SETIT 2009 5th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications March 22-26, 2009 – TUNISIA
Survey Of Temperature Variation Effect On Underwater Acoustic Wireless Transmission Ghada ZAÏBI*, Nejah NASRI*, Abdennaceur KACHOURI* and Mounir SAMET *
* Laboratory of Electronics and Technologies of Information (LETI) National School of Engineers of Sfax B.P.W, 3038 Sfax, Tunisia
[email protected] [email protected] [email protected] [email protected]
Abstract: In acoustic channel it is known that low available bandwidth, highly varying multipath, large propagation delays, noise and physical channel properties variation, in addition to the power consumption restrict the efficiency of underwater wireless acoustic systems. The physical characteristics of Mediterranean channel such as depth, salinity and especially temperature influence the acoustic signal attenuation, the SNR level, error ratio and the signal bandwidth. It seems to be crucial to introduce these parameters in the underwater channel model of the underwater wireless communication network. The goal of this paper is to highlight the impact of the temperature variation at shallow sea on the acoustic signal attenuation and error ratio and explain the relationship between temperature and SNR as well as the optimal frequency of the transmitted signal. Key words: optimal frequency, SNR, temperature, underwater acoustic channel.
as temperature, salinity and depth variation [SOZ 00], [WON 05], [COP 82]. INTRODUCTION This article characterizes the underwater channel In spite of the use of electromagnetic wave, especially and presents its physical characteristics survey as well radio wave in the mobile radio communications, its as its parameters variation effect on the acoustic signal propagation in underwater environment is limited in attenuation. We establish the relationship between few meters. SNR and temperature values and finally we evaluate They are rapidly attenuated and require an important temperature changes effect on image quality. transmission power as well as large antennae. On the other hand, acoustic communications showed the best performances compared to the electromagnetic waves. 1. Description of underwater channel Thus, underwater wireless communications are The underwater channel is an intermittent channel. established by acoustic wave but remain far from an It is characterized by its limited bandwidth, long ideal communication. propagation delays and frequency selectivity The transmission of a reliable underwater acoustic This Disturbance and characteristics of the signal with the least distortions and the minimum underwater channel are represented by three blocks. emission power is attracting increasing interest from researchers considering the unfavourable conditions of In fact, the noise of the underwater is modelled by the the shallow underwater environment, as well as the AWGN channel. The Rayleigh channel represents the problems encountered when providing the system with multipath delay, fading and Doppler frequency shift. energy [AKY 05]. Moreover, adding an attenuation block is necessary to Several alternatives try to improve signal’s quality model transmission losses. Variables f, d, p are (signal to noise ratio, inter-symbols interferences respectively frequency, distance and physical (ISI)) and reduce power consumption of the characteristics (figure 1). underwater channel by modelling a suitable underwater transmission system neglecting the physical specifications of the underwater channel such
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Table 1. Average values of temperature (winter- summer) and salinity of the Mediterranean
Figure 1. Underwater channel model.
1.1. Physical characteristics of the underwater channel In underwater acoustic communication, the well known of the underwater channel will improve the modelling of the underwater acoustic system. The characteristics of the ocean or sea water such as salinity, temperature, density, and current velocity do not vary smoothly with depth, but in a discontinuous fashion. They remain almost constant within certain 1.1.2. Attenuation of the acoustic wave in marine layers and change rapidly in passing from one layer to environment another. The thickness of these layers varies from tens The sound propagation in sea water leads to of centimetres to tens of meters. acoustic signal attenuation. This attenuation is due to In fact, this discontinuity of water layers has a two effects: geometrical considerations like diffusion deep effect on the transmission of an acoustic signal and non geometrical effect like absorption. by increasing or decreasing the attenuation value. The signal attenuation is given by the following 1.1.1. Mediterranean sea formula (1): The Mediterranean Sea is the crossroad of the x(f ) three continents: Europe, Asia and Africa. It is a deep A(x) x k10 10 sea. Its average depth is about 1500 meters and its (1) maximum depth is 5121 meters (narrow trench in the south of the Cape Matapan (Greece)). Mediterranean sea-beds form a chaotic zone. They are Where k represents the spreading factor (k = 1 for characterized by variable depths (siculo-Tunisian cylindrical spreading, k = 1.5 is the practical value and threshold (400m); Gibraltar (less than 100m); the k = 2 for spherical spreading) and β(f) is the Turkish straits threshold (less than 50m)) [SIM 02]. absorption coefficient in dB/Km (for x is the distance between 2 nodes on Km) . The temperature and the salinity of Mediterranean water (from Western to Eastern Mediterranean) The absorption coefficient is chosen for undergo fluctuations which are influenced by the frequencies between 3 KHz and 0.5 MHz, according currents and the depths [ERI 65]. Indeed, water to Marsh and Schulkin empirical formula [STO 06], surface temperature varies between 10 and 30 °C, and as: is stabilized in-depth in an average value of 13 °C. These values are different from surface Atlantic S.A.f .f 2 B.f 2 ( f ) 8.68.103 T 1 6.54.104 P temperatures which reach 4 °C and even less. 2 2 (2) fT f fT The rate of salinity of the Mediterranean varies between 36 ‰ (in the west) and 39 ‰ (in the east) For f in KHz, A = 2.34.10-6, B = 3.38.10-6, S is the [BET 05] and the average rate is close to 37,5 ‰. salinity (‰), P is the hydrostatic pressure [kg/cm²], Its value oscillates around 36 ‰ close to the 6-1520/(T+273) and fT = 21.9.10 is the relaxation frequency Gibraltar Straits where water marries by the currents (kHz) with T is the temperature between 0 and 30 °C. with those of the Atlantic and it is between 38 and It takes into account the variation of salinity, depth 39 ‰ at the Gulf of Gabes (minor Syrte). and temperature which characterise absorption due to The average values of the temperature (winter- the MgSO4 relaxation. It depends also on the shear summer) and salinity measured at various depths (m) viscosity and volume viscosity in the frequency band for several places of the Mediterranean Sea is 100 Hz -100 Khz. illustrated by the following table [ZEN 02]. Both expressions (1) and (2) of signal attenuation and absorption coefficient are used to extract how physical sea water characteristics attenuate the propagated signal.
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2. Sea water characteristics impact on the compensating its effect. Signal attenuation For the other characteristics, their impact is less In this subsection, the focus was on the important than temperature especially for the temperature variation especially at shallow sea and its Mediterranean Sea because it’s not as deep as the impact on the acoustic signal attenuation. Indeed, the Atlantic Ocean, for example. The attenuation research showed the parameters which increase the difference between salinity extremes is about 5.10-2 ‰ temperature influence on signal attenuation when at 200 m depth, 1 Km distance, average temperature propagated in shallow water. and 10 KHz frequency. Table 2(a) shows that for two nodes separated by In the next subsection, the underwater channel 1Km distance and placed at 200 m depth, with an characteristics chosen are 10 KHz as frequency, 200m average salinity, attenuation variation is about 0.46 dB as depth, an average salinity (37.5 ‰) and temperature for 10 KHz signal frequency at the extreme (13 °C). Mediterranean sea temperature values.
Table 2(a). Attenuation variations for different 3. Relationship between the environment temperatures at 1 Km and 10 KHz. temperature and SNR
3.1. Ambient noise The ambient noise is the background noise of the ocean due to four causes: turbulence, shipping activities, waves and thermal noise [STO 06], [LUR 98]. It can be described by Gaussian statistics and a continuous power spectral density (p.s.d). The following formulae present the p.s.d of the four noise sources in dB re µ Pa per Hz as a frequency If the distance between the two nodes increases, function in KHz: example 5 Km (table 2(b)), then the temperature influence is a linear function and the attenuation 10 log(Nt (f)) 17 30(log(f)) (3) difference reaches 2.3 dB at the same frequency. 10 log(Ns (f )) 40 20(s 0.5) 26 log(f ) Table 2(b). Attenuation variations for different [60 log(f 0.03)] (4) temperatures at 5 Km and 10 KHz. 10 log(N (f )) 50 7.5w1 2 20 log(f ) w [40 log(f 0.4)] (5)
10 log(Na (f)) 15 20 log(f) (6)
In the second expression, shipping activity factor s varies between 0 and 1. The wind speed is w in m/s. The total spectral density p.s.d of the ambient noise is In addition, if we use high signal frequencies, for the combination of its four components: example 50 KHz (table 2(c)) then the variation of attenuation is about 7.65 dB. N(f) N (f) N (f) N (f) N (f) (7) Table 2(c). Attenuation variations for different t s w a temperature at 1 Km and 50 KHz. 3.2. Temperature variation and the SNR
By taking into account transmission loss, SNR(x, f, T) will be given by:
P SNR(x,f, T)= (8) A(x,f, T)N(f)Δ(f )
So, for high signal frequencies and huge distance, Where ∆f is the receiver noise bandwidth (a temperature variations effect on signal attenuation in narrow band around the frequency f). shallow water, becomes more and more important and The factor 1/A(x, f, T)N(f) is illustrated in figure 2 must be taken into account when modelling a and represent the frequency dependent part of the transceiver by including it in the channel model and SNR. For each transmission distance x, there exist an
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optimal frequency fo for which the maximum SNR is geographic coverage is greater than the unpartitioned obtained. Figure 3 shows the optimal frequency as a link-layer coverage of all nodes [PAR 06]. function of the distance for T=13°C. We notice that optimal frequency varies between 4 and 51 KHz for a 1/A*N en fonction de la fréquence distance variation from 1 to 100 Km. We can set then -60 the transmission bandwidth around the optimal -80 frequency and we adjust the transmission power to T=30°C achieve the desired SNR level. -100 T=4°C T=13°C -120 1/A*N en fonction de la fréquence 0 -140
-20 -160 1/AN(dB) 5Km -40 -180 10Km -60 -200
50Km -80 -220 100Km
1/AN(dB) -100 -240 0 2 4 6 8 10 12 14 16 18 20 fréquence(KHz) -120
-140 Figure 4. Frequency dependent part of the SNR, 1/A(x, f, T)N(f) factor at different temperatures for -160 x=50 Km.
0.0 2 4 6 8 10 12 14 16 18 20 fréquence(KHz) fo(x,T) en fonction de la température 46 Figure 2. Frequency dependent part of the SNR, 44 X=2Km 1/A(x, f, T)N(f) factor at different distances for T=13 °C, k=1.5, moderate shipping activity(s=0.5) and 42 w=0. 40
fo(x,T) en fonction de la distance 38 60 36 50 T=13°C 34
fréquence optimale(KHz) fréquence 40 32
30 30 28 0 5 10 15 20 25 30 20 température(°C)
fréquence optimale(KHz) fréquence Figure 5. Optimal frequency VS temperature for x=2 10 Km. fo(x,T) en fonction de la température 0 50 1 10 20 30 40 50 60 70 80 90 100 distance(Km) x=5Km 45 x=4Km Figure 3. Optimal frequency VS distance for T=13°C. x=3Km 40 x=2Km Temperature fluctuation of the sea surface layers creates an important optimal frequency variation 35 (figure 4) from 28 to 46 KHz for a distance equal to 2 Km (figure 5). If we increase the distance between 30 two nodes (figures 4 and 6), the optimal frequency fo 25 decreases and the temperature variation effect optimale(KHz) fréquence becomes less important. In fact, for x=5 Km fo varies from 15 to 28 KHz and for x=50 Km it varies from 4 20 to 9 KHz. We notice that surface layers temperature 15 variation effect on the optimal frequency, so on the 0 5 10 15 20 25 30 SNR, is less important than the distance variation température(°C) effect. So that, these results will be useful for single Figure 6. Optimal frequency VS temperature for x=2 hop and multi-hops networks, and less important for Km, 3Km, 4Km, 5Km. DTN (disruption-tolerent network) network, where the
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three specific temperatures (0 °C for arctic area, 13°C and 30°C for Mediterranean sea). We choose 2 4. Image quality and temperature different distances between the transmitter and the receiver. We find that the influence of the temperature variation on MSE and PSNR grows up significantly with The temperature variation effect is proven by distance. For X=3 Km, the highest sea temperatures transmitting a data through the underwater channel. In cause the lowest MSE value. From the inverse relation fact, we transmit an 80x80 RGB color image (Lena), between the MSE and PSNR, this translates to a high which is the composition of three gray scale images. value of PSNR. It means that the ratio of Signal to We used a digital binary modulation (BPSK) and an Noise (image to error) is more significant and fewer LMS (Least mean square) equalizer to compensate the errors are produced. We choose to increase the ISI caused by the rough channel. We change the water distance (X=5 Km) for better seeing the received temperature, fixe the distance at 3 Km, and we image degradation according to the medium compute the difference between the transmitted and temperature (table 4). Indeed, the error increase and the received images, which can be seen in figure (7). we obtain a large variation of MSE and PSNR values for the different temperatures. These results confirm
the conclusions in section 2 and 3.
Table 3. MSE and PSNR at different temperatures and X=3 Km. MSE_R MSE_G MSE_B Psnr_R Psnr_G Psnr_B
T=0 1297.9 2083.3 4633 16.99 14.94 11.472
7(a) 7(b) T=13 1055.9 1628.7 2475.6 17.89 16.01 14.194
T=30 944,34 1575,4 1885.6 18.38 16.15 15.376
Table 4. MSE and PSNR at different temperatures and X=5 Km.
MSE_R MSE_G MSE_B Psnr_R Psnr_G Psnr_B 7(c) 7(d) T=0 2786.4 9685.2 13487 13.68 8.2697 6.8318
Figure 7. (a) Original image, (b) received image T=13 1446.5 3477.8 6160 16.52 12.718 10.235 T=0°C, (c) received image T=13°C, (d) received T=30 1310.5 2114.2 4735.8 16.95 14.879 11.377 image T=30°C.
We used two of the error metrics to compare the difference between the transmitted and the received image, for different temperatures: the Mean Square 5. Conclusion Error (MSE) and the Peak Signal to Noise Ratio In this article the focus was on the temperature (PSNR) given by the following formulas: variation effect, at different frequencies and nodes distances, on acoustic signal attenuation, and its impact on SNR ratio, optimal frequency and signal M1N1 quality. First, we ensured that the temperature 1 2 MSE [PS (x, y) Pr (x, y)] (9) influence is a linear function of the distance between M * N x0 y0 two nodes. Otherwise, minimizing this distance will decrease its negative impact on the acoustic signal. 2552 P 10log (10) For frequencies higher than 50 KHz the SNR 10 MSE temperature variation becomes very important and must be taken into account when modelling a With: PS (x, y): the pixel value at the position (x, transceiver and include it in the channel model. y) in the original image. Pr (x, y): the pixel value at the position (x, y) in the Secondly, we studied the relationship between received image. SNR Ratio and temperature variation and we extracted M and N are the dimensions of the image. optimal frequencies values which allowed the maximum SNR ratio. Table 3 and 4 present the three MSE and PSNR values corresponding to Red, green and blue color for When the temperature of the surface layers varies
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(from 0 to 30C) we notice an almost linear optimal frequency increase especially with an important distance between two nodes (5 Km). In addition, we noticed that if we increase the distance between two nodes, the optimal frequency fo decreases and the temperature variation effect becomes less important. So, this parameter will not very useful in DTN networks but crucial in single and multi-hops network. Finally, we evaluate temperature effect on image transmission quality in a harsh underwater environment, and we confirmed that low temperature in shallow water causes less errors and better image quality. Future research should focus on using these results to fix the signal bandwidth and the channel capacity according to the SNR threshold needed for a specific underwater wireless transmission system, so it will help us to optimize our transceiver.
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