Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the conventional channel estimation methods such as least square (LS), minimum mean square error (MMSE) and back propagation neural network (BPNN) cannot be directly applied to UWA-OFDM systems, since complicated multipath channels may cause a serious decline in performance estimation. To address the issue, two types of channel estimators based on deep neural networks (DNNs) are proposed with a novel training strategy in this paper. The proposed DNN models are trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models in the working process. The experimental results demonstrate that the proposed methods outperform LS, BPNN algorithms and are comparable to the MMSE algorithm in respect to bit error rate and normalized mean square error. Meanwhile, there is no requirement of prior statistics information about channel autocorrelation matrix and noise variance for our proposals to estimate channels in UWA-OFDM systems, which is superior to the MMSE algorithm. Our proposed DNN models achieve better performance using 16QAM than 32QAM, 64QAM, furthermore, the specified DNN architectures help improve real-time performance by saving runtime and storage resources for online UWA communications.

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Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems

Estimation of the Parameters of the K-distributed Using Higher Order and Fractional Moments

With the growth and wide variety of available data, advanced processing, and affordable data storage, machine learning is witnessing great attention in finding optimized solutions in various fields. Machine learning techniques are currently taking a major part of the ongoing research, and expected to be the key player in today’s technologies. This paper introduces and investigates the applications of machine learning in antenna design. It covers the major aspects of machine learning, including its basic concept, differentiation with artificial intelligence and deep learning, learning algorithms, its wide applications in various technologies, with a main focus on its usage in antenna design. The review also includes a comparison of the results using machine learning in antenna design, compared to the conventional design methods.

Machine Learning in Antenna Design: An Overview on Machine Learning Concept and Algorithms

Simulation forms an important part of the development and empirical evaluation of underwater acoustic network (UAN) protocols. The key feature of a credible network simulation model is a realistic channel model. A common approach to simulating realistic underwater acoustic (UWA) channels is by using specialised beam tracing software such as BELLHOP. However, BELLHOP and similar modeling software typically require knowledge of ocean acoustics and a substantial programming effort from UAN protocol designers to integrate it into their research. This paper is a distilled tutorial on UWA channel modeling with a focus on network simulation, providing a trade-off between the flexibility of low level channel modeling via beam tracing and the convenience of automated channel modeling, e.g. via the World Ocean Simulation System (WOSS). The tutorial is accompanied by our MATLAB simulation code that interfaces with BELLHOP to produce channel data for UAN simulations. As part of the tutorial, we describe two methods of incorporating such channel data into network simulations, including a case study for each of them: 1) directly importing the data as a look-up table, 2) using the data to create a statistical channel model. The primary aim of this paper is to provide a useful learning resource and modeling tool for UAN protocol researchers. Initial insights into the UAN protocol design and performance provided by the statistical channel modeling approach presented in this paper demonstrate its potential as a powerful modeling tool for future UAN research.

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Channel Modeling for Underwater Acoustic Network Simulation

Due to the characteristics of the underwater acoustic (UWA) channel, the process at the receiver is complicated to match the channel. To simplify receiver design and match UWA channel better, this study proposes a deep neural network-based orthogonal frequency division multiplexing receiver for UWA communication. Different from existing receivers needing a neural network and several other processing parts, the proposed receiver only uses a single neural network to implement the whole signal processing. Moreover, it is a general receiver which is suitable for other modulation schemes. Simulation results show that the proposed receiver offers better bit error rate performance over traditional ones.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-com.2019.0243

Deep neural network-based underwater OFDM receiver

For K-distributed sea clutter, a constant false alarm rate (CFAR) is crucial as a desired property for automatic target detection in an unknown and non-stationary background. In multiple-target scenarios, the target masking effect reduces the detection performance of CFAR detectors evidently. A machine learning based processor, associating the artificial neural network (ANN) and a clustering algorithm of density-based spatial clustering of applications with noise (DBSCAN), namely, DBSCAN-CFAR, is proposed herein to address this issue. ANN is trained with a symmetrical structure to estimate the shape parameter of background clutter, whereas DBSCAN is devoted to excluding interference targets and sea spikes as outliers in the leading and lagging windows that are symmetrical about the cell under test (CUT). Simulation results verified that the ANN-based method provides the optimal parameter estimation results in the range of 0.1 to 30, which facilitates the control of actual false alarm probability. The effectiveness and robustness of DBSCAN-CFAR are also confirmed by the comparisons of conventional CFAR processors in different clutter conditions, comprised of varying target numbers, shape parameters, and false alarm probabilities. Although the proposed ANN-based DBSCAN-CFAR processor incurs more elapsed time, it achieves superior CFAR performance without a prior knowledge on the number and distribution of interference targets.

Robust CFAR Detection for Multiple Targets in K-Distributed Sea Clutter Based on Machine Learning

The influence factors of underwater acoustic propagation are the foundation of underwater acoustic communication. In this paper, a realistic underwater acoustic channel is modeled in the South China Sea. Three kinds of typical sea surface models with curvilinear boundaries are selected for the investigation by the BELLHOP ray model. Under different restrictions, the characteristics of the channel model are analyzed contrastively by changing the curvilinear surface types, sending and receiving depths and seasonal sound speed profiles. The experimental results present that (1) different types of curvilinear surfaces have little effect on the reflection with canopies and multipath effect but affect the mean transmission loss within limited depths, (2) convergent traits of transmission loss are becoming stronger and the influence of the surface is becoming weaker with the increasing of sending and receiving depths, (3) surface models perform differently on multipath effect and transmission loss under the conditions of seasonal sound speed profiles.

Modeling and analyzing of underwater acoustic channels with curvilinear boundaries in shallow ocean

In underwater acoustic-orthogonal frequency division multiplexing (UWA-OFDM) systems, the performance of channel estimation is significantly affected by pilot allocation in the framework of compressed sensing (CS). However, for optimizing the pilot allocation, an exhaustive search method over all possible allocations is computationally prohibitive and random search method may not ensure convergence accuracy. In this paper, the meta-heuristic algorithm of the whale optimization algorithm (WOA) is employed to address this issue. For reinforcing the capability of balancing exploration and exploitation, an enhanced variant of WOA termed EWOA is presented with four optimization strategies. After that, a joint algorithm combining CS with EWOA (CS-EWOA) is proposed for pilot allocation in UWA-OFDM systems. Through extensive simulations, the improvement of EWOA is demonstrated on the majority of benchmark functions over other well-known meta-heuristic algorithms. With regard to bit error rate (BER) and mean square error (MSE) for channel estimation, the proposed CS-EWOA algorithm outperforms the equispaced, random, genetic algorithm (GA), particle swarm optimization (PSO), and WOA-based pilot allocation methods. Moreover, it is robust with varying system subcarriers and channel models. Furthermore, the CS-EWOA exhibits superior convergence performance without increasing the computational complexity compared with the GA-, PSO-, and WOA-based methods in the iteration process of pilot allocation optimization. It can be concluded from the simulation results that the proposed CS-EWOA algorithm is competitive to optimize pilot allocation for channel estimation in UWA-OFDM systems.

/pdf/joint-compressed-sensing-and-enhanced-whale-optimization-1gketufpff.pdf

Joint Compressed Sensing and Enhanced Whale Optimization Algorithm for Pilot Allocation in Underwater Acoustic OFDM Systems

Multiple Signal Classification (MUSIC) is a high-performance Direction of Arrival (DOA) estimation algorithm, which has been widely used. The algorithm needs to calculate the covariance matrix, eigenvalue decomposition and spectral peak search. In the paper, the hardware structure of the existing Jacobi algorithm for Hermitian matrices is proposed. On this basis, a novel hardware acceleration of the MUSIC algorithm for sparse arrays and uniform linear arrays is proposed, and the sparse array is a nested array. There are two designs, Design 1 supports 1~10 nested array elements or 1~32 uniform linear array elements, distinguishes 1~32 sources, configures snapshots 1~2048, and the maximum number of iterations and iteration accuracy of the complex Jacobi algorithm. Design 2 only needs $101.8~\mu $ s to complete a DOA estimation when the number of array elements is 8, the number of sources is 1, and the snapshots is 128. In more detail, the Root Mean Squared Error (RMSE) of both can reach 0.03°. The logic resources on the Zynq-7000 development board are 14,761 and 28,305 Look-Up Tables (LUTs), respectively.

Rongkun Jiang

Papers

Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems

Robust CFAR Detection for Multiple Targets in K-Distributed Sea Clutter Based on Machine Learning

Modeling and analyzing of underwater acoustic channels with curvilinear boundaries in shallow ocean

Joint Compressed Sensing and Enhanced Whale Optimization Algorithm for Pilot Allocation in Underwater Acoustic OFDM Systems

Hardware Acceleration of MUSIC Algorithm for Sparse Arrays and Uniform Linear Arrays