  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.
