Low Complexity Iterative Receiver Design for Shallow Water Acoustic Channels
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An adaptive iterative receiver structure for the shallow underwater acoustic channel (UAC) is proposed using a decision feedback equalizer (DFE) and employing bit-interleaved coded modulation with iterative decoding (BICM-ID) in conjunction with adaptive Doppler compensation. Experimental results obtained from a sea trial demonstrate that the proposed receiver not only reduces inherent problem of error propagation in the DFE but also improves its convergence, carrier phase tracking, and Doppler estimation. Furthermore, simulation results are carried out on UAC, modelled by utilizing geometrical modelling of the water column that exhibits Rician statistics and a long multipath spread resulting in severe frequency selective fading and intersymbol interference (ISI). It has been demonstrated that there is a practical limit on the number of feedback taps that can be employed in the DFE and data recovery is possible even in cases where the channel impulse response (CIR) is longer than the span of the DFE. The performance of the proposed receiver is approximately within 1 dB of a similar system employing DFE and turbo code, however, at a significantly reduced computational complexity and memory requirements, making our system attractive for real-time implementation.
KeywordsTurbo Code Channel Impulse Response Extrinsic Information Decision Feedback Equalizer Gray Mapping
The UAC is considered to be one of the most difficult and challenging physical communications media in use today. Unlike in Radio Frequency- (RF-) based communications systems, the electromagnetic waves do not propagate over long distances through the water, and thus, acoustic (pressure) waves are employed in order to carry the information signal through a UAC instead. The acoustic waves propagate at a very low speed of approximately 1500 m/s and the propagation occurs over multiple paths due to reflections from the surface and bottom of the sea. Hence, the UAC is considered and modelled as a highly time varying frequency-selective channel. In practice, the multipath profile of the channel depends on the channel geometry and density of the propagation medium. In the case of vertical channels the multipath spread is very short; however, horizontal channels exhibit a multipath spread of 100 s of symbols. Owing to this long multipath spread, the transmitted signal suffers from ISI that degrades the quality of the received signal which needs to be compensated for before detection. The time varying nature of the multipath also poses the problem of the continuous tracking of receiver parameters required for demodulation. Furthermore, the Doppler effect caused by the relative motion between transmitter and receiver plays an important role due to the wideband nature of the transmitted signal, which results in time expansion or compression of the symbol duration, depending on the direction of motion, and requires compensation in order to establish carrier phase and timing synchronization. The combination of these effects poses many challenges to the realization of robust, high data rate communications. Rapidly moving platforms such as autonomous underwater vehicles (AUVs) present a more serious problem. Compensating for Doppler shifts resulting from relative velocities up to 10 m/s is far beyond the capability of conventional adaptive equalization structures, even with explicit phase tracking loops . These velocities can cause an excessive rate of equalizer tap rotation, and hence, the required convergence rate may lead to instability of the adaptive receiver algorithms.
The introduction of the turbo codes  has opened a new research area, where researchers are aiming to design iterative or turbo receivers. Each processing block in the traditional receiver outputs binary integer values resulting in the reliability information about the output symbols being lost. The performance of the receiver can be greatly improved if each block of the receiver outputs a posteriori probabilities (APP) or log likelihood ratios (LLR) of the symbols, that is, soft outputs. Much work in the design of soft output algorithms was encouraged by the need to provide soft inputs to the next processing stage. For example, a channel equalizer should generate soft outputs so as to increase the efficiency of the soft input channel decoder. The channel decoder then not only provides APPs of the information bits but also provides APPs of the encoded bits. These APPs, known as extrinsic information, can be used after interleaving by the equalizer as prior probabilities, also known as intrinsic information, for the next iteration. This is the fundamental idea behind the turbo or iterative receiver, that is, the exchange of soft information. The performance of the receiver improves as the number of iterations increases between the blocks of the receiver. Interested readers can refer to [3, 4, 5, 6, 7, 8] for detailed information on this subject. The first turbo equalizer of its kind was presented by Douillard et al.  to combat multipath using the soft output Viterbi Algorithm (SOVA), where soft information is exchanged between the equalizer and decoder. A complete maximum a posteriori- (MAP-) based turbo equalizer was proposed by Bauch et al.  where it was shown that for a 5-tap channel exhibiting a deep spectral null, the performance of the receiver after 8 iterations between the MAP equalizer and MAP channel decoder is very close to that of a code on a non-ISI channel; however, this cannot be possible when the channel is unknown to the receiver and possibly time varying. A low complexity iterative equalizer structure using minimum mean square error (MMSE) criterion was proposed by Tuchler et al. [7, 11]. The receiver architectures discussed above assume that perfect channel state information (CSI) is available at the receiver, which in most cases is not practical. Moreover, due to the long delay spreads, the MAP-based turbo equalization is simply impractical and similarly the MMSE-based methods have a computational complexity that is beyond the available resources. Recent sea trial experiments [12, 13] put emphasis on the application of iterative receiver structures for the UAC. In , long-term experimental results were presented in order to look for the correlation between environmental parameters. It was also shown that receiver performance can be improved if actual noise statistics were taken into account. An application of the message passing (MP) algorithm is demonstrated in  in order to perform iterative decoding and estimation of channel model parameters. Another active area of research is bit-interleaved coded modulation with iterative detection (BICM-ID). In fading channels, the performance of an error correcting code depends on the code diversity defined by its minimum Hamming distance. The code diversity in BICM is equal to the smallest number of distinct bits along any error event and this is achieved by bitwise interleaving at the encoder output prior to the symbol mapping. The application of turbo-coded BICM (turbo BICM) was proposed  in conjunction with an adaptive decision feedback equalizer (DFE), where the structure takes advantage of the extrinsic information provided by the turbo decoder. Since the DFE is a nonlinear device, as it utilizes previous symbol decisions to eliminate ISI from the current symbol, an erroneous hard decision will propagate throughout the DFE and degrade the performance when used in conjunction with error correction coding (ECC). Most of the ECC techniques are designed to correct random errors, the DFE on the other hand produces errors which are bursty in nature due to the fact that DFE relies on delay-free hard decisions (before decoding) to cancel the ISI. The use of interleavers can convert the burst errors into random errors, thus, a BICM-based receiver not only reduces the error propagation in the DFE but also reduces error floor introduced by turbo decoding.
The focus of this paper is to provide a robust and low complexity receiver solution for underwater communications. The paper is organized as follows. Section 2 presents the communication system and channel model based on the geometry of the channel. The proposed receiver is explained and compared with an iterative DFE using turbo BICM in Section 3. Section 4 summarizes simulation and experimental results, along with the complexity analysis of both receivers. Finally, conclusions are drawn in Section 5.
2. System Definition
where Re Open image in new window denotes the real part, Open image in new window is the attenuation factor for the l th path, Open image in new window is the delay associated with l th path, Open image in new window is the Doppler shift, where Open image in new window denotes the relative velocity between transmitter and receiver and Open image in new window denotes the speed of sound. The received noiseless baseband signal can be written as
If we let Open image in new window and approximate the channel by its equivalent discrete-time baseband model, where the transmit filter, channel and receiver filter are represented by a linear filter with impulse response
where Open image in new window is number of paths and the complex coefficients Open image in new window are time varying and unknown to the receiver. The equivalent received baseband signal at time Open image in new window can be written as
where Open image in new window is complex additive white Gaussian noise (AWGN) with zero mean and variance Open image in new window in each dimension, that is, the noise samples are independent and identically distributed (i.i.d.) exhibiting a normal probability density function (PDF)
and the angle of arrival of the acoustic ray is given as
In (8) and (9),
Underwater channels are commonly classified as doubly spread channels implying that the received signal is dispersed both in time and frequency. A considerable amount of work has been carried out in the past few years in order to characterize the UAC [16, 17, 18, 19, 20, 21]. Models developed in [18, 19, 20, 21] are derived using the measured data from sea trial experiments and provide a deeper insight of the channel dynamics. There are two sources that cause channel variability: inherent changes in the propagation medium and transmitter and/or receiver motion. Inherent changes range from those that occur on very long-time-scales to those that occur on short-time-scales. While the former does not affect the instantaneous power level of the communication signal, the latter are changes induced by surface waves. This causes displacement of the reflection point, resulting in both scattering of the signal and Doppler spreading due to the changing path length, affecting the signal.
3. Proposed Receiver
where Open image in new window is the Hermitian transpose, Open image in new window and Open image in new window are the feedforward and feedback filters, respectively, and Open image in new window is the vector containing the previous hard symbol decisions. The interpolation filter Open image in new window of the 1st order linear interpolator is recursively updated as
where Open image in new window denotes a complex conjugate operation.
In , a DFE-based receiver was presented that takes advantage of the extrinsic information provided from a turbo decoder, where after a fixed number of turbo-decoding iterations, the new extrinsic information is hard limited and given as feedback to the DFE. The key idea exploited is that as the reliability of the extrinsic information increases with the number of iterations, the quality of symbols fed back into the DFE is improved, which in turn reduces error propagation, a key source of performance degradation associated with a DFE. Another problem associated with the DFE is that there is a practical limit for the number of taps utilized. As we increase the number of taps, a longer training sequence is required for the DFE to converge to its optimum solution. The DFE taps are optimized and updated iteratively using a least mean square error (LMS) algorithm in order to maintain low complexity of implementation.
The soft symbols, Open image in new window , are converted into soft bit estimates and deinterleaved before they are passed to the channel decoder. In the turbo BICM transmitter, the encoder in Figure 1 is a parallel concatenation of two or more convolutional codes followed by a bit-by-bit interleaver and a mapper. Unlike turbo BICM, convolutional BICM requires only one encoder and decoder; therefore, the receiver complexity is greatly reduced. The interleaver permutes the encoder output and consequently burst errors created by error propagation in the DFE are converted into random errors. Due to the bit-interleaver in BICM-ID, the bit-based minimum Hamming distance is maximised, in other words the code diversity equals the smallest number of distinct bits, and hence, BICM-ID will achieve a lower bit error probability in fading channels.
At the receiver, we assume that the equalizer has removed most of the ISI which leads to the soft equalized symbols having a Gaussian distribution. The soft demapper processes equalized complex symbols Open image in new window and the corresponding apriori LLRs Open image in new window of the coded bits and outputs extrinsic LLRs 
where Open image in new window denotes the binary random variable with realizations Open image in new window . Using Bayes' rule and taking the expectation of Open image in new window over Open image in new window where Open image in new window is the set of QPSK symbols and Open image in new window in position Open image in new window with Open image in new window , we can write
The first term Open image in new window is computed according to the channel model assuming a Gaussian distribution
The extrinsic estimates Open image in new window are deinterleaved and applied to the a priori probability (APP) channel decoder. By performing iterative decoding, the extrinsic information about the coded bits from the decoder is fed back and regarded as a priori information, Open image in new window , at the demapper. During the initial demapping step, the apriori LLRs are set to zero.
After the Doppler correction and equalization, the soft estimates Open image in new window are demapped into bit likelihoods using (15)–(18). These bit likelihoods are then deinterleaved and fed to the MAP decoder. The MAP decoder not only provides estimates of the information bits, Open image in new window , but also provides extrinsic LLRs about the coded bits. This extrinsic LLRs are then interleaved and treated as a priori information at the demapper for the next iteration. The proposed BICM-ID-based receiver is different in the sense that the extrinsic information is directly exchanged between channel decoder and demapper. In contrast, the turbo-based BICM utilizes two channel decoders resulting in increased performance and complexity.
where Open image in new window is the quantization operation applied to the apriori information.
These newly formed hard symbols are treated as apriori information for the next iteration and fed back to the DFE as shown in Figure 3 represented by a dashed line. The reliability of these new symbols increases as the number of iteration increases, which helps to reduce error propagation in the DFE. In practice, the quantized output of the DFE is used to calculate the error signal in order to update the equalizer taps. However, in this iterative receiver we utilize the newly formed hard symbols to calculate the error signal, and hence, update both the equalizer taps and interpolating factor as well as phase for the 2nd and consecutive iterations between DFE and channel decoder.
At the Open image in new window th received symbol, the feedforward Open image in new window and feedback Open image in new window equalizer coefficients are adaptively updated using the following recursive equation
where Open image in new window , Open image in new window is a step size parameter, Open image in new window is the error signal, Open image in new window contains Open image in new window input symbols for feedforward filter, and Open image in new window input symbols for feedback filter, where Open image in new window and Open image in new window are the number of feedforward and feedback taps, respectively, and Open image in new window represents the cross-correlation function. Since the exact correlation function is mathematically unavailable, we use the LMS estimate Open image in new window and average out the noise in the estimation through the recursion
In the case of the DFE, if an error is made in the hard decision then the estimate Open image in new window will contain erroneous decisions, which will propagate through the DFE and will cause burst errors. If an interleaver is not used then the Log-MAP decoding algorithm will not be able to correct these long burst errors. However, when the correct decisions on the symbols are fed back in the iterative mode, the estimate Open image in new window will have improved decisions which will in turn reduce error propagation.
4. Performance Analysis
4.1. Simulation Results
In this section, we present extensive simulation results for a given geometry and different scenarios, such as static and dynamic frequency selective channel conditions. For the simulation results, it is assumed that the Doppler shift due to relative motion between Tx and Rx is estimated correctly and the resampling operation does not introduce any significant distortion, which leads to the simplified received signal model of (6).
We are considering only first-order multipath reflections, so by substituting Open image in new window in (10), we can calculate all the path lengths and the delay Open image in new window associated with each path by dividing the path length by the speed of sound Open image in new window . In order to simulate the multipath channel, we have considered the relative delays of the multipath arrivals with respect to the direct path. The resulting total delay spread of this channel is of the order 43.4 milliseconds. The delay spread of each path in terms of symbols can be easily calculated by multiplying the delay of each path by the data rate Open image in new window .
Power delay profile.
Delay spread (s)
Average power (dB)
4.2. Complexity Analysis
4.3. Experimental Results
where Open image in new window is the power associated with path Open image in new window and the total power Open image in new window . The rms delay spread ( Open image in new window ) of the channel can be given as
By using (23) and (24), the observed multipath exhibits Open image in new window of 1.4 ms and Open image in new window of 1.1 ms. The receiver used in the demodulation of the data consisted of a DFE equalizer with 32 feedforward taps ( Open image in new window -spaced) and 10 feedback taps. The first few packets were decoded error free for the 1st iteration itself without employing adaptive Doppler correction. However, many packets resulted in BER of 0.5 because synchronization was lost due to relative motion between transmitter and receiver.
In this paper, we have proposed a receiver structure employing adaptive DFE and BICM-ID in conjunction with an adaptive Doppler compensation technique. The objective of this paper was to investigate the performance of the system when the DFE does not cover the entire span of CIR. The shallow water channel has been simulated based on a given geometry for short range communication, which produces large delay spread and was modelled as a Rician multipath fading channel. Moreover, simulation results were carried out for static and dynamic channels and we compared the proposed DFE-BICM-ID receiver with a more complex system employing a DFE receiver and turbo-BICM. It was shown that the proposed receiver performs approximately within 1 dB of the performance of the DFE-turbo-BICM system. It was also shown that it will be more feasible to implement the DFE-BICM-ID receiver in real time due to its lower memory and complexity requirements. Furthermore, it has also been established that there is an upper and lower limit on the number of feedback taps that can be employed. Experimental results demonstrated that in a highly dynamic channel, the proposed receiver not only reduces intersymbol interference and error propagation in the DFE but also improves SINR by approximately 2 dB. It is also shown that the iterative receiver gives better Doppler estimates, and thus, improving the interpolation and phase tracking. The encouraging results and reduced complexity in implementation make the proposed iterative receiver an attractive solution for a robust high data-rate underwater acoustic modem.
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