  A digital twin (DT) leverages a virtual representation of the physical world,
along with communication (e.g., 6G), computing (e.g., edge computing), and
artificial intelligence (AI) technologies to enable many connected intelligence
services. In order to handle the large amounts of network data based on digital
twins (DTs), wireless systems can exploit the paradigm of semantic
communication (SC) for facilitating informed decision-making under strict
communication constraints by utilizing AI techniques such as causal reasoning.
In this paper, a novel framework called causal semantic communication (CSC) is
proposed for DT-based wireless systems. The CSC system is posed as an imitation
learning (IL) problem, where the transmitter, with access to optimal network
control policies using a DT, teaches the receiver using SC over a bandwidth
limited wireless channel how to improve its knowledge to perform optimal
control actions. The causal structure in the source data is extracted using
novel approaches from the framework of deep end-to-end causal inference,
thereby enabling the creation of a semantic representation that is causally
invariant, which in turn helps generalize the learned knowledge of the system
to unseen scenarios. The CSC decoder at the receiver is designed to extract and
estimate semantic information while ensuring high semantic reliability. The
receiver control policies, semantic decoder, and causal inference are
formulated as a bi-level optimization problem within a variational inference
framework. This problem is solved using a novel concept called network state
models, inspired from world models in generative AI, that faithfully represents
the environment dynamics leading to data generation. Simulation results
demonstrate that the proposed CSC system outperforms state-of-the-art SC
systems by achieving better semantic reliability and reduced semantic
representation.
