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In underwater acoustic communications (UAC), signal synchronization plays a key role in the performance. It is usually performed using a known preamble transmitted prior to the data. However, the underwater acoustic (UWA) channel is characterized as time-varying and frequency-varying, which makes the preamble fluctuated as well as the transmitted d...
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... the false alarm is eliminated and the system is still sleeping [33]. Another example is shown in Figure 8 matched filter and an FrFT detector can effectively eliminate the effects of false alarm signals [34]. ...
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In an underwater acoustic cellular (UAC) system, underwater equipment or sensor nodes need to detect the identity of an underwater base station (UBS) and synchronise it with a serving UBS. It is known that, in an underwater acoustic channel, the temporal variability of the ocean coupled with the low speed of sound in water may induce a significant...
The performance of underwater acoustic communication is significantly affected by multipath propagation and Doppler spread. In this paper, we propose a new communication technique called overlapped chirp spread carrier (OCSC) by modifying the existing sweep spread carrier (SSC) technique, which is robust in a multipath propagation environment. Our...
Synchronous detection acts as a key role in underwater acoustic communications. It is discovered that the Symmetrical Triangular Linear Frequency Modulation (STLFM) signal has double energy peaks in their fractional Fourier transform domain and these two peaks have symmetry in their amplitude if the STLFM signal is captured in the accurate time win...
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... Citation information: DOI 10.1109/ACCESS.2024.3361845 Frequency Modulation (LFM) signals are also introduced into the simultaneous experiments due to their widespread use in timing synchronization for underwater acoustic communication [56,57]. The detailed experimental parameters of the OFDM system are shown in TABLE 2. ...
The strong multipath and long delay extensions can lead to large errors in the timing offset estimation and carrier frequency offset estimation of the Orthogonal Frequency Division Multiplexing (OFDM) underwater communication system, resulting in significant performance degradation. Therefore, this paper proposes a robust time-frequency synchronization method for OFDM systems. First, a new preamble based on Constant Amplitude Zero Autocorrelation (CAZAC) sequence is designed, and a new signal timing offset estimation method is developed based on the time domain characteristics of the preamble. Secondly, a two-stage frequency offset estimation (CFO) method is designed in the time domain followed by the frequency domain, which achieves a wide range of high-accuracy frequency offset estimation. Finally, an adaptive threshold signal first-arrival path detection method is proposed to achieve reliable detection of the position of the signal first-arrival path under complex multipath channels. The simulation, pool, and lake experiment results are provided to verify the reliability and effectiveness of this method. Compared with the traditional methods, the proposed method can more accurately obtain the signal timing position and estimate the CFO in complex multipath channels, and at the same time greatly expand the estimation range of the CFO.
... FrFT is applied to the LFM signal and hyperbolic frequency modulation (HFM) signal for the determination of joint time and frequency synchronisation [58]. A time and frequency synchronisation algorithm based on FrFT on the preamble with a symmetrical triangular linear frequency modulation (STLFM) signal is proposed for OFDM systems in UWA channels [59], [60]. Instead of a cyclic prefix (CP), zero padding (ZP) is used with OFDM and proposed for channel equalisation for the UWA channel [61]. ...
Underwater acoustic (UWA) communication is the only option for underwater long-distance wireless communication. However, its data rate is very low, in tens of kbps, and the time-varying nature of the UWA channel exhibits a Doppler effect. The data rate can be increased using a multicarrier technique, such as Filter Bank Multicarrier (FBMC). The FBMC-OQAM system does not require a cyclic prefix (CP) to eliminate intersymbol interference (ISI). Therefore, compared to other multicarrier techniques, the FBMC-OQAM system achieves the maximum spectral efficiency. Despite the well-designed filter, synchronisation errors cause performance degradation in the FBMC-OQAM system. In this paper, we propose frame boundary and Doppler frequency estimation methods for the FBMC-OQAM system under UWA channels. In this study, we propose a preamble-based frame boundary. The conjugate symmetry property is used in the frame boundary estimation. The performance of the proposed technique was studied under different UWA channels with different Doppler scale. The proposed time synchronisation technique yielded multiple peaks under UWA channels. A method for identifying the peak that represents the frame boundary is proposed. We compared the performance of proposed timing metric with existing three reference timing metrics. The proposed timing metric exhibited superior over two metrics and almost identical performance with the Liming’s metric with less computational complexity. In addition, we propose a convolutional neural network (CNN)-based Doppler scale estimation method. The proposed technique was shown to estimate a wide range of Doppler scale factors. The performance of the proposed technique was studied by considering different UWA channels at different SNRs. The proposed CNN architecture is analysed by changing the CNN parameters. Stacked autoencoders were adopted for Doppler scale estimation and the performance of the proposed CNN-based Doppler scale estimation method was compared with the stacked autoencoders based Doppler scale estimation method. Simulation results show that the proposed CNN-based Doppler Scale estimator exhibits good performance in UWA channels at different SNRs and Doppler scale factors.
... To reduce the synchronization complexity, we used multiple equispaced LFM signals in one packet, which is also widely used in UWA communications [19,20]. The LFM signal in the analytic signal form is written as: ...
This study proposes a novel receiver structure for underwater vertical acoustic communication in which the bias in the correlation-based estimation for the timing offset is learned and then estimated by a deep neural network (DNN) to an accuracy that renders subsequent use of equalizers unnecessary. For a duration of 7 s, 15 timing offsets of the linear frequency modulation (LFM) signals obtained by the correlation were fed into the DNN. The model was based on the Pierson–Moskowitz (PM) random surface height model with a moderate wind speed and was further verified under various wind speeds and experimental waveforms. This receiver, embedded with the DNN model, demonstrated lower complexity and better performance than the adaptive equalizer-based receiver. The 5000 m depth deep-sea experimental data show the superiority of the proposed combination of DNN-based synchronization and the time-invariant equalizer.
... Afterward, we construct a traversal path R t and end at (h t , i t ), which should end at a leaf node (line 2). Thus, we propose the threshold as τ h,k (t) = c 2 ln 1/ δ t + ρ −2h ν 1 2 2 kd C r (6) which is used to decrease the depth of tree H(t) and guarantee each node explored adequately such that we are more confident about the acquired feedback μ h t ,i t (t). As the experience of vessels and ocean feedback increases, τ h (t) becomes smaller. ...
The ocean has been investigated for centuries across the world, and planning the travel path for vessels in the ocean has becoming a hot topic in recent decades as the increasing development of worldwide business trading. Planning such suitable paths often bases on big data processing in cybernetics, while not many investigations have been done. We attempt to find the optimal path for vessels in the ocean by proposing an online learning dispatch approach on studying the Mission-Executing-Feedback (MEF) model. The proposed approach explores the ocean sub-domain to achieve the largest average travelling feedback for different vessels. It balances the ocean path by deep and wide search, and considers adaptation for these vessels. Further, we propose a contextual multi-armed bandit-based algorithm, which provides accurate exploration results with sub-linear regret and significantly improves the learning speed. To accelerate the vessel recommending process, a method of context partition for coming vessels is also used. To support big data analysis, we establish an ocean sub-domain (OS)-cluster tree extended from top-to-down to handle the dynamical subdivision of the ocean. Experimental results show that the proposed MEF approach possesses 90% accuracy gain over random exploration and achieves about 25% accuracy improvement over other contextual bandit models on supporting big data online learning pre-eminently.
... Underwater research and engineering often involve underwater acoustic operations close to the seafloor, such as seafloor mapping, sub-sea gas leakage monitoring, tracking and navigation of underwater vehicles or divers, detection of submariners, and underwater acoustic communications (Newton et al., 1980;Prior and Hooper, 1999;Abrahamsen, 2012;Lurton, 2002;Harsdorf et al., 1999;Li et al., 2019a;Song et al., 2019;Yuan et al., 2019;Zhang et al., 2019a;Li et al., 2019bLi et al., , 2016Li, 2017). Acoustic operations benefit from a high signal-to-noise ratio (SNR), which may be compromised by noise in the ocean, e.g., from transit vessel noise and sea surface noise. ...
... To illustrate the capability of the proposed CAPSE method, an example of its application to real data is given as follows. Underwater acoustic channel in shallow water is often characterised as noisy and high attenuation from the acoustic source [35][36][37][38][39][40][41][42][43] . Thus we choose the South China sea to conduct the experiment and collect data. ...
A common approach to detect sinusoidal signals buried in noise is based on spectral analysis, such as the periodogram. The fluctuations of the spectral components associated with the noise can be alleviated via incoherent averaging of the power spectral estimates of each segment, which is the basis of Welch's method. However, Welch's method only utilizes the incoherent information between segments of signals. In this paper, we propose a method of coherent averaging between segments, which enhances ratio of time-invariant sinusoidal signals relative to the level of the noise background. The gain of coherent averaged power spectral estimate has been derived in terms of time duration of the signal. The proposed method provides a flexible, computationally efficient implementation of signal detection, which can be formulated to allow for various integration times to be realised in different frequency bands. Simulation and experimental results show that the proposed method outperforms the Welch's method and the periodogram method.
... Most of the existing underwater acoustic synchronization signals are based on the LFM signal or its extended signals [14], [18], [19]. Among these signals, symmetrical triangular linear frequency modulation (STLFM) [7] is one of the preferred synchronization signal at low signal-to-noise ratio (SNR) due to its large time bandwidth product and symmetrical triangular structure [20], [21]. ...
... Error Multipath channel and received signals collected in Wuyuan Bay, Xiamen, China[7]. distributed in the range of 0.04 ms, only 42% of them are in the range of 0.01 ms. While the synchronization errors of the STLFM algorithm are mostly in the range of 0.01 ms, and about 86% of them are in the range of 0.005 ms. ...
Synchronous detection acts as a key role in underwater acoustic communications. It is discovered that the Symmetrical Triangular Linear Frequency Modulation (STLFM) signal has double energy peaks in their fractional Fourier transform domain and these two peaks have symmetry in their amplitude if the STLFM signal is captured in the accurate time windows for the synchronization. In this paper, we propose a synchronization detection method based on the STLFM. To verify the effectiveness of the proposed method, we apply simulations
based on the Bellhop program and sea trial data collected at Wuyuan Bay, Xiamen, China. The results show that the proposed STLFM method outperforms the LFM method.
Frame synchronization detection before data transmission is an important module which directly affects the lifetime and coexistence of underwater acoustic communication (UAC) networks, where linear frequency modulation (LFM) is a frame preamble signal commonly used for synchronization. Unlike terrestrial wireless communications, strong bursty noise frequently appears in UAC. Due to the long transmission distance and the low signal-to-noise ratio, strong short-distance bursty noise will greatly reduce the accuracy of conventional fractional fourier transform (FrFT) detection. We propose a multi-segment verification fractional fourier transform (MFrFT) preamble detection algorithm to address this challenge. In the proposed algorithm, 4 times of adjacent FrFT operations are carried out. And the LFM signal identifies by observing the linear correlation between two lines connected in pair among three adjacent peak points, called ‘dual-line-correlation mechanism’. The accurate starting time of the LFM signal can be found according to the peak frequency of the adjacent FrFT. More importantly, MFrFT do not result in an increase in computational complexity. Compared with the conventional FrFT detection method, experimental results show that the proposed algorithm can effectively distinguish between signal starting points and bursty noise with much lower error detection rate, which in turn minimizes the cost of retransmission.
Underwater acoustic channel is a challenging medium for communication due to the presence of significant multipath, high noise, frequency‐dependent propagation loss, and high and non‐uniform Doppler spread. Doppler shift is non‐negligible in underwater communication due to the low velocity of underwater signals. Synchronization and Doppler estimation are important requirements for achieving good performance in this channel. Synchronization algorithms that give good performance in radio communication do not work well in underwater communication. Hence, this area has received a lot of attention from researchers. This paper surveys important works in the area. The techniques proposed in the literature for frame synchronization, frequency and phase synchronization, and timing synchronization in single carrier communications are reviewed here. The synchronization techniques proposed for OFDM, MIMO OFDM, and spread spectrum communication are also surveyed. Doppler estimation methods proposed in the literature are also reviewed. It is found that most of the recent works in underwater acoustic communication focus on OFDM synchronization. Deep learning‐based methods proposed in the literature are also reviewed. Key open problems and areas that require future research attention in the field of synchronization and Doppler estimation in underwater communications are highlighted in this paper. The area needing most attention of underwater communication researchers was found to be MIMO OFDM due to the difficulty in synchronization in such systems while used in underwater communication. Reducing the computational complexity of the algorithms used is also important for future work. Schemes that work with Doppler due to relative velocity over 10 m/s also need to be developed.
Underwater acoustic channel is much more complex than radio channel. Background noise, multipath extension, and Doppler frequency shift are all factors that affect high‐speed and stability for underwater communication. Orthogonal frequency division multiplexing (OFDM) is a modulation technology with high spectrum efficiency and strong anti‐multipath ability, usually used in high‐speed underwater acoustic communication. Signal arrival time detection and timing estimation before receiving data is an essential module in an underwater communication system. Timing estimation error will seriously affect the demodulation of the signal. In order to solve the problem of a complex channel environment in underwater acoustic communication system, a timing estimation method is proposed based on multiple hyperbolic frequency‐modulated (MHFM) signals. In order to improve the performance of underwater acoustic communication systems, this article proposes an iterative method of correlation‐sum‐mean absolute error (MAE) based on hyperbolic frequency‐modulated (HFM). The simulation results show that the proposed method can provide better performance and higher timing estimation accuracy under the conditions of low signal‐to‐noise ratio (SNR) and Doppler effect under oceanic noise channel and Doppler spread channel. In order to solve the problem of a complex channel environment in underwater acoustic communication system, a timing estimation method of correlation‐sum‐mean absolute error (MAE) is proposed based on multiple hyperbolic frequency‐modulated (MHFM) signals. It estimate signal arrival time for symbol synchronization. The simulation results show that the proposed method can provide better performance and higher time estimation accuracy under the conditions of low signal‐to‐noise ratio (SNR) and Doppler effect.
