(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 9, Issue: 3, 2012

OFDM Pilot-Aided Underwater Acoustic Channel Estimation Thomas Pedersen #, Alex Bloomberg * #

Aalborg University, Aalborg, Denmark

*Aalto University, Aalto, Finland Abstract—Underwater Acoustic (UWA) communications have been regarded as the most challenging wireless communication systems due to their unique channel properties, such as severe multipath delay and large Doppler Shift. Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique has been widely adopted in the 4G wireless communication systems due to its unique merits, such as robust against multipath propagation. However, the literature on OFDM communications in UWA environments is scarce. In this paper, we investigate OFDM pilot-aided UWA channel estimation approaches, which involve in block-type pilot, comb-type pilot and grid-type pilot. Therein, comb-type pilot-aided channel estimation approach will combine some signal interpolation approaches. Simulation results will show the performance difference of UWA communication systems based on difference channel estimation pilots. Keywords—OFDM;

Time-varying UWA channels; pilot-aided channel estimation I. INTRODUCTION

Underwater acoustic (UWA) environments have been regarded as the most challenging environments to achieve high data rate and reliable communications. Acoustic propagation is characterized by four major factors: frequency dependent attenuation (e.g., attenuation that increases with signal frequency), severe time-varying multipath propagation, low speed of sound, and large Doppler shift [1-3]. Orthogonal Frequency Division Multiplexing (OFDM) is an efficient multi-carrier modulation technique, which divides available spectrum into many narrow bands and transmission data into parallel data streams each transmitted on a separate band. OFDM is a simple way to deal with multipath propagation and overcome problems of inter-symbol interference (ISI) and inter-carrier interference (ICI). Therefore, OFDM techniques have been widely adopted in the next generation wireless communication systems and networks, such as digital video broadcasting (DVB), wireless LANs, IEEE 802.11a, IEEE 802.11g, IEEE 802.16 broadband wireless access system , and HDTV. However, OFDM applications in UWA communications and networks are very scarce [410]. Channel estimation techniques in underwater acoustic environments can be employed in both a network simulator and software modem to quantify the channel-

induced distortion of acoustic signals and thereby to improve the quality of simulation and adaptability of modulation and demodulation, respectively. [11] presented various channel estimators that exploit the channel sparsity in a multicarrier UWA system, which include subspace algorithms from the array processing literature, namely root-MUSIC and ESPRIT, and recent compressed sensing algorithms in form of Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP). In [12], a decision-feedback equalizer is designed which relies on an adaptive channel estimator to compute its parameters. Along with developing of OFDM applications, the channel estimation for OFDM systems becomes a hot topic recently [13-15]. However, little literature focused on OFDM channel estimation for underwater acoustic environments. In [16], in order to overcome the long acoustic multipath limitation in performance metrics of UWA communications, the sparse nature of the channel is exploited in an algorithm based on a compact signal representation that leads to two forms of adaptive implementation. [17] proposed pilot-aided OFDM estimation for time-varying shallow water acoustic channels. In this paper, we propose pilot-aided OFDM channel estimation for UWA environments. Therein, pilot patterns include block-type, comb-type and rectangular-grid-type. In addition, comb-type-aided channel estimation process will combine with several interpolation approaches. Bellhop algorithm [18] is employed to generate timevarying UWA channel impulse response (CIR). Simulation results will show OFDM system performance in terms of difference pilot types. The rest of this paper is organized as follows: Section 2 is the introduction of pilot-aided OFDM communication systems, Section 3 is the introduction of three involved pilot types, Section 4 is the proposed channel estimation approaches for time-varying UWA channels, Section 5 is the simulation results and Section 6 concludes the paper. II. PILOT-AIDED OFDM CHANNEL ESTIMATION

Usually, UWA channel is one kind of fast time varying channels. Therefore, most practical multi-carrier UWA communication systems adopt pilot-aided channel estimation technique to track the fast varying UWA

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(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 9, Issue: 3, 2012

channels. Typically, there are three types of pilots described in Figure 1.

Where Y k and X (k) is the Y(k) and X (k) in corresponding pilot location. Submit Eq. (3) into (4), we can get I N  H (k) H (k)+ + (5) X X The expectation of

I X

+

N

X

is zero and distributed in

Gaussian. Eq. (5) can be written as nICI n  H (k) HP k (6) where ICI is the inter-symbol interference and is considered as a kind of additive noise. From Eq. (6) we can find that LS algorithm is vulnerable to environment noise. Figure1. Three types of pilots: (a) comb-type; (b) blocktype; (c) hexagonal grid-type In the comb-type arrangement, a number of subcarriers are reserved for pilot signals, which are transmitted continuously. Channel estimation can then be performed uninterruptedly based on these pilot subcarriers in every symbol. The spacing of pilot subcarrier must be less than the coherence bandwidth of the channel. In the block-type pilot arrangement, one specific symbol full of pilot subcarriers is transmitted periodically. The pilot symbol must appear at a frequency tens of times higher than the Doppler frequency in order to ensure the validity of the channel estimates. In other words, the interval between two consecutive pilot symbols must be significantly shorter than the channel coherence time. Consequently, block-type pilot pattern is suitable for systems operating under slow-fading channels. In the hexagonal grid-type pilot patterns, pilot subcarriers provide sub-sampling of the two-dimensional channel responses. Since pilot subcarriers are distributed in the shapes of hexagons in the time-frequency lattice, the sampling theorem must be obeyed in both dimensions to avoid the aliasing effect. This scattered pilot arrangement reduces the pilot density and thus improves spectral efficiency. III. CHANNEL ESTIMATION

In OFDM systems, the received signal can be described as                   Y K where        I K

N

X k H K

∑N

 H K                a e

D

T

,

Ka

I K

N K

X k N

sin πfD T e πfD T

N

K

D T

K

D T

K

(1)

e

N

IV. SIMULATION RESULT Parameter settings of the simulation are depicted in Figure 2.

Figure 2. Parameter communications

      2  

                                   

In this paper, we adopt Lease Square (LS) algorithm to do the channel estimation, which can be written as YP XP

of

the

UWA

In this system, we adopt the 8PSk modulation scheme, and with different Doppler Shift values (13kHz and 27kHz). The central frequency is 15kHz, bandwidth is 6kHz, the number of sub-carrier is 128, frequency spacing is 46.9kHzm, symbol duration is 21.3ms. Bit-error-rate (BER) performance with different pilot-aided channel estimation schemes are simulated via the Matlab, and results are shown in Figure 3 and Figure 4. From the simulation results, we can observe that hexagonal gridtype pilot-aided channel estimation method exhibits the best performance, but it also exhibits the highest system complexity. Therefore, considering this trade-off, in most real UWA communication systems, we adopt either combtype pilot or block-type pilot, which depends on the channel’s varying in the time domain.

  3

 H (k)

settings

(4) ©IJEECS

(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 9, Issue: 3, 2012 [4] X. Huang and V. B. Lawrence, “Capacity Criterion-Based Bit and Power Loading for Shallow Water Acoustic OFDM System with Limited Feedback”, IEEE 73rd Vehicular Technology Conference, pp.1-5, 2011 Spring

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10

block-type pilot hexagonal grid type pilot comb-type pilot

-1

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[5] X. Huang and V. B. Lawrence, “Bandwidth-Efficient Bit and Power Loading for Underwater Acoustic OFDM Communication System with Limited Feedback”, IEEE 73rd Vehicular Technology Conference, pp.1-5, 2011 Spring

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BER

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[6] -4

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10 12 SNR (dB)

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FIGURE 3. SIMULATION RESULTS BASED TYPES OF PILOTS (27KHZ DOPPLER SHIFT)

16

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20

ON DIFFERENT

block-type pilot hexagonal grid type pilot comb-type pilot

10

BER

[8] X. Huang, “Capacity criterion-based power loading for underwater acoustic OFDM system with limited feedback”, IEEE WCNIS Conference, pp.54-58, 2010

[10] B. Li and M. Stojanovic, ``Alamouti Space Time Coded OFDM for Underwater Acoustic Communications," in Proc. IEEE Oceans'10 Asia Pacific Conference, Sydney, Australia, May 2010.

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[11] K.Tuy, D.Fertonani, T.Duman, M.Stojanovic, J.Proakis, and P .Hursky, “ Mitigation of Intercarrier Interference for OFDM over Time-Varying Underwater Acoustic Channels," IEEE Journal of Oceanic Engineering, vol.36, No.2, pp.156-171, April 2011.

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[7] X. Huang and V. B. Lawrence, “Effect of wind-generated bubbles on OFDM power loading for time-varying shallow water acoustic channels with limited feedback”, IEEE Oceans Conference, pp.1-6, 2011

[9] Tu, T.Duman, J.Proakis and M.Stojanovic, “Cooperative MIMO-OFDM Communications: Receiver Design for DopplerDistorted Underwater Acoustic Channels," in Proc. 44th Asilomar Conference on Signals, Systems and Computers, November 2010.

0

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G.Rojo and M.Stojanovic, “Peak-to-Average Power Ratio (PAR) Reduction for Acoustic OFDM Systems," Marine Technology Society Journal, vol.44, No.4, July/August 2010, pp.30-41.

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12 14 SNR (dB)

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FIGURE 3. SIMULATION RESULTS BASED TYPES OF PILOTS (13KHZ DOPPLER SHIFT)

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ON DIFFERENT

V. CONCLUSIONS Channel estimation is the most critical point for the success of UWA communications, especially for fast timevarying channels. In this paper, we evaluated the channel estimation performance with different types of pilots. Simulation revealed that based on different channel conditions, we should choose the corresponding pilot types in order to get the best performance.

[12] C. R. Berger, S. Zhou, J. C. Preisig and P. Willett, “Sparse channel estimation for multicarrier underwater acoustic communication: from subspace methods to compressed sensing”, IEEE Trans. on Signal Processing, vol. 58, no.3, pp. 1708-1721, March 2010. [13] M. Stojanovic, L. Freitag and M. Johnson, “ChannelEstimation-Based Adaptive Equalization of Underwater Acoustic Signals”, IEEE Oceans Conference, pp. 590-595, 1999. [14] O. Simeone, Y. Bar-Ness and U. Spagnolini, "Pilot-Based Channel Estimation for OFDM Systems by Tracking the DelaySubspace”, IEEE Trans. on Wireless Communications, vol.3. no. 1, Jan. 2004. [15] F. Shu, J. Lee, N. Wu and G. L. Zhao, “Time-Frequency Channel Estimating for Digital Amplitude Modulation Broadcasting Systems Based on OFDM”, IEEE Proceedings on Communications, vol.150, no.4, pp.259-264, August 32003 [16] M. Morelli and U. Mengali, “A Comparison of Pilot-Aided Channel Estimation Methods for OFDM Systems”, IEEE Trans. on Signal Processing, vol. 49, no. 2, pp.3065-3073, Dec. 2001.

REFERENCES [1] T. C. Yang, “Properties of underwater acoustic communication channels in shallow water”, J. Acoust. Soc. Am., vol. 131, no. 1, pp. 129-145, 2012 [2] M. Stojanovic and J. Presig, “Underwater acoustic communication channels: propagation models and statistical characterization”, IEEE Communications Magazine, pp. 84-89, Jan. 2009. [3] M.Stojanovic, ``Underwater Wireless Communications: Current Achievements and Research Challenges,” IEEE Oceanic Engineering Society Newsletter, Spring 2006

[17] M. Stojanovic, “Adaptive Channel Estimation for Underwater Acoustic MIMO OFDM Systems”, in Proc. IEEE DSP/SPE Workshop, 2009 [18] X. Huang and V. B. Lawrence, “OFDM with Pilot Aided Channel Estimation for Time-Varying Shallow Water Acoustic Channels”, IEEE CMC Conference, pp.442-446, 2010 [19] http://oalib.hlsresearch.com/Rays/index.html

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