Comparing RLS and LMS Adaptive Equalizers for Large Hydrophone Arrays in Underwater Acoustic Communication Channels

The fast temporal variations, long multipath delay spreads, and severe frequency-dependent attenuations of underwater acoustic communication channels are extremely complex that impedes underwater acoustic (UWA) data transmission. To alleviate this problem, channel coding is indispensable in UWA communication system to increase the reliability. In this paper, performances of LDPC codes with different parameters over different underwater acoustic communication channels are presented, which is to know how to adjust the encoding and decoding parameters according to different underwater acoustic channels. The results indicate that LDPC codes have a good performance of the estimated BER on the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> -10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> either in positive sound velocity gradient (PSVG) underwater channel or negative sound velocity gradient (NSVG) one, even invariable sound velocity gradient (ISVG) one. In addition, whatever the encoding or decoding parameters is adopted, performance of the LDPC codes over the NSVG underwater channel is the best, compared with PSVG one and ISVG one. Furthermore, in the desired performance of BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> , the encoding and decoding parameters according to the three different underwater channels specifications are summarized.

This paper proposes a selection method to choose a better modulation scheme in a given underwater acoustic communication channel. In this paper, parameters affecting the BER of CDMA and OFDM were calculated from underwater acoustic channels. Deep Neural Network (DNN) with the parameters was utilized to select the better modulation method given an underwater acoustic channel environment. Computer simulations demonstrate the proposed method selects the better modulation scheme by 98.98%.

Under water acoustic (UAC) channel is subjected to large delay spread compared to radio frequency (RF) channels. In order to capture this large delay spread and avoid ISI, the UAC symbol duration needs to be considerably large reducing the transmission data rate. Unlike other RF systems, in Ultra-wideband (UWB) RF systems, small pulse width in the order of nano seconds is used to transmit information. This in turn, gives high resolution multipaths. In a time hopping UWB system, to avoid multipath overlap, the chip duration and the frame duration are kept equal to or larger than the maximum delay spread of the channel. This results in large UWB symbol duration and hence reduces the data rate. In order to increase the data rate, the chip duration, and frame duration can be reduced to less than the maximum delay spread. In addition, to farther increase the data rate, guard interval between consecutive symbols can be reduced/removed allowing consecutive symbols to overlap. This results in intersymbol interference (ISI). Surprisingly, this type of overlap doesn't degrade UWB system performance significantly. In this paper, the same concept is applied to various UAC channels. In order to increase the data rate, consecutive UAC symbols are allowd to overlap. Performance under ISI is then evaluated.

Based on experimental data from the deep-sea sound channel axis in the Western Pacific, the statistical distribution law of cluster structure and channel delay spread characteristics are analyzed for three typical receiving depths near the sound channel axis in this paper. A ray theory-based underwater acoustic channel model is used to explain the variations in channel parameters over time and the receiving depth. The results indicate that the underwater acoustic communication channel at the channel axis depth over a 20-km range exhibits a clustered structure that depends on the emission angles of sound rays. For the amplitude characteristics, the amplitude of each cluster follows an inverse Gaussian distribution, with the maximum average amplitude observed when the receiver and transmitter depths are similar. The amplitude of each cluster fluctuation decreases as the receiving depth increases. Regarding delay spread characteristics, the delay spread of each cluster, as well as the maximum and root mean square delay spread of the channel, conform to a Gaussian mixture distribution. The mean and fluctuation of the delay spread parameters increase with the receiving depth. Variations in the cluster structure and channel delay spread characteristics above are primarily attributed to the time-varying sound speed along the propagation paths of sound rays emitted at small upward angles.

To fully exploit sea experiments under controlled and reproducible laboratory conditions, a channel model driven by real data is derived. This model relies on the assumption that a channel recorded at sea is a single observation of an underlying random process. From this single observation, the channel statistical properties are estimated to then feed a stochastic simulator that generates multiple realizations of the underlying process. Based on the analysis of data collected in the Atlantic Ocean and the Mediterranean Sea, we fully relax the usual wide-sense stationary uncorrelated scattering (WSSUS) assumption. We show thanks to the empirical mode decomposition that a trend stationary model suits the analyzed underwater acoustic communication channels very well. Scatterers with different path delays are also assumed to be potentially correlated so that the true second-order statistics of the channel are taken into account by our model. Test cases illustrate the benefits of channel stochastic replay to communication system design and validation. The Matlab code corresponding to the proposed simulator is available at http://perso.telecom- bretagne.eu/fxsocheleau/software .

Underwater acoustic communication is paid more and more attention with the development of marine researches in recent years. Underwater acoustic communication channel is considered to be one of the most complicated channels, it suffers from the less available bandwidth, the more serious multipath effect and the even complex noise. Due to the severe distortion of the received signals, the underwater acoustic communication always experiences a high Bit Error Rate (BER). To solve this problem, Carrier Waveform Inter-Displacement (CWID) modulation was proved to be an effective method to decrease BER in our previous work. The Linear Frequency Modulation (LFM) carrier-waves are used in CWID modulation. The frequency band of LFM carrier-waves has an important effect on the communication performance. The Particle Swarm Optimization (PSO) is introduced to search the optimal frequency band of the LFM carrier-waves in this paper, due to its excellent performance in solving complicated optimization problems. The multiple objective optimization nature of the PSO gives a clear hint to deduce the relationship of upper band and lower band of the LFM carrier-waves. Simulations verify the improved performance and effectiveness of the optimization method.

In this paper, we develop a novel statistical detection algorithm following similar principles to that of expectation maximization (EM) algorithm. Our goal is to develop an iterative algorithm for joint channel estimation and data detection in channels that have a long memory and are fast varying in time. At each iteration, starting with an estimate of the channel, we combine a Markov Chain Monte Carlo (MCMC) algorithm for data detection, and an adaptive algorithm for channel tracking, to develop a statistical search procedure that finds joint important samples of possible transmitted data and channel impulse responses. The result of this step, which may be thought as E-step of the proposed algorithm, is used in an M-step that refines the channel estimate, for the next iteration. Excellent behavior of the proposed algorithm is presented by examining it on real data from underwater acoustic communication channels

Channel shortening equalizers are used in acoustics to reduce reverberation, in error control decoding to reduce complexity, and in communication receivers to reduce inter-symbol interference. The cascade of a channel and channel shortening equalizer ideally produces an overall impulse response that has most of its energy compacted into fewer adjacent samples. Once designed, channel shortening equalizers filter the received signal on a per-sample basis and need to be adapted or re-designed if the channel impulse response changes significantly. In this paper, we evaluate sparse filters as channel shortening equalizers. Unlike conventional dense filters, sparse filters have a small number of non-contiguous non-zero coefficients. Our contributions include (1) proposing optimal and sub-optimal low complexity algorithms for sparse shortening filter design, and (2) evaluating impulse response energy compaction vs. design and implementation stage computational complexity tradeoffs for the proposed algorithms. We apply the proposed equalizer design procedures to (1) asymmetric digital subscriber line channels and (2) underwater acoustic communication channels. Our simulation results utilize measured channel impulse responses and show that sparse filters are able to achieve the same channel energy compaction with half as many coefficients as dense filters.

Angle of arrival (AoA) analysis is essential for the statistical characterization of the underwater (UW) channel where the acoustic signal propagation is confined to a restricted geometry. In UW channels, the acoustic signal reaches the receiver either through the line of sight path or through scattering from the sea surface and bottom. Low-frequency acoustic signals (few Hz) undergo reflection from the sea surface and bottom. This is called coherent scattering, where the scattered paths are assumed to be in phase and concentrated around the specular paths. At relatively higher frequencies (above 10 kHz), incoherent scattering occurs, resulting in an omnidirectional scattering pattern at each point of interaction with the sea surface. In this work, we derive a closed-form expression for the AoA distribution for a single bounce (SB) path that undergoes incoherent scattering from the surface/bottom in an UW channel. The analysis is extended to the case of paths involving multiple bounces (MB). The AoA distribution is further used to evaluate the Doppler spread in an UW channel.

Chapter 1 - Introduction

Underwater acoustic communication channels are characterized by a large variety of propagation conditions. Designing a reliable communication system requires knowledge of the transmission parameters of the channel, namely multipath delay spread, Doppler spread, coherence time, and coherence bandwidth. However, the possibilities of its estimation in a real-time underwater communication system are limited, mainly due to the computational time of implementations of the digital signal processing algorithms. The article presents the results of analysis of probe signals measured during a shallow water experiment. The implementation issues of estimation algorithms are discussed, assuming floating point arithmetic, and with both single-core and multi-core digital signal processors as the target platforms.

Underwater acoustic communication (UAC) channel is a typical doubly spread channel with large multipath delay spread and large Doppler spread. The large multipath delay spread will lead the severe inter symbol interference (ISI) in the single carrier communication systems. The usual ISI will affect as much as hundreds of symbols, which will need complex equalization. OFDM can effectively reduce the ISI with guard interval. However due to the slow speed of acoustic propagation (about 1500m/s), the slow movement of underwater sensor nodes caused by ocean current will lead large Doppler spread, which will lead severe inter carrier interference (ICI). Channel estimation in this doubly spread channel will encounter the problem that the channel state information of pilot is affected by the neighboring unknown data symbols, which will largely degrade the performance of OFDM system. The paper studies the iterative OFDM receiver to reduce the performance degradation caused by the ICI.

Communication over doubly selective channels (both time and frequency selective) suffers from significant intercarrier interference (ICI). This problem is severe in underwater acoustic communications. In this paper, a novel partial fractional (PFrFT)-MIMO-OFDM system is proposed and implemented to further mitigate ICI. A new iterative band minimum mean square error (BMMSE) weight combining based on LDLH factorization is used in a scenario of perfect knowledge of channel information. The proposed method is extended from SISO-OFDM configuration to MIMO-OFDM. Simulation results demonstrate that the proposed PFrFT-LDLH outperforms the other methods in the SISO-OFDM scenario and that its performance can be improved in MIMO-OFDM scenarios.

Traditional methods in underwater acoustic communication (UWAC) often struggle to overcome signal degradation and noise interference. This paper explores the application of low-density parity-check (LDPC) codes for improved image transmission in UWAC. The proposed work demonstrates the efficacy of a rate six by seven and a rate five by six LDPC codes in the UWAC channel to compensate the variable nature of noises in UWAC.

The paper presents a brief survey on the underwater acoustic communication channel estimation of orthogonal frequency division multiplexing using deep learning techniques. In recent times, deep learning has shown great potential in the field of communication. The underwater acoustic channel is a highly challenging system as it has a restricted bandwidth, enlarged multi-path, medium refractive characteristics, acute fading, rapid time variation, large Doppler shifts, etc. For the UWA-OFDM communication model, DL-based receivers can be elucidated as a deep neural network for a better estimation than the conventional techniques. Sufficient training is required for the recovery of the transmitted symbols. It does not require any explicit CE or equalization and balancing. The estimation is accomplished in two stages. The first one is training stage. For training, this deep neural network-transmitted data, the signal received in the unknown channel, etc., are used as labeled data. The second one is test stage, where the deep neural network receiver recovers transmitted symbols, given the received signal. The accuracy and the computational complexity, these two have been a great challenge for the underwater acoustic channel estimation with the existing traditional channel estimation methods. The deep learning method is useful to solve these problems. This survey is to find out a few optimum methods of deep learning to estimate the UWA channel in an efficient way.