Semantics-Native Communication with Contextual Reasoning

Abstract

Spurred by a huge interest in the post-Shannon communication, it has recently been shown that leveraging semantics can significantly improve the communication effectiveness across many tasks. In this article, inspired by human communication, we propose a novel stochastic model of System 1 semantics-native communication (SNC) for generic tasks, where a speaker has an intention of referring to an entity, extracts the semantics, and communicates its symbolic representation to a target listener. To further reach its full potential, we additionally infuse contextual reasoning into SNC such that the speaker locally and iteratively self-communicates with a virtual agent built on the physical listener's unique way of coding its semantics, i.e., communication context. The resultant System 2 SNC allows the speaker to extract the most effective semantics for its listener. Leveraging the proposed stochastic model, we show that the reliability of System 2 SNC increases with the number of meaningful concepts, and derive the expected semantic representation (SR) bit length which quantifies the extracted effective semantics. It is also shown that System 2 SNC significantly reduces the SR length without compromising communication reliability.H. Seo and M. Bennis are with the Centre for Wireless Communications

Cognitive Semantic Communication Systems Driven by Knowledge Graph

“…However, there have been no uniform metrics for evaluating the achievable performance. Due to this fact, the first paradigm is restricted to some specific wireless services or communication environment [11]. The second paradigm relys on the large-scale deep learning (DL) models to accurately and quickly recognize and extract intended semantic information, and it does not require semantic metrics.…”

“…Moreover, it has the interpretability during the compression process due to the reasoning rules in the knowledge base [3]. The third paradigm is envisioned to be the most promising one [11]. However, there are few investigations that have focused on this direction since it requires the interdisciplinary knowledge, such as wireless communication, computer science, artificial intelligence, etc.…”

Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework

“…In [8] and [9], the authors investigated the notion of reasoning for semantic communications. The work in [8] developed a semantic reasoning system for a realistic Internet of things (IoT) network to enable the reasoning of actionable knowledge.…”

“…The work in [8] developed a semantic reasoning system for a realistic Internet of things (IoT) network to enable the reasoning of actionable knowledge. Meanwhile, the authors in [9] investigated a semanticnative communication structure for extracting the most effective semantics of the transmitter for the receiver. However, the works in [8] and [9] are limited in many ways.…”

“…Meanwhile, the authors in [9] investigated a semanticnative communication structure for extracting the most effective semantics of the transmitter for the receiver. However, the works in [8] and [9] are limited in many ways. First, they do not consider the effectiveness of the transmitted information for task execution purposes.…”