Acoustic Communication Channel Research Articles

A metric to quantify the time-varying characteristics of underwater acoustic communication channels.

Underwater acoustic communication signals suffer from time dispersion due to time-varying multipath propagation in the ocean. This leads to intersymbol interference, which in turn degrades the performance of the communication system. Typically, the channel correlation functions are employed to describe these characteristics. In this paper, a metric called the channel average correlation coefficient (CACC) is proposed from the correlation function to quantify the time-varying characteristics. It has a theoretical negative relationship with communication performance. Comparative analysis involving simulations and experimental data processing highlights the superior effectiveness of CACC over the traditional metric, the channel coherence time.

Communication-aware formation control for networks of AUVs

We propose a distributed formation control algorithm augmented with communication awareness. We consider autonomous underwater vehicles (AUVs) that are able to communicate over an acoustic link using a Time Division Multiple Access (TDMA) protocol, and to measure the Signal-to-Noise Ratio (SNR) of incoming messages. Based on the measured SNR and packet loss, we endow them with a distributed formation control scheme that accounts for the time-varying nature of the acoustic communication channel. This scheme allows a network of N AUVs to follow a pre-determined, twice-differentiable path while adapting their formation. The size of the formation is dynamically scaled by a formation adaptation mechanism to stabilize the estimated packet loss probability at a desired level. A distributed packet loss estimator is then built on top of the same average consensus routines used by the formation control algorithm, and thus comes with a minimal communication overhead. We test the algorithm by means of high-fidelity simulators, and verify its efficacy in making the network of agents retain formation-wide communication capabilities in a range of cases.

Optimizing multi-user indoor sound communications with acoustic reconfigurable metasurfaces

Sound in indoor spaces forms a complex wavefield due to multiple scattering encountered by the sound. Indoor acoustic communication involving multiple sources and receivers thus inevitably suffers from cross-talks. Here, we demonstrate the isolation of acoustic communication channels in a room by wavefield shaping using acoustic reconfigurable metasurfaces (ARMs) controlled by optimization protocols based on communication theories. The ARMs have 200 electrically switchable units, each selectively offering 0 or π phase shifts in the reflected waves. The sound field is reshaped for maximal Shannon capacity and minimal cross-talk simultaneously. We demonstrate diverse acoustic functionalities over a spectrum much larger than the coherence bandwidth of the room, including multi-channel, multi-spectral channel isolations, and frequency-multiplexed acoustic communication. Our work shows that wavefield shaping in complex media can offer new strategies for future acoustic engineering.

Analysis of High-Frequency Communication Channel Characteristics in a Typical Deep-Sea Incomplete Sound Channel

Deep-sea acoustic communication has attracted widespread attention in recent years. Research on deep-sea acoustic communication channel characteristics is essential for the development and system design of deep-sea acoustic communication technologies. However, the structural and spatiotemporal characteristics of deep-sea high-frequency acoustic communication channels (unlike those of shallow-water acoustic communication channels) are poorly understood. Based on a channel measurement experiment in a typical deep-sea incomplete sound channel environment in the South China Sea, this paper analyzes the structural and spatiotemporal characteristics of high-frequency underwater acoustic channels in the direct-arrival and shadow zones. The channel multipath vertical structure, amplitudes of different path clusters, root mean square (RMS) delay spread, and channel temporal coherence are investigated at different depths. The randomness of different path clusters is quantified as the coefficient of variation. The channels in the shadow zone are characterized by a complex multi-path structure, high RMS delay spread, and low temporal coherence. On the contrary, the channels of the direct-arrival zone show multi-path convergence, low RMS delay spread, and high temporal coherence. Understanding the dynamic changes in these parameters can guide the statistical modeling of channels and the design of communication algorithms in different zones in deep-sea high-frequency acoustic communication scenarios.

Reinforcement learning-based adaptive modulation scheme over underwater acoustic OFDM communication channels

The conventional adaptive modulation (AM) technology is restricted in its effectiveness within the rapidly changing time-space-frequency of the underwater acoustic (UWA) channel. In contrast, reinforcement learning (RL) based AM employs an online training mode that interacts with the channel environment, enabling optimal modulation and coding schemes. By utilizing channel information and considering the bit error ratio (BER) of signals, we propose an RL-based AM strategy that adapts the modulation scheme to enhance spectral efficiency in a non-stationary UWA environment and maximize data throughput for UWA communication. The research is centered on the application of multi-carrier modulation in orthogonal frequency division multiplexing (OFDM). The AM is formulated as a Markov decision process (MDP) and resolved through the implementation of the Proximal Policy Optimization (PPO) algorithm. Simulation experiments indicate that the PPO algorithm exhibits faster and more efficient convergence while maintaining a stable convergence effect, in comparison to the Deep Q-Network (DQN) algorithm, Double DQN (DDQN) algorithm, and Q-Learning (QL) algorithm when addressing the UWA modulation problem.

UCSNet: Priors Guided Adaptive Compressive Sensing Framework for Underwater Images

Image acquisition and reconstruction play an important role in underwater detections and explorations. However, the limited underwater acoustic communication channels and narrow bandwidth resources will have a great impact on the performance of the traditional data acquisition methods, resulting in the loss of details and blur in reconstructed underwater images. Compressive sensing theory (CS) which can reconstruct images from fewer measurement than that required by Nyquist sampling law has been proved to have good effect on image sampling and reconstruction. Nevertheless, the existing CS methods are not suitable for underwater images because most of them are designed for on-land images which have huge differences from underwater images. In this paper, we propose a novel priors guided adaptive underwater compressive sensing framework, dubbed UCSNet, which can effectively sample and reconstruct underwater images under a fixed low sampling ratio. In particular, our framework is composed of three sub-networks: underwater priors extraction and guidance network (UEGN), sampling matrix generation network (SMGNet) and channel-wise reconstruction network (CWRNet). Specifically, inspired by the underwater imaging physical models, UEGN is designed to extract features of underwater priors information and combine them adaptively. UEGN also introduce the imaging process into CS task to make sampling and reconstruction consistent with underwater imaging characteristics. SMGNet uses underwater content degradation to assist the analysis of structural information to generate sampling matrices. Considering the monotony of color tones caused by light absorption in underwater images, CWRNet embedded with the channel-wise module (CWM) is designed to enforce the whole network to allocate different number of sampling points on luminance and chrominance channels respectively and make feature maps extracted from them complement with each other. Experimental results demonstrate that our proposed framework can achieve both PSNR and SSIM gains on underwater images reconstruction quality and have greater visual quality than other state-of-art methods under fixed sampling ratios.

Characterization and modeling of high-speed underwater acoustic communication channels

High-speed mobile underwater acoustic communication (HSM-UAC) is a relatively unexplored terrain, but its importance is expected to increase with the widespread application of flexible underwater mobile platforms such as AUVs and UUVs. Knowledge of HSM-UAC channels is a prerequisite to achieving reliable communication. This work conducted two HSM-UAC channel measurement experiments with a maximum speed of 20knots. The analysis of channel time-varying characteristics demonstrates the shortcomings of traditional consistent Doppler frequency offset channel model. The experimental findings reveal the presence of two non-stationary modes in the HSM-UAC channel. For short moving distances, the changes in channel geometry result in each path experiencing a different Doppler scale, thus observing the multiscale-multilag (MSML) channels. For long moving distances, the alteration in sound ray propagation tracks entirely reshapes the channel impulse response (CIR) structure, leading to a rapid decline in channel time correlation. In further analysis, the correlation coefficient of the power delay profile (PDP) is used to measure channel stationarity. Subsequently, the stationary time and corresponding stationary distance are calculated as characterization parameters for the time-varying channel model. The quantitative evaluation results are helpful for the communication signal design and determining the update frequency of channel state information.

Reliable Data Transmission in Underwater Wireless Sensor Networks Using a Cluster-Based Routing Protocol Endorsed by Member Nodes

Considering Underwater Wireless Sensor Networks (UWSNs) have limited power resources (low bandwidth, long propagation delays, and non-rechargeable batteries), it is critical that they develop solutions to reduce power usage. Clustering is one solution because it not only saves energy consumption but also improves scalability and data integrity. The design of UWSNs is vital to the development of clustering algorithms. The limited energy of sensor nodes, narrow transmission bandwidth, and unpredictable topology of mobile Underwater Acoustic Wireless Sensor Networks (UAWSNs) make it challenging to build an effective and dependable underwater communication network. Despite its success in data dependability, the acoustic underwater communication channel consumes the greatest energy at a node. Recharging and replacing a submerged node’s battery could be prohibitively expensive. We propose a network architecture called Member Nodes Supported Cluster-Based Routing Protocol (MNS-CBRP) to achieve consistent information transfer speeds by using the network’s member nodes. As a result, we use clusters, which are produced by dividing the network’s space into many minute circular sections. Following that, a Cluster Head (CH) node is chosen for each circle. Despite the fact that the source nodes are randomly spread, all of the cluster heads are linked to the circle’s focal point. It is the responsibility of the MNS-CBRP source nodes to communicate the discovered information to the CH. The discovered data will then be sent to the CH that follows it, and so on, until all data packets have been transferred to the surface sinks. We tested our techniques thoroughly using QualNet Simulator to determine their viability.

НАВИГАЦИОННАЯ СИСТЕМА АВТОНОМНОГО ПОДВОДНОГО АППАРАТА НА ОСНОВЕ ДАННЫХ, ПЕРЕДАВАЕМЫХ ПО АКУСТИЧЕСКОМУ КАНАЛУ ОТ ГИДРОАКУСТИЧЕСКОЙ СТАНЦИИ

The paper proposes a method for constructing a navigation system of an autonomous underwatervehicle (AUV) using a limited set of onboard sensors and receiving position data of the AUVvia acoustic communication channels from a hydroacoustic underwater monitoring station (HUMS).The proposed method obtains estimates of the position and velocities of the AUV based on its dynamic model, assuming that the angular velocities, orientation angles and depth of the AUV are determinedusing its onboard sensors. Linear velocities are not directly measured. The Kalman filter isused to implement the navigation algorithm. At the same time, the feature of this algorithm is theimplementation of a two-stage procedure for correcting estimates of coordinates and linear velocitiesof the AUV obtained on the basis of its dynamic model. This correction is carried out in two ways,depending on what data is available at the current step of the system. The first option assumes thecorrection of these estimates only on the basis of data from the depth sensor, which is updated ateach step of the system. And the second option is used when data comes from HUMS via acousticcommunication channels. This data comes with a delay due to the limited speed of propagation ofacoustic signals in the aquatic environment, and may also periodically be distorted and disappear.The paper proposes a method for compensating for these delays by saving an array of previouslycalculated data and evaluating the necessary corrections by comparing the received data with theestimates obtained earlier. The proposed scheme for the construction of the navigation system allowsfor the correction of its readings in the conditions of irregular data updates from the HUMS. Theresults of modeling using a model describing all the main features of the HUMS operation and itsinteraction with the AUV (delays in obtaining information, the presence of measurement noise andsampling of HUMS data) showed a sufficiently high efficiency of the proposed solution. At the sametime, the main advantage can be indicated by the possibility of using a minimum number of on-boardsensors and the possibility of fast deployment of HUMS for interaction with AUV.

Soundscapes of the Yungas Andean forest: Identifying the acoustic footprint of an anuran assemblage

Soundscapes are composed by the conjunction of sound patterns in each space and time. The biophony produced by the advertisement calls of anurans are one of the most outstanding features of a soundscape, particularly in subtropical montane areas such as the Yungas Andean forests of NW Argentina. We recorded soundscapes with automated recording units along an altitudinal gradient and throughout a whole year in the Calilegua National Park. Using a novel combination of visual analysis and acoustic indices we have been able to identify the anuran acoustic fingerprint in the soundscapes of a seasonal subtropical montane forest. In addition, we detected a decrement in using the acoustic communication channel along the altitudinal gradient by anuran assemblages, in agreement with a decrement in species richness. Our results showed a seasonal effect of the vocalization patterns, splitting the group of sounds recorded in two main periods along the year. In this study we presented the first description of Argentina's altitudinal strata of the Yungas Andean forests. This information provides a solid basis for monitoring programs of direct and indirect anthropogenic impacts. Additionally, we highlight the importance of understanding how soundscapes are composed in a naturally protected area and, posit them as a novel ecosystem service that could benefit human health. We emphasise that Natural Soundscapes should be protected and must be included as intangible heritage in the management plans of protected areas in the country. Likewise, the methods proposed in this study encourage settling down the basis for implementing long-term acoustic monitoring of anuran species and other acoustic communities in the Yungas ecoregion.

Experimental characterization of the mobile underwater acoustic communication channel in Shallow Water

This paper presents the results for the experimental characterization of the mobile underwater acoustic communication channel in shallow water through the statistics of the fading and the time and frequency dispersion parameters. Starting from measurements performed in Forno Bay, Arraial do Cabo, Rio de Janeiro, Brazil, using three hydrophones in the reception side, two CW signals (3 and 6 kHz) and three sequences of LFM chirps in different bands: 2 to 4 kHz, 5 to 7 kHz and 1 to 8 kHz, the mobile sounding was performed in that channel in order to characterize it as in narrowband as in wideband.

Precoded Faster-Than-Nyquist Signaling for Doubly Selective Underwater Acoustic Communication Channel

In this letter, we propose faster-than-Nyquist (FTN) signaling transmission that is robust for an underwater acoustic (UWA) doubly selective channel. Owning to the explicit benefits of a reduced symbol interval specific to FTN signaling, the detrimental effects of a time-selective channel are significantly relaxed. Furthermore, upon introducing single-carrier transmit precoding and equalization, the proposed FTN signaling achieves a high detection performance. Our simulation results demonstrate that the proposed FTN signaling outperforms the conventional Nyquist-based benchmark scheme in the doubly selective channel.

Localization and correction of location information for nodes in UWSN-LCLI

Location is essential to identify the floating senor nodes in underwater. Underwater Sensor Networks (UWSNs) enable various critical applications like monitoring underwater environment, flora and fauna, sea life, exploration such as earthquake, disaster mitigation, motion tracking. In many cases, an underwater sensor node is necessary. Some points make the underwater study different and slightly complex. The first point is that radio signal cannot detect the underwater environment, despite radio waves acoustic signals used, which runs due to water currents. The second point is that earthy networks are deployed in the 2D environment, but 3D node deployment is needed in the Underwater environment. The third important point is locating the sensor node position in 3D deployment because nodes move (Floating Nodes) with water waves. It is a very complex assumption to fix the location of the sensor node.Various locations finding algorithms are proposed for the earthy network. Still, the underwater acoustic communication channel's unique properties, like high and varying transmission delay, required the Algorithm to locate the node's position. Only a few localization algorithms are proposed and presented in recent years to address these location finding requirements. Existing algorithms are distributed, scalable, and iterative in nature, but at the same Time, these algorithms use many acknowledgement messages for location identification and communication. To reduce the number of messages and to overcome the transmission, a new Location Finding and Correcting Algorithm for underwater acoustic sensor networks has been proposed in this paper. Our proposed Algorithm (LCLI- Localization and Correction of Location Information for nodes in UWSNs) find the Location of nodes accurately and efficient by considering localization errors. In this paper, we have also proposed a location correcting Algorithm that may use when the localization errors are more. Our proposed Algorithm (UWSN-LCLI) is compared with existing proposed algorithms, and our Algorithm outperforms and shows significant improvement.

PERFORMANCE ENHANCEMENT OF UNDERWATER ACOUSTIC COMMUNICATION USING DEEP LEARNING APPROACH

This research aims to improve underwater acoustic communication using deep learning. Due to an increase in undersea operations, dependable communication systems have become more important. The undersea environments complexity reduces the efficacy of underwater audio communication, despite its widespread use. Using mathematical equations and approximations, the underwater sound pathway has been modeled. These projects aim to enhance underwater communication systems by better understanding the underwater audio channel. In this study, we investigate the abilities of device learning and deep studying to investigate and accurately replicate the underwater acoustic channel by making use of real-world underwater data. This is done by analyzing the results of the study. The information has been compiled with the aid of using a combination of strategies, which include machine learning and in-depth reading. In particular, the Deep Neural Community (DNN) and long quick term memory (LSTM) modeling strategies are used in order to achieve the goal of simulating the underwater audio channel. The results of the trials demonstrate that these models are capable of accurately modeling the underwater acoustic communication channel. Furthermore, the findings suggest that deep learning models, particularly LSTM, are better models in terms of mean absolute percentage error. The vast majority of the currently available UWSN routing protocols use a classical routing strategy.

A Viterbi Decoder under Class A Modeled Noise in Shallow Water

A traditional Viterbi decoder is primarily optimized for additive white Gaussian noise (AWGN). With the AWGN channel, it offers good decoding performance. However, the underwater acoustic communication (UAC) channel is extremely complicated. In addition to white noise, there are a variety of artificial and natural impulse noise that occur suddenly. The traditional Viterbi decoder cannot obtain the optimum performance under this case. In order to solve this problem, this paper introduces a novel Viterbi decoder with the impulsive noise, which is considered to be subjected to Middleton Class A distribution in shallow ocean. Since Middleton Class A noise is very complicated, a simplified model is first introduced. Then, the error analysis of simplified model under various parameters is discussed in detail. The analysis shows that the simplified one just leads to slight error. Hereafter, a novel Veterbi decoder using the simplified model is discussed. Compared to a traditional decoder, a preprocessing is just required. The performance of soft decision-based decoder in the Middleton Class A noise channel (MAIN) and AWGN are further compared. Based on our simulations, the new decoder can significantly improve the performance in comparison with conventional one, which further validates our presented method.

Multi-block Sparse Bayesian learning channel estimation for OFDM underwater acoustic communication based on fractional Fourier transform

In OFDM underwater acoustic communication, according to the sparse characteristics of underwater acoustic communication channel in the time domain, sparse Bayesian learning (SBL) is introduced to make the channel estimation. In order to reduce the use of pilots and improve the transmission efficiency, a sparse pilot structure is adopted, and a multi-blocks sparse Bayesian learning (MBSBL) channel estimation algorithm is proposed. Aiming at the problem of insufficient channel estimation of MBSBL algorithm in the condition of few pilots, the fractional Fourier transform is introduced to compensate the Doppler frequency shift, which further improves the channel estimation performance and output signal-to-noise ratio (SNR). Besides, the complexities of Orthogonal Matching Pursuit (OMP), SBL and MBSBL algorithms are summarized briefly. Simulation and sea trial data show that the proposed algorithm can save half the number of pilots and the channel estimation effect of proposed algorithm is better than that of OMP algorithm and SBL algorithm.

Partial Fractional Fourier Transform (PFrFT)-MIMO-OFDM for Known Underwater Acoustic Communication Channels

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.

Experimental results on acoustic communication channels in pipelines

Transmission of information and data through pipelines using acoustic waves is a useful and practical approach in remote sensing, telemetry and monitoring applications. The data to be transmitted is first modulated by an actuator on acoustic waves, to travel through the body of the pipeline. At the receiver side, strain sensors and triaxial accelerometers can be used to receive and demodulate the data [E. Zhang and A. Abdi, “Communication rate increase in drill strings of oil and gas wells using multiple actuators,” Sensors 19 1337 (2019)]. Understanding the characteristics of the strain and acceleration communication channels in pipelines is of interest, for effective communication and telemetry system design. In this paper, we use the data measured by the strain and triaxial accelerometer sensors, to study several essential communication characteristics of the strain and acceleration channels in pipes, such as frequency responses, possible correlations among different channels, and signal-to-noise ratios of different channels. Results of this study are useful for proper design and optimization of acoustic communication and telemetry systems operating through pipelines. [The work is supported in part by NSF.]

Study of probe signal bandwidth influence on estimation of coherence bandwidth for underwater acoustic communication channel

A signal transmitted in a shallow Underwater Acoustic Communication (UAC) channel suffers from time dispersion due to the multipath propagation and the refraction phenomena. This causes intersymbol interference of the received signal and frequency-selective fading observed in its spectrum. Coherence bandwidth is one of the key transmission parameters used for designing the physical layer of a data transmission system to minimise the influence of time dispersion on the received signal. It can be calculated on the basis of the channel impulse response, measured with the use of the correlation method and frequency modulated signals or pseudorandom binary sequences. Such signals have a narrow, impulse-like autocorrelation function if considered in baseband. However, in the case of bandpass measurements, the influence of the probe signal on the estimate of the impulse response, and thus on the estimate of transmission parameters, is no longer negligible. The paper presents the results of an experimental study on probe signal bandwidth influence on estimation of coherence bandwidth. Simulations were carried out using UAC channel impulse responses measured in an inland reservoir.

Analytical Performance Evaluation of GFDM in Underwater Acoustic Communication Systems

The rapid growth of the Internet of Things (IoT) has extended its concept to underwater environments. However, the implementation of these systems via wired communication still represents a technological challenge, mainly due to the high cost of their deployment. Therefore, wireless communications are seen as an interesting solution for the deployment of underwater communications systems. Preliminary research indicated that underwater acoustic wireless communication could be used for some Internet of Underwater Things applications, mainly due to the wide range of communications involved. However, a significant disadvantage of acoustic systems is their low transmission data rate; thus, studies and analyses to improve this disadvantage must be carried out. Considering that new waveforms have been proposed to improve the performance of terrestrial wireless communications systems, this work presents the development of general analytical expressions that allow the performance evaluation of the Generalized Frequency Division Multiplexing (GFDM) waveform in underwater environments. These analytical expressions were obtained considering a continuous-time model for the GFDM signal and modeling the underwater acoustic communication channel as a time-varying multipath channel. Numerical results were obtained for many different systems and channel parameters, allowing a quantitative evaluation of the system performance degradation.