Acoustic Communication Networks Research Articles

Space-code division multiple access for broadband acoustic networks

We present an investigation into the design of an acoustic communication network, where multiple users are distributed across space and transmit and receive simultaneously in the same band to and from a common base station. Specifically, we focus on a system that utilizes orthogonal frequency division multiplexing as a modulation method, and allows users to transmit and receive in either synchronous or asynchronous fashion. To distinguish between users on the uplink, the base station employs a combination of code-division and space-division multiple access. The base station iteratively steers a beam to each stable propagation path of the desired user’s channel while placing nulls in the direction of other paths, as well as in the directions of interfering users. Finally, the multiple paths of the desired user are recombined before data detection. The process is repeated for each user. Broadband beamforming is employed to account for the broadband nature of acoustic signals. The beamformer coefficients on each carrier depend on the angles of signal arrivals, which are estimated during the uplink transmission and used to construct both the uplink and the downlink beamformer. On the downlink, the base station utilizes angle information to assemble beamforming weights and point in the direction of stable paths of the users. It superimposes multiuser signals and transmits the sum signal to the users. The signal intended for a given user reaches only that user, requiring just a simple detector. Each user is equipped by a single-element transducer. To demonstrate the design concepts, we conducted simulations using a shallow water channel model and performed experimental over-the-air tests in an indoor environment using an acoustic communications testbed. The results were excellent, thereby encouraging future implementations in practical systems.

Distributed Deep Reinforcement Learning With Prioritized Replay for Power Allocation in Underwater Acoustic Communication Networks

Distributed Deep Reinforcement Learning With Prioritized Replay for Power Allocation in Underwater Acoustic Communication Networks

Ocean-Mixer: A Deep Learning Approach for Multi-Step Prediction of Ocean Remote Sensing Data

The development of intelligent oceans requires exploration and an understanding of the various characteristics of the oceans. The emerging Internet of Underwater Things (IoUT) is an extension of the Internet of Things (IoT) to underwater environments, and the ability of IoUT to be combined with deep learning technologies is a powerful technology for realizing intelligent oceans. The underwater acoustic (UWA) communication network is essential to IoUT. The thermocline with drastic temperature and density variations can significantly limit the connectivity and communication performance between IoUT nodes. To more accurately capture the complexity and variability of ocean remote sensing data, we first sample and analyze ocean remote sensing datasets and provide sufficient evidence to validate the temporal redundancy properties of the data. We propose an innovative deep learning approach called Ocean-Mixer. This approach consists of three modules: an embedding module, a mixer module, and a prediction module. The embedding module first processes the location and attribute information of the ocean water and then passes it to the subsequent modules. In the mixing module, we apply a temporal decomposition strategy to eliminate redundant information and capture temporal and channel features through a self-attention mechanism and a multilayer perceptron (MLP). The prediction module ultimately discerns and integrates the temporal and channel relationships and interactions among various ocean features, ensuring precise forecasting. Numerous experiments on ocean temperature and salinity datasets show that Mixer-Ocean performs well in improving the accuracy of time series prediction. Mixer-Ocean is designed to support multi-step prediction and capture the changes in the ocean environment over a long period, thus facilitating efficient management and timely decision-making for innovative ocean-oriented applications, which has far-reaching significance for developing and conserving marine resources.

Multi-Agent Transfer Reinforcement Learning for Resource Management in Underwater Acoustic Communication Networks

Multi-Agent Transfer Reinforcement Learning for Resource Management in Underwater Acoustic Communication Networks

Power Allocation Based on Multi-Agent Deep Deterministic Policy Gradient for Underwater Acoustic Communication Networks

This paper proposes a reinforcement learning-based power allocation for underwater acoustic communication networks (UACNs). The objective function is formulated as maximizing channel capacity under constraints of maximum power and minimum channel capacity. To solve this problem, a multi-agent deep deterministic policy gradient (MADDPG) approach is introduced, where each transmitter node is considered as an agent. Given the definition of a Markov decision process (MDP) model for this problem, the agents learn to collaboratively maximize the channel capacity by deep deterministic policy gradient (DDPG) learning. Specifically, the power allocation of each agent is obtained by a centralized training and distributed execution (CTDE) method. Simulation results show the sum rate achieved by the proposed algorithm approximates that of the fractional programming (FP) algorithm and improves by at least 5% compared with the DQN (deep Q-learning network) -based power allocation algorithm.

Exploiting Propagation Delay in Underwater Acoustic Communication Networks via Deep Reinforcement Learning.

This article proposes a novel deep-reinforcement learning-based medium access control (DL-MAC) protocol for underwater acoustic networks (UANs) where one agent node employing the proposed DL-MAC protocol coexists with other nodes employing traditional protocols, such as time division multiple access (TDMA) or q -Aloha. The DL-MAC agent learns to exploit the large propagation delays inherent in underwater acoustic communications to improve system throughput by either a synchronous or an asynchronous transmission mode. In the sync-DL-MAC protocol, the agent action space is transmission or no transmission, while in the async-DL-MAC, the agent can also vary the start time in each transmission time slot to further exploit the spatiotemporal uncertainty of the UANs. The deep Q -learning algorithm is applied to both sync-DL-MAC and async-DL-MAC agents to learn the optimal policies. A theoretical analysis and computer simulations demonstrate the performance gain obtained by both DL-MAC protocols. The async-DL-MAC protocol outperforms the sync-DL-MAC protocol significantly in sum throughput and packet success rate by adjusting the transmission start time and reducing the length of time slot.

Optimal Broadcast Scheduling Algorithm for a Multi-AUV Acoustic Communication Network

In systems of multiple autonomous underwater vehicles (AUVs), to achieve cooperative operation and cluster intelligence, information is often disseminated via broadcasting. However, due to the long propagation delay and slow transmission rate of underwater acoustic communication, traditional broadcast scheduling algorithms require a long broadcast period to avoid signal collision. To improve the channel utilization rate as much as possible and improve the update rate for broadcast information, we propose an optimal broadcast scheduling algorithm. This algorithm uses the location information of AUVs to adjust the broadcast sequence and broadcast schedule, to achieve the shortest possible collision-free broadcast period in the broadcast network for the current node distribution. Simulation experiments show that this algorithm can achieve a broadcast period much shorter than that of traditional TDMA and higher channel utilization without signal collision. In addition, the simulations prove the feasibility of applying the algorithm in an actual MAC protocol.

Spectrum-Weighted Fusion Cooperative Detection Algorithm Based on Double Thresholds for Underwater Acoustic Networks.

Spectrum-sensing technology is crucial for the development of underwater acoustic communication networks and plays a key role in detecting spectrum holes and channel occupancy. Energy detection technology, as the fundamental spectrum sensing technology in cognitive radio, has reached a mature level of development. Its application in hydroacoustic communications can significantly enhance the utilization of the hydroacoustic spectrum. However, due to the complexity of the hydroacoustic channel compared with that of the radio channel, the traditional double-threshold energy detection technique faces challenges such as fixed threshold values and limited flexibility. To address this, we propose a model for the hydroacoustic channel that incorporates a weight factor based on the signal-to-noise ratio in the algorithm. This allows for adaptive threshold values based on the user's signal-to-noise environment, reducing false detection rates and improving overall detection performance. Through simulation experiments and comparisons, our proposed signal-to-noise weighted collaborative spectrum-sensing technique demonstrates superior detection performance compared with other spectrum-sensing techniques.

Multi-objective Game Learning Algorithm Based on Multi-armed Bandit in Underwater Acoustic Communication Networks

Multi-objective Game Learning Algorithm Based on Multi-armed Bandit in Underwater Acoustic Communication Networks

Adaptive Scheduling MAC Protocol in Underwater Acoustic Broadcast Communications for AUV Formation

With the rapid development of autonomous underwater vehicles (AUVs) and the continuous improvement of marine exploration requirements, AUV formation has emerged as a promising technique for performing underwater tasks with greater flexibility, adaptability, and scalability. To achieve AUV formation control, underwater acoustic communication networks (UACNs) are widely used to support information exchange among AUVs. Considering broadcast communications in fully connected mobile UACNs, existing medium access control (MAC) protocols face severe packet collision risk or low reuse efficiency. To improve the timeliness and reliability of the information exchange among AUVs, we develop an efficient scheduling-based adaptive broadcasting MAC (AB-MAC) protocol. The AB-MAC protocol is incompletely centralized, which can be reflected in two aspects. First, to ensure the consistency of the transmission schedule in the network, the AB-MAC protocol selects a control node for each frame to schedule the transmission of all nodes according to the updated network topology. The slot lengths and transmission sequences are changeable in each frame to avoid packet collisions and minimize frame lengths. Second, to further improve the efficiency and robustness of the protocol, all nodes adaptively adjust their transmission time by integrating the information contained in their previously received control packets and ordinary packets. The transmission scheduling problem is formulated as a combinatorial optimization problem, which can be solved by our proposed improved Genetic Algorithm. Numerical results demonstrate that the AB-MAC protocol not only performs well in terms of network throughput and average update interval but also has a high level of robustness even in low-quality underwater acoustic channels.

Anthropogenic noise events perturb acoustic communication networks

Anthropogenic noise sources impact ecological processes by altering wildlife behavior and interactions with cascading impacts on community structure. The distribution and magnitude of such noise has grown exponentially over the past century, and now inundates even remote areas. Here we investigate biological responses to prolific, anthropogenic noise sources associated with the physical presence of the source (vehicle noise and human voices) and disconnected from it (aircraft overflight). Bioacoustic responses to these noise sources were documented at 103 sites in 40 U. S. National Park units. The presence of bird sounds was noted in 10-s audio samples every 2 min, for 8 days at each site and related to the presence of human voices, vehicle noise, and aircraft noise in the same and preceding samples. Generalized additive models were used to fit smoothing splines to weight the influence of noise in past samples on the probability of detecting bird sounds in the present sample. We found that the probability of hearing birds increased immediately following noise events, and decreased about 2 h after the event. The negative effects were persistent more than 3 h after a noise event. The persistence of these responses – especially for noise from jets that were many kilometers distant – raises questions about the functional significance and ecological consequences of this altered activity, particularly in light of the widespread and diverse habitats in this study and ubiquity of the noise sources evaluated.

Exploring propagation delay and mobility in underwater acoustic networks

This work investigates the impact of mobility on underwater acoustic communication networks where the propagation delay is comparable to or larger than the packet duration. A new simulator is proposed to implement the acoustic communication timing model when the nodes are mobile or static. Synchronous and asynchronous Medium Access Control (MAC) protocols are employed with ALOHA, TDMA (Time-Division Multiple Access), and Artificial Intelligent (AI) agent nodes. Extensive simulations are conducted to study the throughput of a MAC network where a large number of source nodes transmit to a single sink node. A network of 2–15 source nodes are assumed to be randomly located in a 1500 m cubic space and some of the nodes are moving in random directions at 2–4 m/s speed. The simulation results show that the asynchronous MAC protocols achieve much higher throughput than the synchronous protocols by allowing time slots to be much shorter than the maximum propagation delay among nodes and allowing asynchronous transmission time. High mobility of a few mobile nodes also favors asynchronous protocols and improves the network throughput in a manner similar to an AI agent.

Research on Underwater Wireless Acoustic Communication Routing Protocol

Due to the limitation of the communication range of sensor nodes, underwater nodes must transmit data to the water surface through routing mechanism. Therefore, the design of routing algorithm is related to the performance of the network, and the research of routing algorithm is necessary. However, underwater nodes are vulnerable to ocean turbulence and biological factors. The underwater environment is dynamic and complex, which greatly affects the acoustic channel, making the improvement of routing protocol a challenge. These factors lead to problems in underwater acoustic communication network, such as empty nodes, packet delivery rate, energy consumption, delay and other problems, and in the dynamic and complex seawater environment, the performance of acoustic network will be further reduced. This paper introduces some main routing algorithms, such as hollow node awareness algorithm, layer-based routing protocol; Opportunistic routing algorithm; Double-hop routing protocol, etc. These routing algorithms can solve some problems. Simulation shows that they have certain advantages in packet delivery rate, delay, network lifetime, etc.

Space/Frequency-Division-Based Full-Duplex Data Transmission Method for Multihop Underwater Acoustic Communication Networks

Underwater acoustic communication networks (UACNs) have been widely utilized in recent years because of the growing interest in interactive information in the deep ocean. Compared with traditional radio wireless networks, UACNs are characterized by complex and dynamic 3-D network topology and longer signal propagation delay. Therefore, recent studies on UACNs usually apply dynamic routes in data communication to adapt to complex UACN structure. However, the approaches are hardly adequate for UACN scenarios with high-traffic requirements. This is because dynamic routing methods, such as opportunistic routing, usually require external contention costs to control the routing paths, which leads to decreased network throughput. Accordingly, this article focuses on enabling high-speed acoustic communications for underwater application scenarios with high-traffic requirements, and proposes an underwater data transmission method using the multichannel full-duplex (FD) communication technique. The proposed method applies the underwater orthogonal frequency division multiple access (OFDM) technique, that uses collision-free channels to relay data in multihop routes for simultaneous transmission and reception. Unlike the traditional communication methods of UACNs with dynamic routing, the proposed one uses static routes when transporting data over hop-by-hop paths to achieve stable and uninterrupted FD communication. The approach also includes a directional forwarding-based route request method and an elimination-based channel evaluation proposal, which purpose to reduce the overheads from exploring routes and enable collision-free FD communications. The performance analysis of the proposed method is under simulated conditions of high-traffic UACN scenarios, and the results show that it has superior performance compare to the classical underwater data transmission methods.

Coverage Enhancement of Underwater Internet of Things Using Multilevel Acoustic Communication Networks

Underwater acoustic communication networks (UACNs) are considered a key enabler to the Underwater Internet of Things (UIoT). UACN is regarded as essential for various marine applications, such as monitoring, exploration, and trading. However, a large part of existing literature disregards the 3-D nature of the underwater communication system. In this article, we propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -tier UACN that acts as a gateway that connects the UIoT with the space–air–ground–sea integrated system (SAGSIS). The proposed network architecture consists of several tiers along the vertical direction with adjustable depths. On the horizontal dimension, the best coverage probability (CP) is computed and maximized by optimizing the densities of surface stations (SSs) in each tier. On the vertical dimension, the depth of each tier is also optimized to minimize intertier interference and maximize overall system performance. Using tools from stochastic geometry, the total CP of the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -tier network is analyzed. For given spatial distribution of UIoT device’s depth, the best CP can be achieved by optimizing the depths of the transceivers connected to the SSs through a tether. We verify the accuracy of the analysis using Monte Carlo simulations. In addition, we draw multiple useful system-level insights that help optimize the design of underwater 3-D networks based on the given distribution of UIoT device’s depths.

Multi-Node Joint Power Allocation Algorithm Based on Hierarchical Game Learning in Underwater Acoustic Sensor Networks

In order to improve the overall service quality of the network and reduce the level of network interference, power allocation has become one of the research focuses in the field of underwater acoustic communication in recent years. Aiming at the issue of power allocation when channel information is difficult to obtain in complex underwater acoustic communication networks, a completely distributed game learning algorithm is proposed that does not require any prior channel information and direct information exchange between nodes. Specifically, the power allocation problem is constructed as a multi-node multi-armed bandit (MAB) game model. Then, considering nodes as agents and multi-node networks as multi-agent networks, a power allocation algorithm based on a softmax-greedy action selection strategy is proposed. In order to improve the learning efficiency of the agent, reduce the learning cost, and mine the historical reward information, a learning algorithm based on the two-layer hierarchical game learning (HGL) strategy is further proposed. Finally, the simulation results show that the algorithm not only shows good convergence speed and stability but also can adapt to a harsh and complex network environment and has a certain tolerance for incomplete channel information acquisition.

Underwater Acoustic Micromodem for Underwater Internet of Things

Underwater acoustic communication (UAC) networks are becoming a hot spot for research due to the growing demand for ocean development. Previous acoustic modems were mainly designed for the field of ocean engineering construction and scientific investigation, and consequently, with the characteristics of large size and high-power consumption, a low-cost, low-power consuming micromodem is proposed in this paper to satisfy the requirements of underwater Internet of Things (IoT). The STM32F767 processor is embedded in the micromodem as the core for fast digital signal processing. Furthermore, a convolutional code–block interleaving–frequency hopping–MFSK underwater communication scheme is also discussed to ensure the communication quality. Experimental results are shown to demonstrate our modem can realize the reliable UAC transmission of 200~300 bit/s at the distance of 500 m.

Adaptive Formation Control of Multiple Underactuated Autonomous Underwater Vehicles

In this paper, we present a 3D formation control scheme for a group of torpedo-type underactuated autonomous underwater vehicles (AUVs). These multiple AUVs combined with an unmanned surface vessel (USV) construct a sort of star-topology acoustic communication network where the USV is at the center point. Due to this kind of topological feature, this paper applies a virtual school concept. This is a geometric graph where each node is taken as a virtual leader for each specific AUV and assigned its own reference trajectory. For each individual vehicle, its formation strategy is simple: just follow the trajectory of its corresponding virtual leader so as for multiple AUVs to compose the given formation. As for the formation subject, this paper mainly focuses on the formation tracking problem rather than the formation producing. For the torpedo-type vehicle considered in this paper, there are only three control inputs (surge force, pitch, and yaw moments) available for its underwater 3D motion and therefore this is a typical underactuated system. For the following vehicle’s trajectory, a sort of potential field method is used for obstacle avoidance, and a neural network-based adaptive scheme is applied to on-line approximate the vehicle’s unknown nonlinear dynamics, and the uncertainty terms including modeling errors, measurement noises, and external disturbances are handled by the properly designed robust scheme. The proposed formation method can guarantee the uniform ultimate boundedness (UUB) of the closed-loop system. Numerical studies are also carried out to verify the effectiveness of the proposed scheme.

Research on Multi-AUVs Data Acquisition System of Underwater Acoustic Communication Network.

In order to meet the needs of large-scale underwater operations, the underwater acoustic communication network emerged, marking a historic moment. At the same time, the development of artificial intelligence has promoted the application of intelligent underwater robots in large-scale underwater operations, and the research on related algorithms has been gradually promoted. Due to the complexity of underwater operations and the difficulty of replacing batteries, the energy efficiency of intelligent underwater robots is particularly important in multi-AUVs data acquisition systems. In view of the energy consumption of multi-AUVs data acquisition systems in water acoustic cluster networks, this paper proposed the AE (A*-Energy) algorithm for multi-AUVs task assignment and path planning. Through the simulation experiment, it was proved that the AE algorithm proposed in this paper can effectively reduce the energy consumption of multi-AUVs data acquisition systems and has good energy efficiency.

Collision classification MAC protocol for underwater acoustic communication networks using directional antennas

Collision classification MAC protocol for underwater acoustic communication networks using directional antennas