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

Shumin Yao, Xiaodong Xu, Hao Chen, Yaping Sun, Qinglin Zhao

TL;DR

This work tackles cross-technology communication (CTC) by addressing efficiency and reliability through a deep joint source-channel coding framework. It introduces a DJSC encoder/decoder paired with a shared semantic-CTC knowledge base to compress messages into semantic meanings and protect them across heterogeneous links, leveraging existing CTC coding algorithms. Key contributions include the first DJSCC design for CTC, a dual-knowledge KB (semantic vectors and CTC algorithms), and a training framework that jointly optimizes semantic representations with end-to-end decoding. Simulations demonstrate substantial reductions in transmission overhead and large gains in perceptual fidelity, signaling strong potential for practical CTC deployment.

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.

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

TL;DR

This work tackles cross-technology communication (CTC) by addressing efficiency and reliability through a deep joint source-channel coding framework. It introduces a DJSC encoder/decoder paired with a shared semantic-CTC knowledge base to compress messages into semantic meanings and protect them across heterogeneous links, leveraging existing CTC coding algorithms. Key contributions include the first DJSCC design for CTC, a dual-knowledge KB (semantic vectors and CTC algorithms), and a training framework that jointly optimizes semantic representations with end-to-end decoding. Simulations demonstrate substantial reductions in transmission overhead and large gains in perceptual fidelity, signaling strong potential for practical CTC deployment.

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.
Paper Structure (11 sections, 8 equations, 4 figures, 1 table)

This paper contains 11 sections, 8 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Proposed scheme
  3. Component design
  4. DJSC encoder
  5. DJSC decoder
  6. Knowledge base
  7. Encoding and decoding procedures
  8. Encoding
  9. Decoding
  10. Training challenges and solutions
  11. Performance evaluation

Figures (4)

  • Figure 1: Framework of proposed DJSCC scheme.
  • Figure 2: MSE under various SNR levels.
  • Figure 3: SSIM under various SNR levels.
  • Figure 4: Number of bits per image transmission.