Deep Learning Enabled Semantic Communication Systems

TL;DR

This paper introduces DeepSC, a deep learning-based semantic communication system that leverages transformers and transfer learning to improve text transmission by focusing on meaning rather than bit errors, especially in noisy environments.

Contribution

The paper proposes a novel semantic communication system using deep learning and transformers, along with a new sentence similarity metric, enhancing robustness and efficiency over traditional methods.

Findings

DeepSC outperforms traditional systems in low SNR conditions.

DeepSC is more robust to channel variations.

Transfer learning accelerates model training and adaptation.

Abstract

Recently, deep learned enabled end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in the traditional communication systems, which make joint transceiver optimization possible. Powered by deep learning, natural language processing (NLP) has achieved great success in analyzing and understanding large amounts of language texts. Inspired by research results in both areas, we aim to providing a new view on communication systems from the semantic level. Particularly, we propose a deep learning based semantic communication system, named DeepSC, for text transmission. Based on the Transformer, the DeepSC aims at maximizing the system capacity and minimizing the semantic errors by recovering the meaning of sentences, rather than bit- or symbol-errors in traditional communications. Moreover, transfer learning is used to ensure the DeepSC applicable to different communication environments and to accelerate the model training process. To justify the performance of semantic communications accurately, we also initialize a new metric, named sentence similarity. Compared with the traditional communication system without considering semantic information exchange, the proposed DeepSC is more robust to channel variation and is able to achieve better performance, especially in the low signal-to-noise (SNR) regime, as demonstrated by the extensive simulation results.