  Semantic-aware communication is a novel paradigm that draws inspiration from
human communication focusing on the delivery of the meaning of messages. It has
attracted significant interest recently due to its potential to improve the
efficiency and reliability of communication and enhance users' QoE. Most
existing works focus on transmitting and delivering the explicit semantic
meaning that can be directly identified from the source signal. This paper
investigates the implicit semantic-aware communication in which the hidden
information that cannot be directly observed from the source signal must be
recognized and interpreted by the intended users. To this end, a novel implicit
semantic-aware communication (iSAC) architecture is proposed for representing,
communicating, and interpreting the implicit semantic meaning between source
and destination users. A projection-based semantic encoder is proposed to
convert the high-dimensional graphical representation of explicit semantics
into a low-dimensional semantic constellation space for efficient physical
channel transmission. To enable the destination user to learn and imitate the
implicit semantic reasoning process of source user, a generative adversarial
imitation learning-based solution, called G-RML, is proposed. Different from
existing communication solutions, the source user in G-RML does not focus only
on sending as much of the useful messages as possible; but, instead, it tries
to guide the destination user to learn a reasoning mechanism to map any
observed explicit semantics to the corresponding implicit semantics that are
most relevant to the semantic meaning. Compared to the existing solutions, our
proposed G-RML requires much less communication and computational resources and
scales well to the scenarios involving the communication of rich semantic
meanings consisting of a large number of concepts and relations.
