Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data

TL;DR

This paper introduces a neural network-based semantic communication system that enables image transmission with a task-unaware transmitter and adapts to dynamic data environments, maintaining high data and task performance.

Contribution

It proposes a novel system combining semantic coding and data adaptation networks to handle practical issues in semantic communication without shared background knowledge.

Findings

The system effectively adapts to different data distributions.

It maintains high data recovery accuracy.

It supports task execution without transmitter knowledge of the task.

Abstract

Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter. The actual observable data at the transmitter can also have non-identical distribution with the empirical data in the shared background knowledge library. To address these practical issues, this paper proposes a new neural network-based semantic communication system for image transmission, where the task is unaware at the transmitter and the data environment is dynamic. The system consists of two main parts, namely the semantic coding (SC) network and the data adaptation (DA) network. The SC network learns how to extract and transmit the semantic information using a receiver-leading training process. By using the domain adaptation technique from transfer learning, the DA network learns how to convert the data observed into a similar form of the empirical data that the SC network can process without retraining. Numerical experiments show that the proposed method can be adaptive to observable datasets while keeping high performance in terms of both data recovery and task execution.