationship for UWAC shallow

water channel is determined and given in Table I [26]. Additionally the restriction of shallow water channel model which is related to the R is also given in (16) [20]. Here n is the multipath degree, R is horizontal distance between transmitter and receiver and h is depth of water column.

III. CHANNEL ESTIMATION WITH LMS-DFE

A. Least mean square adaptive algorithm LMS algorithm which is given in (17-20) is used to estimate the channel profile. For this robust algorithm, xk is the N-tap input vector, N is the filter order, ck is the vector of adaptive tap weights , dk is desired signal, dk

�is the estimation

of the desired signal, ek is the error signal, μ is the step-size

TABLE II NUMBER OF TAPS, INPUT VALUES AND OUTPUT OF DFE

Feedforward Filter Feedback Filter Tap � , , 01c j Kj � � � , ( 1, )2c j Kj � �

Input � , , 01x j Kk j � � �� , ( 1, )2d j Kk j � ���

Output

Tap update

0 2

11

K

j k j j k jj K j

c x c ddk � ��� �

�� � � ��

*1c c e vk k k k�� ��

Fig. 3. DFE general structure. parameter and (.)H represents the conjugate (Hermitian) transpose [27], [28].

[ , ........... ]1 ( 1)T

x x x xk k k k N� � � � (17)

H

d c xk k k��

(18)

e d dk k k l� � ��

(19)

* ,1c c e xk k k k�� �� (20)

B. Decision-feedback equalizer DFE is composed of a transversal feed forward filter with

K1 taps and a feedback filter with K2 taps [28].

1 21( ,..., , ..., )k k K k k k Kv x x d d� � ��

� � (21)

In Table II channel output is given in (21). A block

diagram of the DFE is shown in Fig. 3. Input and output values of DFE are given in Table II. Two tap channel is used in this study.

IV. RESULTS Simulation parameters which were measured before in

different regions of Turkey are given in Table III. In Fig. 4 it is clear that increase in salinity of sea water,

decreases the bit error rate (BER) for a constant signal to noise ratio (SNR) and increases the UWAC system performance. In Fig. 5 it is obvious that increase in temperature of sea water, decreases UWAC system performance. In Fig. 6 it is shown that increase in wind speed which blows above sea water, decreases UWAC system performance. In Fig. 7, h is selected as 20m so for satisfying shallow water condition (16), R is selected as 250m, 750m

135

TABLE III SIMULATION PARAMETERS

Parameters Values Channel number 2000 Max. bit rate 2000bps Modulation QPSK Horizontal distance between transmitter and receiver (R)

250m–2500m

Salinity (S) ‰20–‰45 Wind speed (wr) 5m/s–50m/s Temperature (T) 0°C–30°C Bandwidth (f) 20KHz-40KHz Depth of receiver (za) 1m-40m Depth of transmitter (zv) 2m-50m Depth of the sea column (h) 4-100m ph value of sea water 7 Sea water density 1000g/m3 Water density at the bottom of sea 1800g/m3 Sound speed at the bottom of sea 1300m/s Sound speed in sea 1500m/s

0 2 4 6 8 10 12 14 16

10-4

10-3

10-2

10-1

BER

SNR [dB]

S=‰20S=‰30S=‰45

Fig. 4. BER-SNR graph for salinity values of ‰20, ‰30 and ‰45.

0 2 4 6 8 10 12 14 16

10-4

10-3

10-2

10-1

BER

SNR [dB]

T=0°T=15°T=30°

Fig. 5. BER-SNR graph for temperature values of 0°C, 15°C and 30°C.

6 7 8 9 10 11 12 13 14 15 16

10-4

10-3

10-2

10-1

BER

SNR [dB]

wr=5m/swr=25m/swr=50m/s

Fig. 6. BER-SNR graph for wind speed values of 5m/s, 25 m/s, 50m/s.

TABLE IV SALINITY AND TEMPERATURES VALUES OF SEA OF TURKEY

Sea Salinity Concentration(‰)

Annual Mean Temperature(°C)

Blacksea 18 10.1-15 Marmara Sea 22 12.6-15 Aegean Sea 38 15.1-17.5 Mediterranean Sea 43 17.6-20

0 2 4 6 8 10 12 14 16

10-4

10-3

10-2

10-1

BER

SNR [dB]

R=250mR=750mR=1000m

Fig. 7. BER-SNR graph for horizontal distance between transmitter and receiver of 250m, 750m and 1000m (in case of h=20m).

0 2 4 6 8 10 12 14

10-4

10-3

10-2

10-1

BER

SNR [dB]

R=1500mR=2000mR=2500m

Fig. 8. BER-SNR graph in the case of high value of water column depth and low value of signal frequency (in case of h=100m ). and 1000m respectively. In order to satisfy the values given in Table I, f is chosen as 40KHz. Rest of the parameters are same as in Table III, and it can be seen that increase in distance between transmitter and receiver, decreases UWAC system performance. In Fig. 8 za, zv and h are taken as respectively 40m, 50m and 100m. R is selected respectively as 1500m, 2000m, 2500m and the bandwidth (f) is chosen 20 KHz for satisfying shallow water conditions (using Table I and (16)). Rests of the parameters are same as in Table III. The main difference between Fig. 7 and Fig. 8 is depth of sea column. Because of high h value, for satisfying (16), the R also needs to be increased. Similarly the frequency also needs to be changed.

Since the value of wind speeds are very low rate like 3m/s [29] in Turkey, it does not affect the channel efficiency highly (except the case of hurricane).

Regarding to the Table IV Blacksea has the least values and Mediterranean Sea has the highest values comparing to the others [30], [31].

136

V. CONCLUSION In order to obtain the effect of the environmental

conditions on the UWAC, we designed a simulation program. To figure out the UWAC suitability of a specific region, the output of the designed simulation program may be used. It is clear that increasing temperature and wind speed over the sea causes a decrease in the UWAC system performance. Conversely, increasing the salinity of water gives rise to improve the UWAC system performance. In order to reach low BER values, while the wind speed and the temperature of the sea water should be low, the salinity should be high. According to temperature and salinity values which are given in Table IV, the most suitable seas are Blacksea and Mediterranean Sea for UWAC applications. Our next study will be a real experiment of UWAC in seas of Turkey by using underwater acoustic modems.

REFERENCES [1] N. Farr, A. Bowen, J. Ware and C. Pontbriand, “An integrated,

underwater optical /acoustic communications system,” Oceans 2010 IEEE, pp. 1-6, May 2010.

[2] A. Al-shamma, A. Shaw and S. Saman, “Propagation of Electromagnetic Waves at MHz Frequencies through Seawater,” IEEE Transactions on Antennas and Propagation, vol. 52, no. 11, pp. 2843-2849, 2004.

[3] Z. Wu, J. Xu and B. Li, “A High-speed Digital Underwater Communication Solution Using Electric Current Method,” Future Computer and Communication (ICFCC), 2010 2nd International Conference on, vol. 2, pp. 14-16, May 2010.

[4] D. E. Chaitanya, Ch. V. Sridevi and S. B. Rao, “Path loss analysis of Underwater communication Systems,” Proceeding of the 2011 IEEE Students' Technology Symposium (TechSym), pp. 65-70, January 2011.

[5] R. Govilkar, A Simulator for Microcontroller-based Underwater Ultrasonic Communications, Master thesis, North Carolina USA, 2006.

[6] A. Stefanov and M. Stojanovic, “Design and Performance Analysis of Underwater Acoustic Networks,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 10, pp. 2012-2021, December 2011.

[7] F. Traverso, G. Vernazza and A. Trucco, “Simulation of non-White and non-Gaussian Underwater Ambient Noise,” Oceans 2012-Yeosu IEEE, pp. 1-10, May 2012.

[8] J. Han, S. Kim, K. Kim, S. Chun and K. Son, “Design of OFDM System for High Speed Underwater Communication,” 2009 International Conference on Computational Science and Engineering.CSE’09. , vol. 2, pp. 988-992, August 2009.

[9] S. Guo and Z. Zhao, “Design of a QPSK-CDMA Acoustic Communication System for Multiple Underwater Vehicles,” Proceedings of the 2009 IEEE International Conference on Mechatronics and Automation .ICMA 2009. , pp. 2568-3572, August 2009.

[10] Y. Labrador, M. Karimi, D. Pan and J. Miller, “Modulation and Error Correction in the Underwater Acoustic Communication Channel,” IJCSNS International Journal of Computer Science and Network Security,” vol. 9, no .7, pp. 123-129, July 2009.

[11] Y. Yoon and A. Zielinski, “Simulation of the Equalizer for Shallow Water Acoustic Communication,” Oceans '95. MTS/IEEE. Challenges of Our Changing Global Environment, vol. 2, pp. 1197 – 1203, Victoria, Canada, October 1995.

[12] F. Blackmon, E. Sözer, M. Murandian, J. Proakis and M. Salehi, “Performance Comparison of Iterative/Integral Equalizer/Decoder Structures for Underwater,” Oceans 2001. MTS/IEEE Conference and Exhibition, vol. 4, pp. 2191-2200, 2001.

[13] C. Bjerrum-Niese and R. Lützen, “Stochastic Simulation of Acoustic Communication in Turbulent Shallow Water”, IEEE Journal of Oceanic Engineering, vol. 25, no. 4, pp. 523-532, October 2000.

[14] M. Stojanovic, "Underwater Acoustic Communications,'' in Encyclopedia of Electrical and Electronics Engineering, John G. Webster, Ed., John Wiley & Sons, 1999.

[15] H. Yoshida, T. Hyakudome, S. Ishibashi, H. Ochi, K. Asakawa, T. Kasaya, T. Saito and S. Okamoto, “Study on Land-to-Underwater communication, Toward Underwater Mineral Resorces Exploration System Development,” 14th International Symposium on Wireless Personal Multimedia Communication (WPMC), pp. 1-5, October 2011.

[16] S. Özen, M. A. Çavu�lu, Y. H. Ba�aran, M. Öner and Ö. Tatar, “Simulation of Naval Platforms’ Acoustic Signals,” 20th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, April 2012.

[17] K. Chen, E. Cheng, F. Yuan,W. Su and D. Wang, “Characterization of Underwater Acoustic Channel for a Relative Positioning System,” Oceans 2012-Yeosu IEEE, pp. 1-4, May 2012.

[18] L. E. Emokpae and M. Younis, “Throughput Analysis for Shallow Water Communication Utilizing Directional Antennas,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 5, pp. 1006-1018, June 2012.

[19] J. G. Proakis, Digital Communications Fourth Edition, McGraw- Hill Book Company, New York, 2001.

[20] A. Zielinski, R. Coates, L. Wang and A. Saleh, “High Rate Shallow Water Acoustic Communication,” Oceans’ 93. Enginerring in Harmony with Ocean. Proceedings, vol. 3, pp. 432-437, October 1993.

[21] M. Stojanovic and J. Preisig, “Underwater Acoustic Communication Channels: Propagation Models and Statistical Characterization,” IEEE Communications Magazine, vol. 47, no. 1, pp. 84-89, January 2009.

[22] C. He, J. Huang and Q. Zhang, “Research on Bandwidth Efficient Underwater Acoustic Communication,” IEEE Region 10 Conference TENCON 2010, pp. 742-745, November 2010.

[23] M. Anjum, “Underwater Acoustic Channel Modeling, Simulation, and Estimation,” Proceedings of International Bhurban Conference on Applied Sciences & Technology,” pp. 10-13, January 2011

[24] A. Jones, J. Sendt, A. Duncan, P. Clarke and A. Maggi,” Modelling the acoustic reflection loss at the rough ocean surface,” Proceedings of acoustics, pp. 1-8, November 2009.

[25] L. Kinsler, A. Frey, A. Coppens and J. Sanders, “Fundamentals of Acoustics,” Fourth Edition, John Wiley & Sons, United Kingdom, 2010.

[26] N. Nasr�, A. Kachouri, L. Andrieux and M. Samet, “Design Considerations For Wireless Underwater Communication Transceiver,” 2nd International Conference on Signals, Circuits and Systems. SCS 2008, pp. 1-5, November 2008.

[27] B. Widrow, “Adaptive filters, I: Fundamentals,” Stanford Electron Lab., Stanford University, California, Technical Report No. 6764-6, December 1996.

[28] A. Batra, J. R. Zeidler and A. A. Beex, “Mitigation of Narrowband Interference Using Adaptive Equalizers,” In Proceedings of the European Signal Processing Conference (EUSIPCO), SPAWAR Systems Center, San Diego, and the UCSD Center, 2006.

[29] C. �lk�l�ç, “Türkiye’de Rüzgar Enerjisi Potansiyeli ve Kullan�m�,” Mühendis ve Makina dergisi, vol. 50, pp. 26-32, June 2006.

[30] M. Can, A. B. Etemolu ve A. Avc�, “Deniz Suyundan Tatl� Su Eldesinin Teknik ve Ekonomik Analizi,” Uluda� Üniversitesi Mühendislik-Mimarl�k Fakültesi Dergisi,” vol. 7, no. 1, pp. 147-160, 2002.

[31] M. Demircan, “2011 Y�l� S�cakl�klar�n�n Analizi,” Klimatoloji �ube Müdürlü�ü.” Available: http://www.mgm.gov.tr/FILES/ziraat/2011yili SicakliklarininAnalizi.pdf, 2011.

137