Abstract
In this paper, we investigate an accurate synchronization between a physical
network and its digital network twin (DNT), which serves as a virtual
representation of the physical network. The considered network includes a set
of base stations (BSs) that must allocate its limited spectrum resources to
serve a set of users while also transmitting its partially observed physical
network information to a cloud server to generate the DNT. Since the DNT can
predict the physical network status based on its historical status, the BSs may
not need to send their physical network information at each time slot, allowing
them to conserve spectrum resources to serve the users. However, if the DNT
does not receive the physical network information of the BSs over a large time
period, the DNT's accuracy in representing the physical network may degrade. To
this end, each BS must decide when to send the physical network information to
the cloud server to update the DNT, while also determining the spectrum
resource allocation policy for both DNT synchronization and serving the users.
We formulate this resource allocation task as an optimization problem, aiming
to maximize the total data rate of all users while minimizing the
asynchronization between the physical network and the DNT. To address this
problem, we propose a method based on the GRUs and the value decomposition
network (VDN). Simulation results show that our GRU and VDN based algorithm
improves the weighted sum of data rates and the similarity between the status
of the DNT and the physical network by up to 28.96%, compared to a baseline
method combining GRU with the independent Q learning.