The proliferation of wireless communications networks over the past decades, combined with the scarcity of the wireless spectrum, have motivated a significant effort towards increasing the throughput of wireless networks. One of the major factors which limits the throughput in wireless communications networks is the accuracy of the time synchronization between the nodes in the network, as a higher throughput requires higher synchronization accuracy. Existing time synchronization schemes, and particularly, methods based on pulse-coupled oscillators (PCOs), which are the focus of the current work, have the advantage of simple implementation and achieve high accuracy when the nodes are closely located, yet tend to achieve poor synchronization performance for distant nodes. In this study, we propose a robust PCO-based time synchronization algorithm which retains the simple structure of existing approaches while operating reliably and converging quickly for both distant and closely located nodes. This is achieved by augmenting PCO-based synchronization with deep learning tools that are trainable in a distributed manner, thus allowing the nodes to train their neural network component of the synchronization algorithm without requiring additional exchange of information or central coordination. The numerical results show that our proposed deep learning-aided scheme is notably robust to propagation delays resulting from deployments over large areas, and to relative clock frequency offsets. It is also shown that the proposed approach rapidly attains full (i.e., clock frequency and phase) synchronization for all nodes in the wireless network, while the classic model-based implementation does not.
翻译:在过去几十年中,无线通信网络的扩散,加上无线频谱的缺乏,促使人们大力努力增加无线网络的输送量。限制无线通信网络输送量的主要因素之一是网络节点之间时间同步的准确性,因为更高的传输量需要更高的同步性。现有的时间同步计划,特别是以脉冲组合振动器为基础的方法(PCOs)是当前工作的重点,在节点位置接近时,可以简单实施并实现高精确度,但往往会为遥远的节点实现低同步性能。在本研究中,我们建议采用基于PCO的强健时间同步算法,保留现有方法的简单结构,同时可靠地运行并快速凝聚到遥远和近距离的节点。这是通过加强基于PCO的同步方法与可以分布式培训的深层学习工具(PCO)的同步方法,从而使得节点能够在不需要更多的信息交流或中央协调的情况下培训其基于螺旋式的网络组件。数字结果显示,我们提议的深度学习周期同步性方法在快速进行,而整个时空档计划则显示,从大规模部署到整个时空档计划。