Deep Joint Source-Channel Coding for Efficient and Reliable
  Cross-Technology Communication
Shumin Yao Xiaodong Xu Hao Chen Yaping Sun Qinglin Zhao
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.