International Conference on Communication and Signal Processing, April 6-8, 2016, India
Research Trends in Underwater Communications-A Technical Survey B.Pranitha, L.Anjaneyulu
Abstract—Research in Underwater Communications is tremendously growing now-a-days. Because of changes in undersea environment and multipath fading, it has become a challenging task for the researchers and scientists to communicate underwater. This paper describes various signals that can be passed through underwater and also the factors influencing them. Various techniques used for underwater communication, such as, Orthogonal Frequency Division Multiplexing (OFDM), differentially encoded quadrature phase shift keying (DQPSK OFDM), Magnetic Induction (MI) and the parameters, models used in Underwater Acoustics (UWA) Communications are presented. The ongoing areas of research have been identified and a summary of the research activities and future trends have been presented. Index Terms—UWA, OFDM, DQPSK OFDM, MI
I. INTRODUCTION
W
HEN we think of wireless communication what usually come to our mind is land terrestrial networks, with their applications that range from industrial to ambient assisted living. But there is also underwater communication. The first use of underwater acoustics (UWA) was started in 1490, by Leonardo Da Vinci, but this area of engineering came into complete picture in the early 20th century when the tragic sinking of Titanic compelled the research community to explore the area of underwater communication. And during World Wars this area explored several new applications like underwater telephones. The introduction of things like underwater sensor network, submarine Communications, military surveillance and remotely operated vehicles (ROV) opened a whole new area of research and development. Although most of the applied concepts have been adopted from radio frequency (RF) applications, the major challenge has been on how to apply those concepts to the underwater channel [1].
B.Pranitha is with the Electronics and Communication Engineering Department, National Institute of Technology, Warangal, India. (email:
[email protected]) (Ph.No:9701443592) Dr.L.Anjaneyulu is with the Electronics and Communication Engineering Department, National Institute of Technology, Warangal, India. (email:
[email protected])
For underwater communication initially “Gertrude” or “underwater telephone” was developed with a frequency range of 2-25 KHz. Underwater Acoustic Communications (UAC) pose new challenges to wireless communications research due to the harsh underwater environments and due to limited bandwidth, extended multipath, rapid time variation and severe fading. Various applications of underwater communications include Oceanography, Marine archeology, Offshore oil exploration, Rescue missions, Under water sports, Military and Navigation purposes. The rest of the paper is organized as follows: Section-II describes the nature of signals for Underwater Communications (UWC). Section-III conveys the factors that influence the Underwater Communications. Section-IV elucidates the techniques that are used for UWC and the research directions are presented in Section-V. II. NATURE OF SIGNALS FOR UWC Oceans cover about 70% of the Earth’s surface. The four major means of communication through underwater are using (i) Acoustic waves (ii) EM waves (iii) Optical signals (iv) Optical fiber cables. EM waves work in power limited region and they get attenuated rapidly in water. Optical signals are limited to short distances (less than 100m) and rapidly absorbed in water, though they can carry more information. So we are left with Acoustic signals that can travel to longer distances. When temperature of water increases by 1°C, sound speed increase by 1.4 m/s. As the depth of the water increases by 1km, sound speed increases by 17 m/s, but it is bandwidth limited [3]. The equipment used to convey information within these environments is the general hydrophone. So, Acoustic waves are used generally to carry information inside the water because of various drawbacks of other signals. A sound wave travelling inside the water consists of alternating compressions and rarefactions. These compressions and rarefactions are detected by a receiver, such as the human ear or a hydrophone. General frequencies associated with underwater acoustics are between 10 Hz and 1 MHz. The propagation of sound in the water at frequencies lower than 10 Hz is usually not possible without penetrating deep into the seabed, whereas frequencies above 1 MHz are rarely used because they are absorbed very quickly. The common and most used acoustic waves, while promising long
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Fig. 1. Block diagram for an OFDM system for Underwater Communications [8]
communication ranges under water, exhibit high propagation delay along with unreliable and unpredictable channel behavior and low data rate, which in turn are caused by complex multi-path fading, Doppler effects. The significant variations of these properties are due to underwater temperature, salinity or pressure III.FACTORS INFLUENCING THE UNDERWATER COMMUNICATIONS A. Ambient Noise The ambient noise in the ocean can be modeled using four sources: turbulence, shipping, waves, and thermal noise. Turbulence noise has its influence only in the very low frequency region for f 100 kHz. The noise power in all these cases can be calculated by the empirical formulae given in [4] B. Multipath Channel Underwater channel is a time varying multipath channel causing Inter symbol interference (ISI), Inter Carrier Interference (ICI), Inter Channel Interference (ICI) and fading. Due to the detrimental effect of time and frequency spreading, achieving high data rates in underwater wireless communication is challenging [5]. C. Doppler Effect The Doppler Effect of acoustic waves is much more severe, because of the following reasons: the speed of sound is low, the system is inherently a wideband system, the Doppler
frequencies can be relatively large compared to the carrier frequency, and the Doppler shifts are dependent on the subcarriers. The frequency of the transmitted signal is significantly distorted by the Doppler Effect and multipath propagation. The motion-induced distortion has far-reaching implications on the design of the synchronization unit and the channel estimation algorithm. Generally, Doppler Effect is caused by (i)Doppler shift caused by Tx / Rx motion. (ii)Doppler shift caused by the moving sea surface. Also Signal to Interference Ratio (SIR) not only depends on Doppler shift but also depends on bandwidth, the transmit power and all these factors are related as follows:(i) As Doppler frequency increases then Inter Channel Interference (ICI) power (or) transmit power increases. (ii)As bandwidth increases ICI power decreases but the ambient noise power increases [6]. IV.TECHNIQUES USED FOR UNDERWATER COMMUNICATIONS (UWC) The following are some major examples out of various techniques used for implementation of UWC systems. A. Orthogonal Frequency Division Multiplexing (OFDM) Generally OFDM is the best technique that is suitable for transmission of information in underwater communication [5][6].OFDM is a multi-carrier modulation technique that has received considerable attention for UWA communications. OFDM is an efficient method to tackle delay spread induced ISI in underwater acoustic communications [7]. B.C.Kim and I.Lu et al., have implemented a broadband underwater communication system with Doppler time scaling effect as shown in fig.1 [8]. The input data is modulated initially using QPSK modulation scheme. Then the serial data
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is converted to parallel data and Inverse Fast Fourier TABLE1 COMPARISON OF UNDERWATER MI, EM, ACOUSTIC AND OPTICAL COMMUNICATIONS [11]
Transform (IFFT) is applied to convert frequency domain signal to time domain signal. After IFFT, the parallel data is converted to serial data and cyclic prefix is applied to the serial data. The digital data is then converted to analog data using a Digital to Analog (D/A) converter and it is passed through a Low pass filter (LPF). The output of the LPF drives the transducer, finally to transmit data as Acoustic waves. The hydrophone at the receiver receives these acoustic waves and converts into electrical signals. The signals then undergo various operations of filtering, analog to digital conversion, re-sampling, FFT, demodulation until the original data is obtained at the receiver. A two dimensional Minimum Mean Square Error (MMSE) using time and frequency pilots are employed for channel estimation. Doppler frequency shift is estimated using cyclic prefix and is compensated by a digital sampling rate conversion procedure. The equations (1)-(7) of various OFDM signals as given in [6] are shown below. The transmitted OFDM signal after performing IFFT is given by 𝑥[𝑡𝑛 ] =
1 √𝑁
𝑗2𝜋𝑘𝑛/𝑁 ∑𝑁−1 𝑛 = 0,1. . 𝑁 − 1 𝑘=0 𝑋[𝑓𝑘 ]. 𝑒
Combining equations (1), (2) and (3) we have 1 𝑗2𝜋(𝑚−𝑘)𝑛/𝑁 𝑋̂[𝑓𝑘 ] = ∑𝑁−1 [(∑𝑁−1 ) X [𝑓𝑚 ] ] 𝑛=0 𝐻 [𝑓𝑚 , 𝑡𝑛 ] 𝑒 𝑁 𝑚=0 +W[𝑓𝑘 ] = S [𝑓𝑘 ] + I [𝑓𝑘 ] + W [𝑓𝑘 ] (4) 1
1
√𝑁
𝑗2𝜋𝑘𝑛/𝑁 ∑𝑁−1 +w [𝑡𝑛 ] 𝑘=0 𝐻 [𝑓𝑘 , 𝑡𝑛 ]X [𝑓𝑘 ] . 𝑒
1 √𝑁
∑𝑁−1 ̂ [𝑡𝑛 ]𝑒 − 𝑛=0 𝑥
𝑗2𝜋𝑘𝑛 𝑁
𝑁−1 𝑗2𝜋(𝑚−𝑘)𝑛/𝑁 I [𝑓𝑘 ] =∑𝑁−1 X [𝑓𝑚 ] 𝑚=0 ∑𝑛=0 𝐻 [𝑓𝑚 , 𝑡𝑛 ] 𝑒
(6)
W [𝑓𝑘 ]
𝑚 ≠𝑘 1 = ∑𝑁−1 𝑤[𝑡𝑛 ]. 𝑒 −𝑗2𝜋𝑘𝑛/𝑁 √𝑁 𝑛=0
(7)
In the equations above, S [fk] is the desired signal, I[fk] denotes the interference signal, and W[fk] the Fourier transform of the ambient noise w [tn] Further, the combination of OFDM with non-coherent detection scheme helps in the simple design of receiver, which is reliable and spectral efficient [9]. Differential coding is chosen to overcome the harmful effect of random phase in the receiver and also for slow Doppler frequency shift, the phase difference remains constant and the data can be received. This method can also transmit image through underwater channel. DQPSK OFDM with selection combining diversity is very promising solution for node-tonode communication. The simplicity of this scheme is its ability to handle multipath propagation. Multi-input multi-output (MIMO) is extensively considered in underwater acoustic (UWA) communications to overcome the bandwidth limitation of undersea channel. Generally for shallow water acoustic channel in MIMOOFDM we consider the defaulted depth of 30m and range of 1000m.With the capacity based approach, a useful tool to design and optimize undersea MIMO-OFDM communication systems was developed [10].
(1)
(2)
Where H[fk, tn] is the TVCTF (Time-variant channel transfer function) of the UWA channel model, and w[tn] denotes the ambient noise which heavily depends on the signal frequency. At the receiver, the demodulated data at the kth sub-carrier is obtained as ̂ 𝑘] = 𝑋[𝑓
(5)
𝑁
Where X[fk] is the transmitted data of the kth sub-carrier, x[tn] is the discrete time sample of the UWA-OFDM system, and N is the number of sub-carriers. This signal is transmitted over the UWA channel. The received UWAOFDM signal at time tn is given by 𝑥̂[𝑡𝑛 ] =
Where S [𝑓𝑘 ] = ∑𝑁−1 𝑛=0 𝐻 [𝑓𝑘 , 𝑡𝑛 ]X [𝑓𝑘 ]
B. Magnetic Induction (MI) In the last decade, research on magnetic induction (MI)based communications in challenged and harsh environments was carried out. Different from acoustic waves that propagate at a speed of 1500 m/s under water, MI waves propagate at a speed of 3.33 ×107 m/s under water. The comparison of MI, EM, acoustic, and optical
(3)
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communications is summarized in Table 1 [11]. This extremely high propagation speed of MI waves can significantly improve the delay performance of underwater communications, while facilitating the design and implementation of the underwater networking protocols, such as medium access control (MAC) and routing, and the underwater networking services (e.g., localization). Moreover, physical layer synchronization among wireless devices becomes simple and reliable due to the negligible delay and stable channel. The underwater MI channel modeling where in the magnetic field is generated by the modulated sinusoid current along an MI coil antenna at the transmitter. The receiver takes the information by demodulating the induced current along the receiving coil antenna. Generally three types of environments that are considered in underwater are: sea water with conductivity 4 S/m, lake water with conductivity 0.005 S/m and drinking water with conductivity 0.0005 S/m. According to Fig. 2, using a pocket-sized wireless device can achieve 20 m communication range in the drinking water, more than 10 m range in the lake water, but less than 1 m range in the sea water. Due to the orders of magnitudes differences in the medium conductivities in the sea water, lake water, and drinking water, high difference is observed in communication ranges.
Fig. 3. BER Performance comparison of Chirp-DPSK in measured UWAC[12]
V.FUTURE SCOPE Advances in underwater acoustic communications are slow, due to the cost of sea trials and the lack of standards. The extensive work carried out for wireless simulations cannot be easily done in underwater, due to the peculiarities of this environment [3]. The transmission range of MI communications can be extended by adopting the MI waveguide technique [11]. In underwater communications using OFDM, bandwidth efficiency of the scheme can be improved by the use of 8-PSK or QAM for subcarrier modulation. Forward error correction coding (FEC) and interleaving will make the system more robust against the channel distortion. Application of Diversity techniques will further improve the performance of the communication system against the fading channel impairments [6]. ACKNOWLEDGMENT The authors are grateful to the administration of National Institute of Technology, Warangal REFERENCES Prashant Kumar, Vinay Kumar Trivedi, Preetam Kumar.”Recent Trends in Multicarrier Underwater Acoustic Communications”, 2014. [2] Mandar Chitre, Shiraz Shahabudeen, Lee Freitag, Milica Stojanovic, ”Recent Advances in Underwater Acoustic Communications and Networking”. [3] J.Poncel, M.C.Aguayo, P.Otero, “Wireless Underwater Communications”, Published in Springer Science+ Business Media, LLC. 2012. [4] M. Stojanovic, “On the Relationship between Capacity and Distance in an Underwater Acoustic Channel,” ACM SIGMOBILE Mobile Comp. Commun. Rev., vol. 11, no. 4, Oct. 2007, pp. 34–43. [5] K.Chithra,N.sireesha.,Thangavel.,V.Gowthaman.,S.SathyaNarayanan , Tata sudhakar and M.A.Atmanand.,”Underwater Communication implementation with OFDM”, Indian Journal Of Geo-Marine Sciences, 2015. [6] Do Viet Ha, Nguyen Van Duc, Matthias Patzold, ”SINR Analysis of OFDM Systems Using a Geometry-Based Underwater Acoustic Channel Model”, Published in 2015 IEEE 26th International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC): Fundamentals and PHY [7] Charalampos C. Tsimenidis, Yuriy Zakharov, Christophe Laot, Konstantinos, Pelekanakis,Paolo Casari, and Andrey K.Morozov, ”Underwater Communications and Networking”, Published in Journal of Electrical and Computer Engineering (2012). [8] B.C.Kim and I.Lu, “Parameter study of OFDM underwater communications system,” in Proc. IEEE OCEANS 2000, vol. 2, Providence, RI,USA, pp. 1251–1255, Sep. 2000. [1]
Fig. 2. Path loss of the underwater MI Underwater Communications [11]
C. M-ary Chirp (MCSS-DPSK) In order to increase the data rate, a novel modulation method combing MCSS, DPSK and time overlapping named MCSS-DPSK was proposed by Chengbing He, Maohua Ran, Qinwei Meng, Jianguo Huang et al., [12]. A data rate of 300 bps with BER below 10-4 at a distance of 25 km has been achieved. Computer simulations have been carried out to and Fig.3 shows the comparison of BER performance of Chirp-DPSK/QDPSK with Rake receiver and it was proved that Rake receiver can improve the performance by at least 3dB based in this channel model.
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[9]
Prashant Kumar, Vinay Kumar Trivedi, Preetam Kumar, “Performance Evaluation of DQPSK OFDM for Underwater Acoustic Communications” IIT Patna. [10] Pierre-Jean BOUVET and Alain LOUSSERT., “An analysis of MIMO-OFDM for Shallow water Communication Systems’ in Underwater Acoustics Lab” 2011. [11] Ian F. Akyildiz, Pu Wang, and Zhi Sun,” Realizing Underwater Communication through Magnetic Induction, Published in IEEE Communication Magazine 2015. [12] Chengbing He, Maohua Ran, Qinwei Meng, Jianguo Huang, ”Underwater Acoustic Communications using M-ary Chirp-DPSK Modulation”, Published in ICSP 2010.
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