R.I.E.I.T., Railmajra (S.B.S. Nagar), Punjab, India. R.I.M.T., Mandi Gobindgarh, Punjab, India

Enhancements in High Rate Signal Processing of Underwater Wireless Communication 1 Sartajvir Singh, 2 Ravneet Kaur, 3 Vishakha Sood 1,3 R.I.E.I.T., Railmajra (S.B.S. Nagar), Punjab, India 2 R.I.M.T., Mandi Gobindgarh, Punjab, India Abstract The past three decades have seen a growing interest in underwater wireless communications (UWC). Continued research over the years has resulted in improved performance and robustness as compared to the initial communication systems. High data rate communications over underwater acoustic channel is challenging due to time-varying multipath propagation. Multi-Input Multi-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is one viable solution to provide significant improvements in both data rate and reliability, is a promising technology for enhancing communications in the band-limited and highly-dynamic underwater acoustic (UWA) channel. However, this requires accurate channel estimation & Doppler estimation. Space-time processing of signals received by an antenna array allows reducing the Inter Symbol Interference (ISI) due to multipath propagation. Doppler spread always result in severe Inter Carrier Interference (ICI), can be overcome by pilot tone based channel estimation. In this paper, we dealt with an overview of the recently key enhancements in this field to achieve high data rate signal processing by compensate the undesirable effects as discussed above with new developed techniques. The key developments & obtained results suggest that MIMO-OFDM with necessary enhancement gives an appealing solution for high data rate signal processing over underwater acoustic channels. Keywords underwater wireless communication (UWC), Multi Input Multi Output (MIMO), Orthogonal frequency-division multiplexing (OFDM), Multicarrier Modulation, Inter symbol Interface (ISI), Inter Carrier Interface (ICI), low-density parity- check (LDPC) code, Space time coding. I. Introduction High Data rate signal processing in UWC is one of the most researching & challenging media use today. The underwater acoustic channel is severely band limited by multipath fading due to reflections and scattering and this limits both the error performance and channel capacity of acoustic communication systems operating in this environment. However, there is a pressing demand for higher data rate systems that can cope with the highly scattered underwater channel and this is especially true for underwater acoustic networks in the littoral environment. Traditionally, more bandwidth is required for higher data-rate transmission, due to the nature of the signal, the bandwidth of the systems is very limited and usual designs operate within a few tenths of khz. A series of review papers provide an excellent history of the development of the field until the end of the last decade [1-4]. A recent review paper presents an overview of the development in the field from the start of the decade [5]. Continued research over the years has resulted in improved performance and robustness as compared to the initial communication systems. Recently, multicarrier modulation in the form of orthogonal frequency division multiplexing (OFDM) has been actively pursued for underwater wireless communications; see e.g., [6,10]. On the other front, multi-input multi-output (MIMO) techniques have been applied to UWA communications via spatial modulations [11-12]. OFDM is attractive because it can meet the demand of High data rate in the band limited underwater acoustic channel. Guard intervals are always inserted to avoid multipath effects in OFDM communication. Frequency offset leads to inter-carrier interference (ICI) and hence performance degradation. In [13-14], pilot tone based channel estimation was used to reduce the OFDM system s sensitivity to frequency offset which leads to ICI. So we also used pilot tone based technique for channel estimation. Moreover Space-time processing of signals received by an antenna array allows reducing the inter symbol interference (ISI) due to multipath propagation. Several spacetime processing techniques are investigated in application to OFDM signals transmitted by a fast moving omnidirectional transducer [15]. Furthermore, this paper is organized as follows. The MIMO- OFDM technique is described in Section II. Transmitter designed introduced in Section III. Receiver algorithms & necessary estimation are shown in Section IV, Space time Coding in Section V, finally complete MIMO-OFDM System, conclusions are explained in Section VI & VII respectively. II. Basic Definations A. MIMO-OFDM Multicarrier modulation in the form of orthogonal frequency division multiplexing (OFDM) is considered for the next generation of acoustic modems as a low-complexity alternative to single-carrier modulation. This fact motivates the use of OFDM in underwater environments. The quest for efficient use of acoustic bandwidth pushes the system design towards a large number of carriers and multiple-input multiple-output (MIMO) configurations that support parallel transmission of independent data streams [16]. A basic System of MIMO-OFDM shown in Fig. 1. In a MIMO OFDM system operating with MT transmit and MR receive elements; there are MTMR channels whose transfer functions need to be estimated at each of the K (coefficients in the frequency domain). If performed in the impulse response domain then channel estimation will require L (coefficients in the time domain)<k coefficients per transmitter/receiver pair in a bandwidth-efficient acoustic system. To this end, at least MTL carriers have to contain known symbols. In block oriented processing, these symbols must be known a-priori (pilots or null carriers). In contrast, block-adaptive processing utilizes symbol decisions, and channel estimation can benefit from signals received on all carriers [16]. 74 International Journal of Electronics & Communication Technology

Fig. 1: MIMO-OFDM System B. Spatial Modulation Information theoretic studies have shown that the capacity of a channel increases linearly with the minimum of the number of transmits and receives antennas. This increase in capacity translates to a corresponding increase in achievable data rate through the use of multiple input multiple output (MIMO) processing techniques and space-time coding. Optimal detection techniques such as MAP and maximum likelihood sequence estimation (MLSE) exponentially grow in terms of complexity with the number of antennas. To address this problem, space-time trellis codes (STTC) and layered spacetime codes (LSTC) can be used with sub-optimal decoding techniques [17]. The benefits of MIMO over single-input single-output (SISO) underwater communication systems were successfully demonstrated a spatial modulation scheme with an outer block code, interleaver and an inner trellis-coded modulation (TCM) was demonstrated in [18]. With these experiments, demonstrated that with the spatial modulation scheme offered increased bandwidth and power efficiency as compared to signals constrained to temporal modulation. For ISI-limited channels, spatial modulation offers the possibility of increasing data rates when simply increasing transmission power does not. In [13] a MIMO-OFDM experiment, nearly error free performance was achieved with a 2-transmitter 4-receiver setup at ranges up to 1.5 km using a ½-rate low-density parity check (LDPC) code at a coded data rate of 12 kbps. The promise of increased throughput and spatial diversity in practical MIMO systems can only be achieved if the transducers in transmit and receive arrays are placed with spacing larger than the spatial coherence scale at the frequency of interest. In [19], the author theoretically and experimentally studies the gain due to spatial diversity given parameters such as the number of transducers and the spacing between them. Further research is required to better understand issues surrounding transducer locations, especially as their placement may be constrained in mobile systems such as autonomous underwater vehicles (AUV). C. Iterative Equalization Methods To increase the fidelity of such underwater acoustic communication links, a controlled amount of redundancy is added for error correction coding (ECC). Block codes, convolutional codes, and LDPC codes have all been used in various contexts. While including ECC helps to reduce overall Bit error rate (BER), ECC are considerably less effective when used subsequent to, but isolated from equalization in the presence of severe ISI. Fortunately, the block-processing nature of the transmitted data allows the use of the vast array of iterative decoding and equalization algorithms that have been developed since the advent of turbo codes and turbo equalization [20] methods over the last decade. Turbo equalization has been shown to provide significant performance gains, even for severe ISI channels, through iterative soft-input/soft-output equalization and decoding. However, due to long delay spreads involved, MAP-based turbo equalization is simply impractical. Similarly, Minimum Mean Square Error (MMSE)-based methods requiring channel knowledge at the receiver also have a computational complexity that is often beyond the available resources. This computational burden is further amplified by the need to perform the equalization and decoding steps multiple times over the received data block [21]. A number of low-complexity methods for turbo-equalization have been developed over the last decade and many of these methods have been applied with initial success in underwater acoustic environments including those exploiting multi-channel spatial and temporal diversity combining methods [22, 23]. D. Diversity Diversity leads to improved link reliability by rendering the channel less fading and by increasing the robustness against co-channel interference. Diversity gain is obtained by transmitting the data signal over multiple (ideally) independently fading dimensions in time, frequency, and space and by performing proper combination at the receiver. Spatial (i.e., antenna) diversity is particularly attractive when compared to time or frequency diversity, as it does not incur expenditure in transmission time or bandwidth respectively [19]. Space-time coding realizes spatial diversity gain in systems with multiple transmit antennas without requiring channel knowledge at the transmitter. III. Transmitter Design The transmitted signals were of the zero-padded OFDM type [24], given by s(t) = Re{u(t) } (1) u(t) = (2) Where g (t) is a unit-amplitude rectangular pulse of duration T, T _ = T +T g is the signaling interval that includes the multipath guard timet g, f g is the lowest carrier frequency, Δ f = 1/T is the subcarrier spacing, K is the number of subcarriers, and (n) is the data symbol transmitted on the k-th subcarrier during the n-th signaling interval. In the case of multiple transmitters, a different data stream, (n), t = 1...., was used to modulate each of the transmitted signals [24]. In [25] a MIMO-OFDM transmission with spatial multiplexing on transmitters, where = 2, 3, or 4. Within each OFDM block, independent bit streams are encoded with a low-density parity- check (LDPC) code [26] separately. The In t e r n a t i o n a l Jo u r n a l o f El e c t r o n i c s & Co m m u n i c a t i o n Te c h n o l o g y 75

coded bits are mapped into information symbols using QPSK. There are OFDM blocks formed from the streams of information sequences and transmitted through transmitters simultaneously. On each transmitter, the zero-padded (ZP) OFDM format was used in [14]. The key transmission parameters for experimental results in this paper are listed in Table I [27]. Table 1: ZP-OFDM Parameters Parameters Values Signal bandwidth B = 4.8828 khz OFDM block duration T = 209.7152 ms Guard interval T g = 25 ms Subcarrier spacing Δ f = 4.8 Hz Number of subcarriers K = 1024 Number of data carriers K d = 672 Number of pilot carriers K p = K/4 = 256 Number of null subcarriers K n = 96 With QPSK modulation and parallel data streams from four transmitters, the uncoded data rate is = (4 4 )/ (T + (3) = 44.73Kbps Over the 12 khz bandwidth, where is the number of data carrier, T is the OFDM block duration, and is the guard time. For coding, we use a 16-state rate 1/2 convolutional code with the generator polynomial (23, 35), and a rate 1/2 regular LDPC cycle code over Galois Field GF (64) with (n, k) = (1344, 672) bits [26]. With rate 1/2 coding, the overall data rate is =.5 = 22.36 Kbps (4) For each OFDM block, we generate independent bit streams each of length log 2 M and encode them separately using the nonbinary low-density-parity-check (LDPC) codes [20]. Each coded bit stream of length log 2 M is mapped into a symbol sequence of length. A total of OFDM blocks are formed with each block carrying symbols from one symbol sequence. After proper pilot insertions, the OFDM blocks are transmitted from transmitters simultaneously. In [27], Accounting for all the overheads due to guard interval, channel coding, pilot, and null subcarriers, the overall spectral efficiency in terms of bits per second per Hz (bits/s/hz) is: α = * T / (T + ) * /K * * log 2 M. (5) With a bandwidth B, the data rate is R = αb bits per second. A. Doppler Scaling Factor Estimation Coarse estimation of the Doppler scaling factor is based on the preamble and the postamble of a data packet. (This idea was used in, e.g., [29] for single-carrier transmissions.) The packet structure, containing OFDM blocks, is shown in Fig. 2. Fig. 2: Packet structure By cross correlating the received signal with the known preamble and postamble, the receiver estimates the time duration of a packet. The time duration of this packet at the transmitter side is. By comparing with, the receiver infers how the received signal has been compressed or dilated by the channel. = (7) a = - 1 (8) The receiver then resamples the packet with a resampling factor b=a used in eq.7 of [14]. Resampling has two effects: 1) it rescales the waveform and 2) it introduces a frequencydependent Doppler compensation. In [14] used the polyphaseinterpolation-based resampling method available in Matlab. For each transmitted packets, the above algorithm was used to estimate the Doppler scaling factor. Based on each Doppler scaling factor, the relative speed between the transmitter and the receiver was estimated as v=a *c, using a nominal sound speed of c=1500 m/s. The relative speed and the resulting Doppler shift at the carrier frequency a fc are shown in Fig. 3, which summarizes the results for element 1. Fig. 3 illustrate that the Doppler shifts are much larger than the OFDM subcarrier spacing. For example, if v=8.30 kn (packet 15), which indicates that Mytilus was moving toward Tioga at such a speed, the Doppler shift is 76.98 Hz fc at 27 khz, while the subcarrier spacing is only f =23.44, 11.72, and 5.86 Hz for k=512, 1024, and 2048, respectively. Hence, rescaling the waveform (even coarsely) is necessary to mitigate the Doppler Effect nonuniformly in the frequency domain [14]. IV. Receiver Algorithm The receiver algorithm used for data detection in the SPACE'08 experiment is the one explained in [27] and an overview is presented here commenting also the particularities of the MIMO channel. As a result of sending the signal through a channel, the received signal can be expressed in the frequency domain after the FFT demodulation as (n)= (n) (6) Where t, r, k, n refer to the transmitter, the receiver, the frequency index and the time respectively is referring to the channel frequency response and n to the noise component of the received signal [16]. 76 International Journal of Electronics & Communication Technology Fig. 3: Coarse estimation of the relative speed and the Doppler shift at fc = 27 khz for element 1. B. Carrier Frequency Offsets (CFO) Estimation The Doppler Effect can be viewed as caused by carrier frequency offsets (CFO) among the transmitters and the receivers. The

Doppler Effect can be viewed as caused by carrier frequency offsets (CFO) among the transmitters and the receivers [9], [10]. On each receiver, assume a common CFO relative to all transmitters, as in [30, Chapter 11.5]. Hence, the CFO estimation algorithm in Section IV.B of [31] is directly applicable, where the energy on the null subcarriers is used as the objective function to search for the best CFO estimate. If the receiver compensates the data samples with the correct CFO, the null subcarriers will not see the ICI spilled over from neighboring data subcarriers [14]. After Doppler shift estimation and compensation, the average energy on the null subcarriers is used to compute the variance of the additive noise and residual inter-carrier interference (ICI). on the receiver will recover the original, useful data. Note that both the space time processor and space time decoding require channel state information [34]. Space-time trellis codes are introduced in 1998 [35]. These codes are described by a trellis structure; an example is shown in Fig. 5. The incoming symbol stream is first encoded using the trellis structure and the encoded stream is then distributed among the transmit antennas. The trellis is designed to provide the full spatial diversity advantage, ; The example code shown in Fig. 5 is designed for 2 transmit antennas using BPSK symbols and has 4 trellis states [36]. C. Pilot-Tone-Based Channel Estimation After CFO compensation, pilot tones are used for channel estimation, the ICI induced by CFO is greatly reduced. Due to assumption A2 of [14], we will not consider the ICI because of channel variations within each OFDM block. Note that ICI analysis and suppression in the presence of fast-varying channels have been treated extensively in the literature; see, e.g., the references listed in [32, Ch. 19]. Ignoring ICI, the signal in the mth sub channel can be represented as in [33]. = H (m) d[m] + (9) Where H (m) = C ( ) is the channel frequency response at the mth subcarrier and is the additive noise. On a multipath channel, the coefficient H (m) can be related to the equivalent discrete-time baseband channel parameterized by L+1 complexvalued coefficients through H (m) = (n) (10) To estimate the channel frequency response, we use pilot tones at subcarrier indices ; i.e., are known to the receiver [14]. Fig. 4: Channel estimates from one OFDM block with four transmitters Fig. 4 shows the channel estimates in one OFDM block with four transmitters. The channel delay spreads are about 5ms. Note that the channel corresponding to the first data stream has lower energy than others. This is a general trend for all the received blocks, and is attributed to the implementation differences discussed earlier [27]. V. Space Time Coding The Space Time Coding is a techniques used to improve reliability in a MIMO link. Redundancy is introduced in the transmitters with the hope that forward error correction (FEC) Fig. 5: four states STTC, BPSK, 1 b/s/hz Layered space-time codes were introduced in [37], [38], for rich scattering, flat Rayleigh fading environments. Unlike STTC, which tries to exploit the full spatial diversity of the system, these codes aim at achieving the very high spectral efficiencies possible in a system with a large number of transmit antennas. At the transmitter, the incoming bit stream is spatially multiplexed across the transmit antennas, whereby each substream, so formed is independently encoded, interleaved and mapped into symbols before being transmitted over the channel as shown in Fig. 6. Since independent streams are transmitted from each transmit antenna, the system s spectral efficiency grows linearly with the number of transmit antennas. At the receiver, each receive antenna observes a superposition of all the streams corrupted by Additive white Gaussian noise (AWGN). For a flat fading channel, each stream can be successively decoded by using layered successive interference cancellation and nulling techniques [37]. The general structure of the considered system is depicted in Fig. 6. The data bits d[i] are fed into the space time encoder that outputs L vectors x[k] = {x 1 [k].. [K]} T of length NT. They are transmitted over a MIMO channel. The channel coefficients [k] = are assumed to be constant during one encoded frame so that the received signal becomes [39]. Fig. 6: Structure of transmit diversity system with antennas receive In t e r n a t i o n a l Jo u r n a l o f El e c t r o n i c s & Co m m u n i c a t i o n Te c h n o l o g y 77

Y[k] = H X[k] + N[k] (11) Combining all L vectors x[k], y[k], and n[k] within one coded frame as column vectors into the matrices X, Y, and N, respectively, results in Y = H X + N (12) VI. MIMO-OFDM System A MIMO-OFDM system with four transmits and receives antennas is shown in Fig. 7. Fig. 7: MIMO-OFDM system Though the fig. 6 shows MIMO-OFDM with four transmit antennas, the techniques developed in this paper can be directly applied to OFDM systems with any number of transmit antennas [10]. At time, each of two data blocks is transformed into two different signals, through two space time encoders. When used with space-time coding (coding across both spatial and temporal axes), reliability can be further enhanced through the use of iterative equalization and decoding methods, similar to those used in the single-input/single-output (SISO). Furthermore, the sub-carriers can easily be generated at the transmitter and recorded at the receiver using highly efficient digital signal processing schemes based on Fast Fourier Transform (FFT). The main attraction of OFDM lies in its simplicity of implementation via FFT modulation/demodulation, for implementation in the next generation of acoustic transceivers. VII. Conclusion Efficient use of acoustic bandwidth implies the need for a large number of carriers in an OFDM system, and multiple transmitters to support spatial multiplexing of data streams. In this paper, we have seen that MIMO-OFDM with four transmitters over a limited bandwidth has overall data rates 22.36 Kbps with QPSK modulation. Good performance can be achieved even when the transmitter and the receiver were moving at a relative speed of up to 10 kn, where the Doppler shifts were greater than the OFDM subcarrier spacing. The algorithm incorporates compensation of the motion-induced non uniform Doppler frequency offset across the wide acoustic signal bandwidth and adaptive MIMO channel estimation which capitalizes on the frequency correlation between adjacent carriers and time correlation between adjacent OFDM blocks. In this low-complexity approach, a single matrix inversion of size (number of transmitters) is required per carrier, and these operations can be performed in parallel for the K carriers. To improve the reliability in MIMO link, Space Time Coding technique has been used. Different space time coding techniques have their own features so the selection of space 78 International Journal of Electronics & Communication Technology time coding technique is also play an important role. Recent research suggest that OFDM is a viable candidate for high-rate transmission over UWA channels. 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[36] Subhadeep Roy, Tolga Duman, Leo Ghazikhanian, Vincent McDonald, John Proakis, James Zeidler, Enhanced Underwater Acoustic Communication Performance Using Space-Time Coding and Processing, MTTS/IEEE TECHNO- OCEAN '04, pp. 26, Nov. 2004. [37] P. W. Wolniansky, G. J. Foschini, G. D. Golden, R. A. Valenzuela, V-BLAST: An architecture for realizing very high data rates over the rich-scattering wireless channel, URSI International Symposium on Signals, Systems, and Electronics, pp. 295 300, 29 Sept. - 2 Oct. 1998. [38] G. J. Foschini, Layered space-time architecture for wireless communication in a fading environment when using multielement antennas, Bell Labs Technical Journal, 1996. [39] Volker Kuhn, Wireless Communications over MIMO Channels, John Wiley and Sons, 2006. Sartajvir Singh Dhillon received his B. Tech degree in Electronic and Communications Engineering from Rayat & Bahra Institute of Engineering & Bio-Technology, Mohali, Punjab, India, in 2009. He is currently Pursuing M.Tech degree in Electronic and Communications Engineering from Rayat Institute of Engineering & Information Technology, S.B.S. Nagar, Punjab, India. Ravneet Kaur received his B.Tech degree in Information Technology from S.U.S.C.E.T., Tangori, Mohali, Punjab, India, in 2009. She is currently working as Asstt. Prof. at R.I.M.T., Mandi Gobindgarh, Punjab, India & also Pursuing Part Time M.Tech degree in Computer Science Engineering from Shaheed Udham Singh Engineering, Mohali Punjab, India Vishakha Sood received her B-Tech degree in Electronics and Communication Engineering from G.H.E.C, Kumarhatti, Shimla, Himachal Pradesh, India, in 2008. She is currently pursuing M-Tech degree from R.I.E.I.T, Railmajra, Punjab, India. In t e r n a t i o n a l Jo u r n a l o f El e c t r o n i c s & Co m m u n i c a t i o n Te c h n o l o g y 79