LAUSR

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 $\rm 6^{th}$ generation (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. To this end, most of the emerging SC works focus on specific data types and employ sophisticated machine learning models including deep learning and neural networks. However, they could be impractical for multi-modal data possibly within a real-time constraint, relative to the purpose of the communication. 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 hashing-based semantic extraction approach to 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.