  In Shannon theory, semantic aspects of communication were identified but
considered irrelevant to the technical communication problems. Semantic
communication (SC) techniques have recently attracted renewed research
interests in (6G) wireless because they have the capability to support an
efficient interpretation of the significance and meaning intended by a sender
(or accomplishment of the goal) when dealing with multi-modal data such as
videos, images, audio, text messages, and so on, which would be the case for
various applications such as intelligent transportation systems where each
autonomous vehicle needs to deal with real-time videos and data from a number
of sensors including radars. A notable difficulty of existing SC frameworks
lies in handling the discrete constraints imposed on the pursued semantic
coding and its interaction with the independent knowledge base, which makes
reliable semantic extraction extremely challenging. Therefore, we develop a new
lightweight hashing-based semantic extraction approach to the SC framework,
where our learning objective is to generate one-time signatures (hash codes)
using supervised learning for low latency, secure and efficient management of
the SC dynamics. We first evaluate the proposed semantic extraction framework
over large image data sets, extend it with domain adaptive hashing and then
demonstrate the effectiveness of "semantics signature" in bulk transmission and
multi-modal data.
