  Recently, it was shown that a communication system could be represented as a
deep learning (DL) autoencoder. Inspired by this idea, we target the problem of
OFDM-based wireless cross-technology communication (CTC) where both
in-technology and CTC transmissions take place simultaneously. We propose
DeepCTC, a DL-based autoencoder approach allowing us to exploit DL for joint
optimization of transmitter and receivers for both in-technology as well as CTC
communication in an end-to-end manner. Different from classical CTC designs, we
can easily weight in-technology against CTC communication. Moreover, CTC
broadcasts can be efficiently realized even in the presence of heterogeneous
CTC receivers with diverse OFDM technologies. Our numerical analysis confirms
the feasibility of DeepCTC as both in-technology and CTC messages can be
decoded with sufficient low block error rate.
