Underwater Acoustic Orthogonal Frequency Division Research Articles

Neural-Network-Based Equalization and Detection for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communications: A Low-Complexity Approach

The performance of the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is often restrained by time-varying channels with large delays. The existing frequency domain equalizers do not work well because of the high complexity and difficulty of finding the real-time signal-to-noise ratio. To solve these problems, we propose a low-complexity neural network (NN)-based scheme for joint equalization and detection. A simple NN structure is built to yield the detected symbols with the joint input of the segmented channel response and received symbol. The coherence bandwidth is investigated to find the optimal hyperparameters. By being completely trained offline with real channels, the proposed detector is applied independently in both simulations and sea trials. The results show that the proposed detector outperforms the ZF and MMSE equalizers and extreme learning machine (ELM)-based detectors in both the strongly reflected channels of the pool and time-variant channels of the shallow sea. The complexity of the proposed network is lower than the MMSE and ELM-based receiver.

Long Short-Term Memory Neural Network assisted Peak to Average Power Ratio Reduction for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communication

Long Short-Term Memory Neural Network assisted Peak to Average Power Ratio Reduction for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communication

Data augmentation aided complex-valued network for channel estimation in underwater acoustic orthogonal frequency division multiplexing system.

In this paper, a data augmentation aided complex-valued network is proposed for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) channel estimations, wherein empirical mode decomposition based data augmentation is proposed to solve the current dilemma in the deep learning embedded UWA-OFDM communications: data scarcity and data-sampling difficulties in real-world applications. In addition, the significance of high-frequency component augmentation for the UWA channel and how it positively influences the following model training are discussed in detail and demonstrated experimentally in this paper. In addition, the complex-valued network is specially designed for the complex-formatted UWA-OFDM signal, which can fully utilize the relationship between its real and imaginary parts with half of the spatial resources of its real-valued counterparts. The experiments with the at-sea-measured WATERMARK dataset indicate that the proposed method can perform a near-optimal channel estimation, and its low resource requirements (on dataset and model) make it more adaptable to real-world UWA applications.

Long short-term memory neural network assisted peak to average power ratio reduction for underwater acoustic orthogonal frequency division multiplexing communication

The underwater acoustic wireless communication networks are generally formed by the different autonomous underwater acoustic vehicles, and transceivers are interconnected to the bottom of the ocean with battery deployed modems. Orthogonal frequency division multiplexing (OFDM) has become the most popular modulation technique in underwater acoustic communication due to its high data transmission and robustness over other modulation techniques. To maintain the operability of underwater acoustic communication networks, the power consumption of battery-operated transceivers becomes a vital necessity to be minimized. The OFDM technology has a major lack of peak to average power ratio (PAPR), resulting in more power consumption, creating non-linear distortion, and increasing the bit error rate (BER). To overcome this situation, we have contributed our research into three dimensions, firstly, we propose a machine learning-based underwater acoustic communication system through long short-term memory neural network (LSTM-NN). Secondly, the proposed LSTM-NN reduces the PAPR and makes the system reliable and efficient, turning into a better BER performance. Finally, the simulation and water tank experimental data results are executed which proves that the LSTM-NN is the best solution for mitigating the PAPR with non-linear distortion and complexity in the overall communication system

Performance Analysis of Selective Mapping in Underwater Acoustic Orthogonal Frequency Division Multiplexing Communication System

Under-Water Acoustic (UWA) communication networks are commonly formed by associating various independent UWA vehicles and transceivers connected to the bottom of the sea with battery-operated power modems. Orthogonal Frequency Division Multiplexing (OFDM) is one of the most vital innovations for UWA communications, having improved data rates and the ability to transform fading channels into flat fading. Moreover, OFDM is more robust on Inter-Symbol and Inter-Carrier Interferences (ISI and ICI respectively). However, OFDM technology suffers from a high Peak to Average Power Ratio (PAPR), resulting in nonlinear distortions and higher Bit Error Rates (BERs). Saving power of battery deployed modems is an important necessity for sustainable underwater communications. This paper studies PAPR in UWA OFDM communications, employing Selective Mapping (SLM) as a tool to mitigate PAPR. The proposed SLM with the oversampling factor method proves to be less complex and more efficient. Simulation results indicate that SLM is a promising PAPR reduction method for UWA OFDM communications reducing BER.

Snapping shrimp noise detection and mitigation for underwater acoustic orthogonal frequency division multiple communication using multilayer frequency

This paper proposes Snapping Shrimp Noise (SSN) detection and corrupted Orthogonal Frequency Division Multiplexing (OFDM) reconstruction methods to increase Bit Error Rate (BER) performance when OFDM transmitted signal is corrupted by impulsive SSNs in underwater acoustic communications. The proposed detection method utilizes multilayer wavelet packet decomposition for detecting impulsive and irregularly concentrated and SSN energy in specific frequency bands of SSN, and the proposed reconstruction scheme uses iterative decision directed-subcarrier reconstruction to recover corrupted OFDM signals using multiple carrier characteristics. Computer simulations were executed to show receiver operating characteristics curve for the detection performance and BER for the reconstruction. The practical ocean experiment of SAVEX 15 demonstrated that the proposed method exhibits a better detection performance compared with conventional detection method and improves BER by 250% and 1230% for uncoded and coded data, respectively, compared with the conventional reconstruction scheme.

Analysis of SNR Metrics for a Typical Underwater Acoustic OFDM System

Signal to noise ratios (SNRs) are universal metrics for the performance analysis of communication systems. In underwater acoustic orthogonal frequency division multiplexing (OFDM) systems, compared with the time domain input SNR (ISNR), the pilot SNR (PSNR) and effective SNR (ESNR) are demonstrated to be more effective in system performance indication, especially the ESNR. In this paper, for a typical underwater acoustic OFDM system with orthogonal matching pursuit (OMP) based sparse channel estimation, we derive formulas with a closed form for evaluating the ESNR, which avoids the requirement of successful data decoding in conventional ESNR calculation. The derivation is also extended to PSNR easily. We find out that the ESNR is mainly determined by five parameters: the number of deterministic pilots, the number of dominant paths, the inter-carrier interference (ICI), the total channel power and the ISNR; while the PSNR is only determined by the ICI, the total channel power and the ISNR. Simulations and experimental data decoding are carried out to validate the correctness of the theoretical analysis in this paper.

Parallel Iterative Inter-carrier Interference Cancellation in Underwater Acoustic Orthogonal Frequency Division Multiplexing

In underwater acoustic orthogonal frequency division multiplexing (OFDM) communication system, channel variations would introduce inter-carrier interference (ICI). This gets more severe as mobile speed, carrier frequency or OFDM symbol duration increases. In this paper, we propose a kind of parallel iterative ICI cancellation algorithm to combat that problem. The proposed method extracts channel variations from the adjacent OFDM symbols, and constantly improves the estimation accuracy of ICI through iteration. Finally, simulation and trial in real lake channel verify the availability and stability of the algorithm in the underwater acoustic time varying channel. In addition, this algorithm avoids the matrix inversion, reducing the complexity of the algorithm, and has the obvious advantage in accuracy and speed.

Snapping shrimp noise mitigation based on statistical detection in underwater acoustic orthogonal frequency division multiplexing systems

We propose a mitigation scheme for snapping shrimp noise when it corrupts an orthogonal frequency division multiplexing (OFDM) signal in underwater acoustic communication systems. The OFDM signal distorted by the snapping shrimp noise is filtered by a band-stop filter. The snapping shrimp noises in the filtered signal are detected by a detector with a constant false alarm rate whose threshold is derived theoretically from the statistics of the background noise. The detected signals are reconstructed by a simple reconstruction method. The proposed scheme has a higher detection capability and a lower mean square error of the channel estimation for simulated data and a lower bit error rate for practical ocean OFDM data collected in northern East China Sea than the conventional noise-mitigating methods.

Decision fractional fast Fourier transform Doppler compensation in underwater acoustic orthogonal frequency division multiplexing.

A method of compensating for Doppler distortions of underwater acoustic channels for orthogonal frequency division multiplexing (OFDM) signals is proposed. This method is based on symbol rebuilding. OFDM symbol rebuilding is performed after fractional fast Fourier transform (F-FFT) processing. A stochastic gradient-type adaptive algorithm is designed to learn the combiner weights for detection. Synthetic data and experimental data from a recent mobile acoustic communication experiment are used to demonstrate the performance of the proposed method, which represents a significant improvement over the F-FFT detection technique.

Sparse channel estimation and pilot optimization for underwater acoustic orthogonal frequency division multiple access uplink communications

Considering that the conventional channel interpolation method with sparse and irregular spaced pilots will lead to an error floor in underwater acoustic (UWA) orthogonal frequency division multiple access (OFDMA) uplink communications, a method for sparse channel estimation and pilot optimization is proposed in this paper. A compressed sensing (CS) algorithm is utilized for sparse channel impulse response estimation, which performs well in sparse and irregular spaced pilots and significantly decreases the channel estimation error. Besides, the pilots’ pattern and power joint optimization algorithm based on the random search technique is proposed for the minimum mutual coherence criterion in CS theory, which further improves the performance of CS estimation algorithm. During each iteration step, we randomly pick a pilots’ pattern from the subcarrier index set and a pilots’ power subset from the available power set. Then we perform this step iteratively within a certain searching time. Finally, the local optimal solution of the objective function for minimizing mutual coherence is considered as the feasible pilots’ pattern and power. Simulation results show that the convergence performance of the pilots’ pattern and power joint optimization algorithm is much better than that of the pilots’ pattern optimization algorithm. Furthermore, the channel estimation error of the proposed method is much lower than that of conventional least-squares channel estimator based on linear interpolation, CS channel estimator without pilot optimization, and CS channel estimator merely with pilots’ pattern optimization in channels of different multipath delay spreads. Finally, performance of the proposed method is demonstrated in the UWA uplink OFDMA systems with interleaved and generalized carrier assignment schemes respectively in the two-user case in a pool experiment. Experimental results show that the proposed method decreases dramatically the bit error rate in both carrier assignment schemes, and simultaneous reception for two users is achieved when signal noise ratio is larger than 10 dB.

Sparse channel estimation of underwater acoustic orthogonal frequency division multiplexing based on basis pursuit denoising

To solve the problem of poor performance of the traditional l2-norm channel estimation, a sparse channel estimation approach based on basis pursuit denoising (BPDN) is proposed in orthogonal frequency division multiplex underwater acoustic communication. Owing to the sparsity of the underwater acoustic channel, only a few observations are needed to recover the channel impulse response with a high accuracy. Compared with greedy pursuit algorithm, BPDN algorithm has the globally excellentest solution. The signal is estimated based on the l2-l1 norm rule and the observations containing the noise are considered. The regularization parameter can be changed to balance the signal's sparsity against the residual error. The influences of the pilot distribution and the regularization parameter on the BPDN algorithm are discussed in the simulation. The BPDN channel estimator is compared with the least square (LS) and also with orthogonal matching pursuit (OMP). The data collected from lake experiment show that the BPDN channel estimator outperforms the LS and OMP channel estimator over spare underwater acoustic channel.

A simple and effective noise whitening method for underwater acoustic orthogonal frequency division multiplexing

Underwater acoustic orthogonal frequency division multiplexing (OFDM) enables simple frequency domain equalization, but its performance is often limited by intercarrier interference (ICI) that is induced by channel variation, in addition to the ambient noise. As the signal itself, the variance of the ICI is frequency dependent as (i) the transmitter often has a nonideal transmit voltage response (TVR) and (ii) underwater acoustic propagation introduces frequency dependent attenuation. In this paper, a simple method to account for the frequency dependent spectrum of the ICI plus noise is proposed. Specifically, the power spectrum of the ICI plus noise is approximated using a low-order polynomial in the log domain, by fitting the measurements on the null subcarriers embedded in each OFDM symbol. Prewhitening is then applied to each OFDM symbol before channel estimation and data demodulation. The proposed method is tested using experimental data collected from the SPACE08 and RACE08 experiments, where signals with and without transmitter precompensation are compared side by side in the former. Impressive performance gains are found whenever the signal is significantly colored. This is the case when either the TVR is not compensated or the transmission distance and bandwidth are large.

Sparse underwater acoustic OFDM channel estimation based on superimposed training

A superimposed training (ST) based channel estimation method is presented that provides accurate estimation of a sparse underwater acoustic orthogonal frequency-division multiplexing (OFDM) channel while improving bandwidth transmission efficiency. A periodic low power training sequence is superimposed on the information sequence at the transmitter. The channel parameters can be estimated without consuming any extra system bandwidth, but an unknown information sequence can interfere with the ST channel estimation method, so in this paper, an iterative method was adopted to improve estimation performance. An underwater acoustic channel’s properties include large channel dimensions and a sparse structure, so a matching pursuit (MP) algorithm was used to estimate the nonzero taps, allowing the performance loss caused by additive white Gaussian noise (AWGN) to be reduced. The results of computer simulations show that the proposed method has good channel estimation performance and can reduce the peak-to-average ratio of the OFDM channel as well.