  Today, wireless networks are becoming responsible for serving intelligent
applications, such as extended reality and metaverse, holographic telepresence,
autonomous transportation, and collaborative robots. Although current
fifth-generation (5G) networks can provide high data rates in terms of
Gigabytes/second, they cannot cope with the high demands of the aforementioned
applications, especially in terms of the size of the high-quality live videos
and images that need to be communicated in real-time. Therefore, with the help
of artificial intelligence (AI)-based future sixth-generation (6G) networks,
the semantic communication concept can provide the services demanded by these
applications. Unlike Shannon's classical information theory, semantic
communication urges the use of the semantics (meaningful contents) of the data
in designing more efficient data communication schemes. Hence, in this paper,
we model semantic communication as an energy minimization framework in
heterogeneous wireless networks with respect to delay and quality-of-service
constraints. Then, we propose a sub-optimal solution to the NP-hard
combinatorial mixed-integer nonlinear programming problem (MINLP) by utilizing
efficient techniques such as discrete optimization variables' relaxation. In
addition, AI-based autoencoder and classifier are trained and deployed to
perform semantic extraction, reconstruction, and classification services.
Finally, we compare our proposed sub-optimal solution with different
state-of-the-art methods, and the obtained results demonstrate its superiority.
