Channel Coding for Underwater AcousticCommunication System

Andre Goalic*, Joel Trubuil* and Nicolas Beuzelin***ENST-Bretagne, BP 83818, 29238 Brest Cedex 3, France

**G.E.S.M.A. (Groupe d'Etudes Sous-Marines de l'Atlantique), BP 42, 29240 Naval Brest, France

Abstract-GESMA (Groupe d'Etudes Sous-Marines de l'Atlantique) ob-jective is to develop a sufficiently robust acoustic link allowing the transmis-sion of different information (text, images...). A real-time platform namedTRIDENT (TRansmission d'Images et de Donnes EN Temps reel) was de-signed at ENST Bretagne. A blind spatio-temporal equalizer is used to re-duce the various perturbations brought by the underwater acoustic chan-nel (UWA). The acoustic link performances was evaluated and shown itsrobustness for transmission in a strongly disturbed channel. GESMA alsowants to increase the link reliability and provides strongly protected lowbit rate speech (MELP, 2400 bps) transmission. In order to do so, a chan-nel coding will be added to the system. Different kinds of error correct-ing scheme will be tested including Convolutional Codes (CC) and ReedSolomon (RS) block code. A Differential Phase-Shift Keying (DPSK) is usedto solve phase ambiguities in case of CC coding and Viterbi decoding uti-lization.

Keywords-Underwater acoustic communication, Channel coding, Con-volutional code, Reed Solomon code, BER (Bit Error Rate), MELP (MixedError Linear Prediction).

I. INTRODUCTION

Since a few years, GESMA (Groupe d'Etudes Sous-Marinesde l'Atlantique), in collaboration with ENST Bretagne and SER-CEL, is developing a robust underwater acoustic link to im-prove vehicles autonomy. An acoustic transmission usually iscorrupted by different impacts brought about by the underwaterchannel. One can note multipath propagation, Doppler effectand noise. From another point of view, carrier frequency andavailable bandwidth are much lower than those existing in othercommunication channels. To mitigate these different effects andoptimize spectral efficiency, a blind spatio-temporal equalizerintroduced by J. Labat et al [1], [2] was chosen.

A real time prototype was designed at ENST Bretagne. Thisprototype named TRIDENT [3] (TRansmission d'Images et deDonnes EN Temps reel) is an acoustic link able to transmit im-ages, text and data. With such a platform, information can betransmitted at a data rate higher than 20 kbps in horizontal con-figuration without periodic and training sequences. The Equal-izer has already shown its robustness and reliability to struggleagainst strongly disturbed channel. Moreover, speech transmis-sion were successfully realized with a 6 Kbps speech coder inBrest bay over 2 km. GESMA also wants to increase the linkreliability and provide strongly protected burst transmission forAUVs (Autonomous Underwater Vehicle).

Now, the purpose is to choose channel coding abilities ableto correct residual errors and thus adds extra improvements inBit Error Rates (BER). In order to do so, two kinds of chan-nel coding are evaluated (Convolutional Coding (CC) and ReedSolomon (RS) block). In case of Convolutional Coding and

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Fig. 1. TRIDENT Platform

Viterbi decoding utilization, a Differential Phase-Shift Keying(DPSK) is used to solve phase ambiguities. In order to pro-vide extra ranges over 4 km for speech transmission, anotherlow bit rate speech coder, named Mixed Excited Linear Predic-tion (MELP, 2400 bits/s) [4] is under consideration.

This paper aims to highlight some aspect of the high datarate acoustic link. Firstly, we present the TRIDENT platformwith the different extensions. Secondly, different channel cod-ing strategies are described. Then, we present main charac-teristics of the low bit rate speech coder/decoder (2400 bits/s,MELP) under consideration for TRIDENT extension. The lastpart will present some preliminary results.

II. TRIDENT ACOUSTIC SYSTEM

The TRIDENT system is a high data rate acoustic link basedon blind spatio-temporal equalizer called SOC-MI-DFE (SelfOptimized Configuration - Multiple Input - Decision FeedbackEqualizer) [1]. The SOC-MI-DFE uses input signals sampledon several sensors coming from the same emission source. Thisspace diversity provides a better SNR (Signal to Noise Ratio)compared to a mono-sensor version. The SOC-MI-DFE is ableto run according to two modes: a convergence or starting modeand a tracking mode which are differentiated both on structuraland algorithmic levels. Switching between the modes are car-ried out in an automatic and reversible way by comparison ofthe MSE (Mean Square Error) with a threshold. The interest ofthis adaptivity lies in the possibility to switch from one struc-ture to another according to the channel severity. Moreover, itis not necessary to use preamble or another training sequence.In fact, only user data are transmitted and spectral efficiency isincreased.

The TRIDENT platform (Fig. 1) can use two carrier fre-

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quency (20 and 35 kHz). The bit rate under consideration arefrom 6.7 to 23.3 kbps with a QPSK modulation (QuadraturePhase Shift Keying). The receiver platform is based on an acqui-sition board, plugged in a personal computer (PC). The architec-ture of this board is based on a Texas Instruments Digital Sig-nal Processor (DSP) namely the TMS320C6201. In reception,acoustic signals are received on 4 hydrophones. These input sig-nals are synchronously sampled. Demodulation, rhythm recov-ery and equalization are then performed using digital process-ing. Decided data or output equalizer samples are then trans-ferred to the PC to be processed for source decoding.

The acoustic link was evaluated during sea-trials carried outbetween 2002 and 2004. The adaptive receiver was tested forcontinuous data stream transmission. The acoustic link have tobe modular enough in order to test many configurations and toevaluate its robustness on many scenarios. Most of the trans-mitted sequences were successfully demodulated during sev-eral minutes. Receiver robustness and adaptability was clearlyshown. Spatial diversity interest is confirmed. In order to vali-date the TRIDENT system in an operational context, the acous-tic modem was integrated onboard the REDERMOR II AUV[5]. According to the results obtained during sea-trial carried outin June 2005, one can note the robustness of the spatio-temporalequalizer. The first tests show that real time transmission of in-formation (sonar images) is feasible even with harsh channelssuch as the underwater acoustic channel and in presence of mul-tiple interferers. In this context, the contribution of a channelcoding can improve the transmission robustness and protect thedata transmitted from remaining errors.

So far, this acoustic link version does not use channel coding.To evaluate different source and/or channel coder, data are emit-ted from a reference file. A new carrier frequency (11.2 kHz) isadded to allow low bite rate (from 2.8 to 5.6 kbps) speech trans-mission. For this reason, platform extension concern the newdesign of the emitter (Fig. 1). Unlike the receiver, the emitterplatform is based on an external board with an identical DSPconnected to the PC by an USB link (Universal Serial Bus).

III. CHANNEL CODING STRATEGIES

In order to improve the acoustic link, channel coding maycorrect remaining errors. Main objectives are to decrease BERfrom 10-2 down to 10-4 . Different channel coding strategiescan be used. In this project we check the use of ConvolutionalCodes (CC) and Reed Solomon (RS) block codes. Differentialcoding may be added for its efficiency in dealing with phaseambiguity in reception.

A. Convolutional codes

Convolutional codes are commonly specified by three param-eters (n, k, m):. n: number of output bits. k: number of input bits. m: number of memory registersThe quantity k/n called the code rate is a measure of the effi-ciency of the code. Commonly k range from 1 to 8 and n, from

e(x)

S2 (x)

Fig. 2. CONVOLUTIONAL CODE (7,5) SCHEME

2 to 10. Usually convolutional codes are specified by parame-ters (n, k, L), quantity L designing the constraint length of thecode. It is defined by L = k(m -1) and it represents the num-ber of bits in the encoder memory that affects the generation ofthe n output bits. The decoding process use the Viterbi algo-rithm with a trellis representation. Decoding is perform withtwo option: hard and soft decision decoding. The hard decodingonly uses binary values, whereas the soft option uses real valuescoming from output equalizer or soft differential decoder.The first code studied is the convolutional code (CC) (7, 5)1.This code is defined by two equations (1),(2), Xq is the inputvalue q-delayed, and is represented as we can see in the Fig. 2:

Si(x) =(1 + x + X2)e(x)

S2(X) = (1 +X2)e(X)(1)

(2)In this case, two zero bits are added at the end of the frame in

order to close the treillis.

Si(x) =(1 + x + X2)e(x)

S2(X) = (1+X +X2+X4)e(x)

(3)

(4)The scheme for the CC (35, 23) is shown in Fig. 3. Equations

(3),(4) define this code. In this case, four zero bits are added atthe end of the frame.

SI (X)

e (x)T

S2 (x)

Fig. 3. CONVOLUTIONAL CODE (35,23) SCHEME

Both hard and soft decoding are studied and evaluated.

B. Reed Solomon codes

Reed Solomon (RS) codes (n, k, t) are cyclic codes built fromn symbols with a maximum of n = q -1, where q is the num-ber of elements in the Galois Field (GFq) and q = 2n. t is thesymbol power corecting code, so the number of control sym-bols is 2t and the number of information symbols that can be

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transmitted is k = n -2t. The decoding process can also behard or soft. Hard decoding does not use information comingfrom the channel whereas soft decoding uses such informationto improve the decoding process. Based on the Chase algorithm,the soft decoding process also uses Berlekamp and Chien algo-rithm to correct received symbols. In our case, we look after 4unreliable bits and check over 16 possible codewords.

C. Phase ambiguityReceiver and emitter have to be synchronized. Carrier recov-

ery is classically resolve by the use of a Phase Locked Loop(PLL). Without resolving the fourfold phase ambiguity, the PLLcould lock to any of four possible phase states, only one has theright carrier phase state. Since the QPSK is invariant to 7/2, 7and 37w/2 shift, the receiver suffers from phase ambiguity. A so-lution to cut out this problem is to applied a differential codingto the phase of the transmitted QPSK symbols. In case of harddecisions, the solution is well-known.

Different solutions proposed in [6] and [7] may be usedfor soft differential decoding. Proposed modifications of DPSKwould reduce the amount of signal processing that would haveto be done in the receiver. This can be based on the phase of theQPSK signals xo [n] = jV) [n]

bDiff [n] = bDiff [n- 1] + 4b[n] (5)

QPSK symbols can be obtained using the following equation:

x [n] = x [n -l]o [n] (6)

where x [n] = ej PDiff is the differentially encoded QPSK(DQPSK) symbol stream. Decoding can be based on the phasevalues of the received DQPSK signal x according to:

4 [n] = ODiff [n] -4Diff [n-1] (7)

where y [n] = e /Diff [n] or alternatively:

yo [n] = y[n].y [n-1] (8)Rotated data ZDiff can be represented by:

ZDiff = y[n].y [n -1] (9)Then the real part and the imaginary part of ZDi ff are weightedwith the short term average of the squared errors to generate thesoft decisions (Log-likelihood ratio) both soft real and imagi-nary part of ZDiff.

ZDiff .real = real (ZDiff )/72 (10)ZDiff.lmg = tmg (ZDiff ) /7 ( 11)

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Fig. 5. CONVOLUTIONAL CODE (7,5) and (35,23)

in order to compute soft differential decoding output (or beingthe output equalizer variance).

IV. MELP CODER/DECODER

The Mixed Excitation Linear Prediction (MELP) voice codermodel is based on the traditional LPC vocoder. However, theMELP has additional abilities, like mixed pulse and noise ex-citation, periodic or aperiodic impulses, adaptive spectral en-hancement and pulse dispersion filter. New features allowMELP coder/decoder to better represents the natural humanspeech [8]. Speech signal is segmented in frames having a du-ration of 22.5 ms, with a 8 kHz sample frequency. Each speechsample is 16 bits-quantizied giving a number of 2560 bits perframe at the MELP coder input. After a compression factorclose to 53, this bit rate is down reduced to 54 bits per frame,that is to say 2.4 kbps [9]. Synchronization bits are added to thebit stream to find back the speech frame at the decoder.

V. RESULTS

To improve performance of TRIDENT acoustic link and be-fore adding speech extension, channel coding is under con-sideration, so far we examine Convolutional Codes and ReedSolomon block code. Once solved the equalizer robustness,channel coding may add extra performance and improve speechsynthesis quality. Channel coding goal is to lower the bit errorrate from 10-2 to 10-4. Results presented are obtained by simu-lations in presence of Additive White Gaussian Noise (AWGN).Fig. 5, presents performance obtained with convolutional codesboth for a 4 states and a 16 states coder. It clearly appears thatdifferential coding decreases performance in doubling the bit er-ror rate. We take the BER at the output of the channel as areference for the 2 options, with Differential Coding (DD) andwithout DD (SD). At 10-5, we have a lost of 2.5 dB when usingDD both for hard and soft decoding. Let us recall that in case ofphase ambiguity, the decoding process may not work correctly.It also appears that we get better performance with the 16 statescoder/decoder but the 16 states decoding is more expensive in

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computational load. Fig. 6 shows the different codes answeringto the specifications. All the configurations using the soft, op-

tion allow to lower the BER from 10-2 to 10-4. In case of RScoding/decoding (Fig. 7), the soft option allows also to reach theBER of 10-4.

We also check the channel coding to protect the MELP bitstream. Errors may appear, resulting in signal modificationsat the synthesiser, shown in blue in Fig. 8. Each coding op-

tion has its coding rate (0.5 for the CC option and 0.93 forthe RS option). According to the coding option and the user

bit rate fixed, spectral efficiency may decrease and used fre-quency bandwith, significantly increases. Consequently equal-izer performance may be worst. In this case, coding contribu-tion might be insufficient to recover resulting degradation andimprove BER.

VI. CONCLUSIONS - PERPECTIVES

GESMA (Groupe d'Etudes Sous-Marines de l'Atlantique)and ENST-Bretagne already have developed a real time acous-

tic link named TRIDENT (TRansmission de Donnes EN Tempsreel) able to transmit images, text and data. An acoustic trans-mission is corrupted by different impacts. One can note multi-path propagation, Doppler effect and noise. A spatio-temporal

Fig. 8. Errors with a 5 dB SNR for a CC(7,5) Gaussian Channel

equalizer is used to reduce those different effects. GESMAwishes to extend TRIDENT possibilities to the tansmission ofspeech and also improves the acoustic link robustness by theaddition of channel coding. To extend the speech acousticlink range, GESMA decided to choose the low bit rate MELPcoder/decoder working at 2400 bps. First trials show a goodsynthesized speech quality.

Different kinds of error correcting scheme are presented in-cluding convolutional codes (CC), Reed Solomon codes (RS).Different options are also under consideration, the possibility ofhard and soft decoding for the two cases (CC and RS). In or-

der to solve the phase ambiguity, differential coding/decoding,both hard and soft option are presented in the case of CC cod-ing/decoding. The final decision have to take into account othersfeatures like code rate, spectral efficiency, real time constraints.Next step is to evaluate these different possibilities over sea tri-als transmissions.

REFERENCES

[1] J. Labat, C. Laot Blind adaptive multiple input decisionfeedback equalizerwith a selfoptimized configuration trans on Comm, Vol. 49, N4, April 2001.

[2] G. Lapierre, J. Labat, J. Trubuil Evaluation of an high data rate acousticlink: contribution ofblind spatio-temporal equalization Underwater Acous-tics ECUA 2000, vol. 1, pp. 549-554, 2000.

[3] J. Trubuil, G. Lapierre, T. Gall, J. Labat Real-time high data rate acousticlink based on spatio temporal blind equalization: the TRIDENT acousticsystem OCEANS 2002, Vol.4, pp 2438-2443 Biloxy, MI, USA

[4] L. M. Suplee, R. P. Cohn, J. S. Collura, A. V. McGree MELP: The New Fed-eral Standard at 2400 bps IEEE ICASSP'97 Conference, Munich Germany,pp 1591-1594

[5] J. Trubuil, G. Lapierre, J. Labat, N. Beuzeulin, A. Goalic, C. Laot ImprovedAUV autonomy provided by an underwater acoustic link International So-ciety of Offshore and Polar Engineers, ISOPE 2006, San Francisco, USA -

28 mai-2 juin, 2006.[6] H. Igarashi, K. Ueda, K. Murakami, T. Fujino Performance ofa soft-output

adaptive equalizer combined with soft-decision metrics generator based on

differential detection Vehicular Technology Conference, 1997 IEEE 47thVolume 2, 4-7 May 1997 Page(s):700 - 704 vol.2

[7] S. Weiss, M. R. Bennett, N. C. Tisdale, E. Gibson A differential QPSKModem Using the TMS320C6711 DSK Texas Instruments European DSPEducation and Research Symposium, Birmingham, November 16, 2004.

[8] D. J. Rahika, J. S. Collura, T. E. Fuja, D. Sridhara, T. Fazel Error CodingStrategies for MELP vocoder in wireless andATM environments NationalSecurity Agency and Maryland, Notre Dame Universities, USA.

[9] T. Fazel, T. Fuja Channel-Encoded Transmission of MELP-CompressedSpeech Electrical Engeneering Department, Maryland University, USA.

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