Deep Joint Source-Channel Coding for Efficient and Reliable Cross-Technology Communication

TL;DR

This paper introduces a deep joint source-channel coding scheme that significantly improves the efficiency and reliability of cross-technology communication by compressing messages and preserving their semantic integrity across incompatible wireless systems.

Contribution

It proposes a neural-network-based encoder-decoder framework that incorporates existing CTC algorithms and shared semantic knowledge to enhance cross-technology communication efficiency and robustness.

Findings

Reduces transmission overhead by up to 97.63%.

Increases similarity index measure by up to 734.78%.

Improves reliability and efficiency of CTC systems.

Abstract

Cross-technology communication (CTC) is a promising technique that enables direct communications among incompatible wireless technologies without needing hardware modification. However, it has not been widely adopted in real-world applications due to its inefficiency and unreliability. To address this issue, this paper proposes a deep joint source-channel coding (DJSCC) scheme to enable efficient and reliable CTC. The proposed scheme builds a neural-network-based encoder and decoder at the sender side and the receiver side, respectively, to achieve two critical tasks simultaneously: 1) compressing the messages to the point where only their essential semantic meanings are preserved; 2) ensuring the robustness of the semantic meanings when they are transmitted across incompatible technologies. The scheme incorporates existing CTC coding algorithms as domain knowledge to guide the encoder-decoder pair to learn the characteristics of CTC links better. Moreover, the scheme constructs shared semantic knowledge for the encoder and decoder, allowing semantic meanings to be converted into very few bits for cross-technology transmissions, thus further improving the efficiency of CTC. Extensive simulations verify that the proposed scheme can reduce the transmission overhead by up to 97.63\% and increase the structural similarity index measure by up to 734.78%, compared with the state-of-the-art CTC scheme.