Multiple autonomous underwater vehicles (AUVs) integrating multi-agent reinforcement learning (MARL) have made remarkable achievement and widely utilized for underwater search and rescue missions. However, to perform collaborative multi-AUV search efficiently in harsh and communication-constrained marine environments, challenging issues need to be addressed, such as cold-start problem and poor collaborative information fusion. To deal with these challenges, this paper proposes a MACS scheme which integrates the reasoning capabilities of large language models (LLMs) into the MARL framework to solve the cold-start problem and facilitate efficient collaborative information fusion. In MACS, to alleviate the cold-start problem of MARL caused by the lack of prior knowledge, we design a LEMACS algorithm, which leverages LLMs to infer the initial Target Probability Map (TPM) from search tasks and underwater terrain information to accelerate the search process. Furthermore, to address low efficient data exchange and fusion issue under unstable channel, we propose a LLM-enhanced link selection algorithm LESCL which integrates TPM information and AUV link metrics to optimize the link selection procedure to enhance multi-AUV cooperative search information fusion. To validate the effectiveness of the proposed algorithms, we conduct extensive numerical simulations using open-source regional underwater terrain data, such as coral reef map dataset of Arizona State University (ASU) and the terrain data of the Dongsha Islands, and the simulation results indicate that MACS achieves a search success rate of up to 95% in emergency multi-AUV cooperative search missions. The code is available at https://github.com/SDUST-smartocean/MACS.

As a result of the development of the Internet of Underwater Things (IoUT), underwater connected devices generate a large volume of data with varying values and time sensitivity. Previous data collection strategies cannot accommodate the varying time requirements of various data types. To address the aforementioned issues, this article proposes a cooperative data collection method (MADC-DV) for multiple autonomous underwater vehicles (AUVs) based on local global deep$Q$learning (LG-DQN) and data value, which divides data into emergency and nonemergency and achieves hybrid data collection. First, the MAC protocol for communication between AUVs and clusters is designed to divide nonemergency data into high-value data and low-value data, with low-value data not needing to reply to ACK acknowledgment packets, thereby reducing the nonemergency data collection delay. Second, nonemergency data are collected cooperatively using multiple AUVs, and the LG-DQN approach is used to plan the paths for multiple AUV data collection in order to reduce the overall energy consumption of underwater wireless sensor networks (UWSNs). Finally, emergency data are collected using a multihop routing approach to assist in the collection. A routing method is proposed to compensate for the inability of AUVs to be applied to emergency data collection. The experimental results indicate that the method can improve the network life cycle by 18.7%, reduce the delay in the collection of nonemergency data by 40%, and reduce the delay in the collection of emergency data by 26.3%, thereby meeting the varying time requirements for different types of data.

Underwater wireless sensor network (UWSN) plays a vital role in the field of ocean development and exploration. Designing a routing protocol for UWSN is a great challenge due to the characteristics of short lifetime and high delay. This paper proposes a Q-learning based routing optimization algorithm for UWSN. Two reward functions are designed based on the average residual energy of network, integrating factors such as energy information, transmission delay and link success rate to better balance transmission quality and lifetime. In addition, a holding time mechanism for packet forwarding is developed according to the priority of nodes. The simulation results show that compared to DBR and QLFR algorithms, this algorithm can effectively reduce transmission delay and prolong network lifetime.

Resource allocation faces significant challenges due to the complexity of the underwater environment. To address the problem of bandwidth assignment and improve the underwater resource utilization efficiency, this article proposes a quantum-based deep Q-network resource allocation algorithm. First, this algorithm combines factors, such as signal-to-noise ratio and data amount to construct the state space, which can better disclose the interaction between learning and environment. It also designs a unique reward function, which can guide nodes to select appropriate bandwidth, thus improving the learning capability of the deep reinforcement learning model. Furthermore, this article constructs a hybrid network model based on trainable quantum circuits, which fully utilizes various quantum gate operations to process and analyze data, predict the corresponding Q-values for actions. Simulation results show that the algorithm can reduce packet loss ratio and blocking probability while improving network bandwidth utilization.

Automatic modulation recognition (AMR) technology enables receivers to automatically recognize the modulation type of the received signal for correct demodulation of the received data, but there are still many shortcomings to be addressed. To achieve accurate and efficient AMR, this paper proposes a data augmentation method for AMR, which can increase the amount of data by seven times and solve the problem of a small sample size more effectively than the existing methods. In addition, this paper proposes a concept of microscale, rationalizes the underwater acoustic signal into time series, and proposes a temporal feature extractor named One2Three block, which can extract temporal features of signals from three microscales. Finally, a spatial feature extractor named the Dual-Stream squeeze-and-excitation (SE) block is designed to abstract and synthesize more advanced spatial features for AMR. The recognition accuracy of the proposed method is verified with eight commonly used modulation modes in underwater acoustic communications on the datasets collected in the South China Sea and the Yellow Sea. The results show that the proposed method can achieve a recognition accuracy of 99% with a lower time and space complexity, and has high robustness to noisy data.