Knowledge-Aware Semantic Communication System Design and Data Allocation

TL;DR

This paper introduces a semantic communication system that transmits only relevant keywords using an auto-encoder, optimizing data allocation among users with different dataset categories, and demonstrates its advantages over existing methods.

Contribution

It proposes a novel semantic communication system design with an auto-encoder for keyword transmission and formulates a data allocation problem, providing a greedy solution with near-optimal performance.

Findings

The SemCom system outperforms state-of-the-art in sentence accuracy for given word count.

The greedy algorithm for data allocation performs close to the optimal solution.

Simulations validate the effectiveness of the proposed methods.

Abstract

The recent emergence of 6G raises the challenge of increasing the transmission data rate even further in order to overcome the Shannon limit. Traditional communication methods fall short of the 6G goals, paving the way for Semantic Communication (SemCom) systems that have applications in the metaverse, healthcare, economics, etc. In SemCom systems, only the relevant keywords from the data are extracted and used for transmission. In this paper, we design an auto-encoder and auto-decoder that only transmit these keywords and, respectively, recover the data using the received keywords and the shared knowledge. This SemCom system is used in a setup in which the receiver allocates various categories of the same dataset collected from the transmitter, which differ in size and accuracy, to a number of users. This scenario is formulated using an optimization problem called the data allocation problem (DAP). We show that it is NP-complete and propose a greedy algorithm to solve it. Using simulations, we show that the proposed methods for SemCom system design outperform state-of-the-art methods in terms of average number of words per sentence for a given accuracy, and that the proposed greedy algorithm solution of the DAP performs significantly close to the optimal solution.