arXiv subjects

Bingwen Huangfu

Publications and source records attributed to Bingwen Huangfu.

4 recordsLinked to original sources

arXiv2026

An Asynchronous Triggered MAC Protocol for Underwater Acoustic Networks

Time Division Multiple Access (TDMA)-based Medium Access Control (MAC) protocols have proven their practicality through extensive field trials in Underwater Acoustic Networks (UANs), attributable to their hardware compatibility and ease of implementation. In conventional TDMA-based MAC designs, channel access is typically organized using synchronized, fixed-length slots to mitigate contention and coordinate transmissions. However, this paradigm imposes significant clock synchronization overhead in UANs with long and variable propagation delays and struggles to improve scheduling flexibility. Although some protocols attempt to refine this slot paradigm (adjust the slot length to improve channel reuse efficiency or scheduling frequency), they are still constrained by the trade-off between channel utilization and scheduling complexity. To this end, this paper proposes AT-MAC, an Asynchronous Triggered MAC protocol that aims to achieve efficient and fair channel access through coordinated asynchronous scheduling. AT-MAC introduces a triggered slot paradigm without time synchronization, decoupling transmission scheduling from a rigid timeline and enabling asynchronous, variable-length slots to accommodate the long and diverse propagation delays. To power this slot paradigm, AT-MAC augments conventional Multi-Agent Deep Reinforcement Learning to handle asynchronous interaction, achieving coordinated channel access under partial observations. It further devises a load-aware fairness guard mechanism to enable network-wide fairness status inference solely through local overhearing, thereby guiding adaptive scheduling correction to maintain fairness. Field-reconstructed simulations and on-board inference benchmarking demonstrate the feasibility of AT-MAC. Extensive simulation results further demonstrate its consistent performance gains across the evaluated scenarios and traffic conditions.

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arXiv2026

Is DRL-based MAC Ready for Underwater Acoustic Networks? Exploring Its Practicality in Real Field Experiments

Medium Access Control (MAC) protocols rely on neighbor and environment information to design collision-free access rules for Underwater Acoustic Networks (UANs). Acquiring this information suffers from high communication overhead due to the unique underwater acoustic channel characteristics, such as long propagation delay, spatiotemporal variations in communication quality, and high attenuation. Deep Reinforcement Learning (DRL) is promising to circumvent the UANs' physical constraints and provide a low-overhead solution for underwater MAC protocols, since it can decide access rules based on real-time observation without extra information exchange. However, the unique underwater acoustic channel characteristics impose significant challenges on observation acquisition, training time, and the balance of multiple reward factors for DRL-based MAC protocols. Most existing methods remain at the theoretical level: (1) they design partial intelligent agents failing to achieve fully autonomous access; (2) they assume unreasonable simulation scenarios, weakening the effects of underwater acoustic channel characteristics on MAC protocols. To enhance the practicality of DRL-based MAC protocols, we first analyze the application challenges of DRL in UANs through real field experiments. Based on the above challenges, we propose a DRL-based MAC protocol that considers observation loss and balances multiple reward factors to achieve efficient Entire Autonomous access in the UAN (EA-MAC). To further explore the feasibility of DRL-based MAC protocols, we implement EA-MAC and other state-of-the-art protocols on underwater acoustic modems and evaluate their performance in real field experiments. Experimental results demonstrate that EA-MAC can adaptively determine the scheduling sequence for each node, enabling high-throughput and fair communication in a straightforward manner for UANs.

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arXiv2024

Aqua-Sim Fourth Generation: Towards General and Intelligent Simulation for Underwater Acoustic Networks

Simulators are essential to troubleshoot and optimize Underwater Acoustic Network (UAN) schemes (network protocols and communication technologies) before real field experiments. However, due to programming differences between the above two contents, most existing simulators concentrate on one while weakening the other, leading to non-generic simulations and biased performance results. Moreover, novel UAN schemes increasingly integrate Artificial Intelligence (AI) techniques, yet existing simulators lack support for necessary AI frameworks, failing to train and evaluate these intelligent methods. On the other hand, these novel schemes consider more UAN characteristics involving more complex parameter configurations, which also challenges simulators in flexibility and fineness. To keep abreast of advances in UANs, we propose the Fourth Generation (FG) ns-3-based simulator Aqua-Sim~FG, enhancing the general and intelligent simulation ability. On the basis of retaining previous generations' functions, we design a new general architecture, which is compatible with various programming languages, including MATLAB, C++, and Python. In this way, Aqua-Sim~FG provides a general environment to simulate communication technologies, network protocols, and AI models simultaneously. In addition, we expand six new features from node and communication levels by considering the latest UAN methods' requirements, which enhances the simulation flexibility and fineness of Aqua-Sim~FG. Experimental results show that Aqua-Sim~FG can simulate UANs' performance realistically, reflect intelligent methods' problems in real-ocean scenarios, and provide more effective troubleshooting and optimization for actual UANs. The basic simulator is available at https://github.com/JLU-smartocean/aqua-sim-fg.

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arXiv2024

A Digital Twin-based Intelligent Network Architecture for Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network Architecture (DTNA), enhancing UASNs' environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement learning framework (CMFD) and a task-oriented network slicing (TNSD). CMFD patches scarce real data and provides extensive DT data to accelerate AI model training. TNSD unifies heterogeneous tasks' demand extraction and efficiently provides comprehensive network status, improving the flexibility of multi-task scheduling algorithms. Finally, practical and simulation experiments verify the high fidelity of DT. Compared with the original UASN architecture, experiment results demonstrate that DTNA can: (i) improve the timeliness and robustness of resource allocation; (ii) greatly reduce the training time of AI algorithms; (iii) more rapidly obtain network status for multi-task scheduling at a low cost.

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