Underwater Acoustic Orthogonal Frequency Division Multiplexing Systems Research Articles

A fast temporal multiple sparse Bayesian learning-based channel estimation method for time-varying underwater acoustic OFDM systems

In this paper, a fast temporal multiple sparse Bayesian learning (FTMSBL)-based channel estimation method for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) systems is proposed, which is optimized using the fast marginalized likelihood maximization method. The algorithm fully uses the consistent sparse structure and time-domain correlation properties of channels to improve the reconstruction performance and computational efficiency, offering better performance and higher computational efficiency than the traditional Bayesian learning algorithms. At the same time, the FTMSBL algorithm does not require computing the inverse of large matrices and consumes very little storage resources in the operation, making it suitable for hardware implementation. Simulation and sea trial results show that the FTMSBL-based underwater channel estimation algorithm achieves higher channel estimation accuracy than the orthogonal matching tracking algorithm, and the system bit error rate (BER) is significantly reduced; specifically, the FTMSBL algorithm can achieve optimal performance in strong time-dependent channels.

Real-Time Adaptive Modulation Schemes for Underwater Acoustic OFDM Communication.

Adaptive modulation received significant attention for underwater acoustic (UA) communication systems with the aim of increasing the system efficiency. It is challenging to attain a high data rate in UA communication, as UA channels vary fast, along with the environmental factors. For a time-varying UA channel, a self-adaptive system is an attractive option, which can choose the best method according to the channel condition to guarantee the continuous connectivity and high performance constantly. A real-time orthogonal frequency-division multiplexing (OFDM)-based adaptive UA communication system is presented in this paper, employing the National Instruments (NI) LabVIEW software and NI CompactDAQ device. In this paper, the received SNR is considered as a performance metric to select the transmission parameters, which are sent back to the transmitter for data transmission. In this research, a UA OFDM communication system is developed, employing adaptive modulation schemes for a nonstationary UA environment which allows to select subcarriers, modulation size, and allocate power adaptively to enhance the reliability of communication, guarantee continuous connectivity, and boost data rate. The recent UA communication experiments carried out in the Canning River, Western Australia, verify the performance of the proposed adaptive UA OFDM system, and the experimental results confirm the superiority of the proposed adaptive scheme.

Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels

We propose an effective, low complexity and multifaceted scheme for peak-to-average power ratio (PAPR) reduction in the orthogonal frequency division multiplexing (OFDM) system for underwater acoustic (UWA) channels. In UWA OFDM systems, PAPR reduction is a challenging task due to low bandwidth availability along with computational and power limitations. The proposed scheme takes advantage of XOR ciphering and generates ciphered Bose–Chaudhuri–Hocquenghem (BCH) codes that have low PAPR. This scheme is based upon an algorithm that computes several keys offline, such that when the BCH codes are XOR-ciphered with these keys, it lowers the PAPR of BCH-encoded signals. The subsequent low PAPR modified BCH codes produced using the chosen keys are used in transmission. This technique is ideal for UWA systems as it does not require additional computational power at the transceiver during live transmission. The advantage of the proposed scheme is threefold. First, it reduces the PAPR; second, since it uses BCH codes, the bit error rate (BER) of the system improves; and third, a level of encryption is introduced via XOR ciphering, enabling secure communication. Simulations were performed in a realistic UWA channel, and the results demonstrated that the proposed scheme could indeed achieve all three objectives with minimum computational power.

Impact of Inter-Channel Interference on Shallow Underwater Acoustic OFDM Systems

This paper investigates the impacts of Inter-Channel Interference (ICI) effects on a shallow underwater acoustic (UWA) orthogonal frequency-division multiplexing (OFDM) communication system. Considering both the turbulence of the water surface and the roughness of the bottom, a stochastic geometry-based channel model utilized for a wide-band transmission scenario has been exploited to derive a simulation model. Since the system bandwidth and the sub-carrier spacing is very limited in the range of a few kHz, the channel capacity of a UWA system is severely suffered by the ICI and Doppler effects. For further investigation, we construct the signal-to-noise-plus-interference ratio (SINR) based on the simulation model, then evaluate the channel capacity. Numerical results show that the various factors of a UWA-OFDM system as subcarriers, bandwidth, and OFDM symbols affects the channel capacity under the different Doppler frequencies. Those observations give hints to select the good parameters for UWA-OFDM systems.

Low-Complexity Doppler Compensation Algorithm for Underwater Acoustic OFDM Systems With Nonuniform Doppler Shifts

The Doppler effect severely degrades the orthogonality of orthogonal frequency division multiplexing (OFDM) systems, and is a severe problem in the underwater acoustic (UWA) communications, leading to nonuniform Doppler shifts for all subcarriers. In order to alleviate Doppler effect, resampling technique by oversized fast Fourier transform (FFT) can achieve fast and accurate resampling in frequency domain. Nevertheless, the accuracy of this conventional FFT based approach depends on the FFT size, which in turn increases receiver complexity. In order to overcome this problem, the Doppler compensation model in frequency domain is simplified by Taylor series expansion in this letter. Based on the simplified model, we propose the Doppler compensation algorithm by using FFT and interpolation to achieve higher compensation accuracy. Simulation results demonstrate that the proposed algorithm shows very similar bit error rate (BER) performance with that of Chirp Z-transform (CZT) based method. However, the complexity of our proposed algorithm is about half of CZT based method. Further, our proposed low-complexity Doppler compensation algorithm can also be applied to the sampling frequency offset (SFO) correction in terrestrial OFDM communication systems.

Compressive sensing compensation algorithm about nonlinear distortion of clipping for underwater acoustic OFDM systems

Orthogonal frequency division multiplexing (OFDM) is one of the most attractive multicarrier modulation schemes over underwater acoustic (UWA) communication due to its robustness against multipath channels. However, the multicarrier modulation technique also leads to a higher peak to average power ratio (PAPR). The nonlinear distortion is produced when the traditional clipping method was used to restrain PAPR. In this paper, we proposed a compensation algorithm based on compressive sensing (CS) about the nonlinear distortion. Combined with orthogonal matching pursuit (OMP) algorithm, pilot and null subcarriers are used to compensate the nonlinear distortion in the receiver. Theoretical analysis and simulation results show that the new algorithm can effectively reduce computational complexity and restrain the nonlinear distortion for UWA communication systems.

New Results on Joint Channel and Impulsive Noise Estimation and Tracking in Underwater Acoustic OFDM Systems

Impulsive noise can greatly affect the performance of underwater acoustic (UA) orthogonal frequency-division multiplexing (OFDM) systems. In this paper, by utilizing the sparsity of the UA channel impulse response and impulsive noise, we first propose a novel sparse Bayesian learning (SBL) based expectation maximization (EM) algorithm for joint channel estimation and impulsive noise mitigation in UA OFDM systems. Secondly, considering that the UA channel and impulsive noise are fast time-varying, we develop a new approach which combines the SBL with the forward-backward Kalman filtering to track the UA channel and impulsive noise. To further improve the system performance, we utilize the information available on data subcarriers for joint time-varying channel estimation and data detection, based on the SBL algorithm and the Kalman filter. The performance of our proposed algorithms is verified through both numerical simulations and by data collected during a UA communication experiment conducted in the estuary of the Swan River, Perth, Australia. The results demonstrate that compared with existing approaches, the proposed algorithms achieve a better system bit-error-rate and frame-error-rate performance.

Channel Estimation Based on Adaptive Denoising for Underwater Acoustic OFDM Systems

Underwater acoustic (UWA) communications systems suffer from a low signal-to-noise ratio (SNR) and a doubly selective channel, which are caused by many limitations, such as high propagation loss, slow propagation speed, and time-varying environmental factors. To overcome a low SNR, various diversity techniques are often adopted in UWA communications. However, such diversity can only be exploited with knowledge of the channel. Accordingly, channel estimation needs to be performed without obtaining the benefit of diversity. Moreover, accurate side information that can support a channel estimator (CE) is difficult to acquire at a low SNR under doubly selective channel. In this article, a novel CE based on an adaptive denoising is proposed for UWA orthogonal frequency-division multiplexing (OFDM) systems. The proposed method exploits two different types of pilot symbols. Channel impulse response (CIR) is estimated based on primary pilot symbols. By minimizing the squared error of the received secondary pilot symbols, a near-optimal denoising window is adaptively determined based on the channel length for the given CIR estimate. The proposed method does not require a priori information about channel statistics and SNR values. Analysis on the effect of denoising and the performance of the proposed denoising window estimator are also presented. Simulation and at-sea experiments verify that the proposed method has superior performance, compared with conventional CEs over diverse channel conditions. Complexity analysis shows that the proposed method is computationally efficient. Therefore, the proposed method is effective for real-time UWA OFDM systems under a harsh UWA channel with strong noise.

Fine Doppler scale estimations for an underwater acoustic CP-OFDM system

In the challenging underwater acoustic (UWA) communication channels, the performance of orthogonal frequency division multiplexing (OFDM) systems is severely affected by the Doppler effect. Hence, the Doppler scale estimation and compensation are critical for UWA OFDM systems. As a commonly adopted solution, the Doppler scale can be estimated by the maximization of auto-correlation between the received cyclic prefix (CP) and OFDM symbol. However, the accuracy of the auto-correlation based Doppler scale estimation is decided by the sampling rate of the signal. To achieve higher estimation accuracy, higher sampling rate is required and which means more computational complexity. To overcome this problem, we propose several fine Doppler scale estimation methods by interpolating the auto-correlation peak values, based on the derived expression of the expected auto-correlation outputs corresponding to a CP-OFDM signal after the UWA channel. Simulation results demonstrate the effectiveness of the proposed methods, and the decoding of experimental data further verifies the superiority of those methods.

An Underwater Acoustic OFDM System Based on NI CompactDAQ and LabVIEW

Recently the orthogonal frequency division multiplexing (OFDM) technique has attracted increasing interests in underwater acoustic (UA) communications. In this paper, we present an OFDM-based UA communication system, which is designed based on the National Instruments LabVIEW software and the CompactDAQ device. Details on both the transmitter and receiver system design are discussed. The performance of this UA OFDM system is verified through recent UA communication experiments performed in a tank, in the Canning River, and in the estuary of the Swan River, Western Australia. Experimental results show that the system achieves a reliable bit error rate performance even with high-order modulation schemes. Compared with conventional field-programmable gate array and digital signal processor based designs, the proposed implementation simplifies the prototype design process and reduces the software development time. The proposed system provides a flexible and reconfigurable prototype for researchers to test and validate the performance of UA communication algorithms in real UA channels.

Parameter estimation based on factor graph in wide‐band OFDM systems

The channel estimation in a wide-band orthogonal frequency division multiplexing (OFDM) system is different from the narrow-band system, for the estimation parameters should include the Doppler scale factor, which is a particular parameter of the wide-band system. The underwater acoustic OFDM system is a typical wide-band system, and its channel has more than one scale factor usually. For this multi-scale case, the common estimation method is a factor-by-factor process with repeated calculation of the reproduced waveform. This article proposes a factor-graph-based estimation method, which fully uses the restriction relationship among the parameters, and estimates the parameters simultaneously. To reduce the computation load, the modified Bayesian propagation (BP) algorithm is proposed. Comparing to the common BP algorithm, the modified BP algorithm simplifies the calculation of the condition probability in the summation. The simulation results of the mean squared error show that the proposed method is an effective estimation method for the multi-scale wide-band system. The bit error rate performance of the proposed method is very close to the result of the known channel-state-information, and outperforms the fractional Fourier transform (FrFT) and the modified particle swarm optimisation (MPSO) methods for well channel condition.

A low-complexity orthogonal matching pursuit based channel estimation method for time-varying underwater acoustic OFDM systems

In this paper, we propose a low-complexity Orthogonal Matching Pursuit (OMP) based channel estimation method in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, where the channel is time-varying and described by a limited number of paths, each characterized by path delay, path gain and path Doppler scale. In the proposed method, we calculate the candidate path signature Hermitian inner product matrix in advance, avoiding the repeated calculations at each iteration in conventional OMP method. To further reduce the complexity, we estimate the path gains by solving a least-squares (LS) problem after the whole iteration procedure instead of doing this at each iteration step in the conventional OMP method. Results of numerical simulation and sea trial demonstrate the effectiveness of the proposed method in time-varying UWA channels, which can achieve the comparable performance with much lower computational complexity compared with the conventional OMP method.

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

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.

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.

Iterative Compressive Sensing for the Cancellation of Clipping Noise in Underwater Acoustic OFDM System

Due to the characteristics of the underwater acoustic channel especially the limited bandwidth, orthogonal frequency division multiplexing (OFDM) is widely used because of its high spectrum efficiency and ability in anti-multipath fading. However OFDM also has its drawbacks, one of which is the relatively high peak-to-average ratio (PAPR). The problem leads to saturation in the power amplifier and consequently distorts the signal which is not allowed in the underwater acoustic communication. Clipping as the most classic and convenience way is widely applied to address the high PAPR. However, it introduces additional noise that degrades the system performance. In this paper compressed sensing (CS) technique is proposed to mitigate the clipping noise. The scheme exploited pilot tones and data tones instead of reserved tones, which is different from the previous works and causes less loss of data rate. Also, in contrast with previous works, to minimizing the influence of underwater acoustic channel, compressed sensing in channel estimating is also adopted during mitigating the clipping noise, which can provide more accurate channel characteristics for estimating the clipping noise than traditional method such as LS or MMSE. The iterative CS technology proposed in this article can significantly improve the communication quality even in low SNR.

Joint Channel and Impulsive Noise Estimation in Underwater Acoustic OFDM Systems

Impulsive noise is one key factor that limits the performance of underwater acoustic (UA) communications. In this paper, two pilot-subcarrier based algorithms are proposed to improve the performance of channel estimation and impulsive noise mitigation for UA orthogonal frequency-division multiplexing (OFDM) systems. The first algorithm jointly estimates the channel and the impulsive noise based on the least-squares principle. The second algorithm is developed with the aim to reduce the computational complexity, where the expectation-maximization principle is applied to estimate the channel and the impulsive noise iteratively. We compare the proposed algorithms by simulations and apply them to process the data collected during an experiment conducted in December 2015 in the estuary of the Swan River, Western Australia. The results show that both proposed algorithms have better performance than existing methods in mitigating impulsive noise in UA OFDM systems.

Equalization and Carrier Frequency Offset Compensation for Underwater Acoustic OFDM Systems

Due to noise enhancement, conventional Zero Forcing (ZF) equalizers are not suitable for wireless Underwater Acoustic (UWA) Orthogonal Frequency Division Multiplexing (OFDM) communication systems. Furthermore, these systems suffer from increasing complexity due to the large number of subcarriers, especially in Multiple-Input Multiple-Output (MIMO) systems. On the other hand, the Minimum Mean Square Error equalizer suffers from high complexity. This type of equalizers needs an estimation of the operating Signal-to-Noise Ratio to work properly. In this paper, we propose a Joint Low-Complexity Regularized ZF equalizer for MIMO UWA-OFDM systems to cope with these problems. The main objective of the proposed equalizer is to enhance the system performance with a lower complexity by performing equalization in two steps. The co-channel interference can be mitigated in the first step. A regularization term is added in the second step to avoid the noise enhancement. Simulation results show that the proposed equalization scheme has the ability to enhance the UWA system performance with low complexity.

Joint Channel Estimation and Impulsive Noise Mitigation in Underwater Acoustic OFDM Communication Systems

Impulsive noise occurs frequently in underwater acoustic (UA) channels and can significantly degrade the performance of UA orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we propose two novel compressed sensing based algorithms for joint channel estimation and impulsive noise mitigation in UA OFDM systems. The first algorithm jointly estimates the channel impulse response and the impulsive noise by utilizing pilot subcarriers. The estimated impulsive noise is then converted to the time domain and removed from the received signals. We show that this algorithm reduces the system bit-error-rate through improved channel estimation and impulsive noise mitigation. In the second proposed algorithm, a joint estimation of the channel impulse response and the impulsive noise is performed by exploiting the initially detected data. Then, the estimated impulsive noise is removed from the received signals. The proposed algorithms are evaluated and compared with existing methods through numerical simulations and on real data collected during a UA communication experiment conducted in the estuary of the Swan River, WA, Australia, during December 2015. The results show that the proposed approaches consistently improve the accuracy of channel estimation and the performance of impulsive noise mitigation in UA OFDM communication systems.

Impulsive Noise Mitigation in Underwater Acoustic OFDM Systems

Mitigation of impulsive noise has been extensively studied in wireline, wireless radio, and powerline communication systems. However, its study in underwater acoustic (UWA) systems is quite limited. This paper considers impulsive noise mitigation for underwater orthogonal frequency-division multiplexing (OFDM) systems, where the system performance is severely impacted by the channel Doppler effect. We propose a practical approach based on a least squares formulation: First, the positions of impulsive noise are determined in the time domain based on the signal amplitude, and second, impulsive noise samples are jointly estimated with the Doppler shift based on the measurements of the OFDM null subcarriers. Based on the available channel estimate and tentative data symbol decisions, an iterative receiver is further developed. Data sets have been acquired in a recent sea experiment near Kaohsiung city, Taiwan, in May 2013. Performance results based on extensive simulations and collected data sets demonstrate that the proposed receivers effectively mitigate impulsive noise for UWA OFDM systems.

Comparison of sparse recovery algorithms for channel estimation in underwater acoustic OFDM with data-driven sparsity learning

Through exploiting the sparse nature of underwater acoustic (UWA) channels, compressed sensing (CS) based sparse channel estimation has demonstrated superior performance compared to the conventional least-squares (LS) method. However, a priori information of channel sparsity is often required to set a regularization constraint. In this work, we propose a data-driven sparsity learning approach based on a linear minimum mean square error (LMMSE) equalizer to tune the regularization parameter for the orthogonal frequency division multiplexing (OFDM) transmissions. A golden section search is used to accelerate the sparsity learning process. In the context of the intercarrier interference (ICI)-ignorant and ICI-aware UWA OFDM systems, the block error rates (BLERs) using different sparse recovery algorithms for channel estimation under the L0, L1/2, L1, and L2 constraints are compared. Simulation and experimental results show that the data-driven sparsity learning approach is effective, overcoming the drawback of using a fixed regularization parameter in different channel conditions. When the sparsity parameter for each approach is optimized based on the data-driven approach, the L1/2 recovery algorithm and the considered four L1 recovery algorithms: SpaRSA, FISTA, Nesterov, and TwIST, have nearly the same BLER performance, outperforming L0 and L2 algorithms.