Open Access

ARTICLE

DAUNet: Unsupervised Neural Network Based on Dual Attention for Clock Synchronization in Multi-Agent Wireless Ad Hoc Networks

Haihao He^1,2, Xianzhou Dong^1,*, Shuangshuang Wang¹, Chengzhang Zhu¹, Xiaotong Zhao^1,2

1 Institute of War, Academy of Military Science, Beijing, 100091, China
2 School of Graduate, Academy of Military Science, Beijing, 100091, China

* Corresponding Author: Xianzhou Dong. Email:

Computers, Materials & Continua 2026, 86(1), 1-23. https://doi.org/10.32604/cmc.2025.069513

Received 25 June 2025; Accepted 13 August 2025; Issue published 10 November 2025

Abstract

Clock synchronization has important applications in multi-agent collaboration (such as drone light shows, intelligent transportation systems, and game AI), group decision-making, and emergency rescue operations. Synchronization method based on pulse-coupled oscillators (PCOs) provides an effective solution for clock synchronization in wireless networks. However, the existing clock synchronization algorithms in multi-agent ad hoc networks are difficult to meet the requirements of high precision and high stability of synchronization clock in group cooperation. Hence, this paper constructs a network model, named DAUNet (unsupervised neural network based on dual attention), to enhance clock synchronization accuracy in multi-agent wireless ad hoc networks. Specifically, we design an unsupervised distributed neural network framework as the backbone, building upon classical PCO-based synchronization methods. This framework resolves issues such as prolonged time synchronization message exchange between nodes, difficulties in centralized node coordination, and challenges in distributed training. Furthermore, we introduce a dual-attention mechanism as the core module of DAUNet. By integrating a Multi-Head Attention module and a Gated Attention module, the model significantly improves information extraction capabilities while reducing computational complexity, effectively mitigating synchronization inaccuracies and instability in multi-agent ad hoc networks. To evaluate the effectiveness of the proposed model, comparative experiments and ablation studies were conducted against classical methods and existing deep learning models. The research results show that, compared with the deep learning networks based on DASA and LSTM, DAUNet can reduce the mean normalized phase difference (NPD) by 1 to 2 orders of magnitude. Compared with the attention models based on additive attention and self-attention mechanisms, the performance of DAUNet has improved by more than ten times. This study demonstrates DAUNet’s potential in advancing multi-agent ad hoc networking technologies.

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