Cross-technology communication(CTC) enables seamless interactions between
diverse wireless technologies. Most existing work is based on reversing the
transmission path to identify the appropriate payload to generate the waveform
that the target devices can recognize. However, this method suffers from many
limitations, including dependency on specific technologies and the necessity
for intricate algorithms to mitigate distortion. In this work, we present
NNCTC, a Neural-Network-based Cross-Technology Communication framework inspired
by the adaptability of trainable neural models in wireless communications. By
converting signal processing components within the CTC pipeline into neural
models, the NNCTC is designed for end-to-end training without requiring labeled
data. This enables the NNCTC system to autonomously derive the optimal CTC
payload, which significantly eases the development complexity and showcases the
scalability potential for various CTC links. Particularly, we construct a CTC
system from Wi-Fi to ZigBee. The NNCTC system outperforms the well-recognized
WEBee and WIDE design in error performance, achieving an average packet
reception rate(PRR) of 92.3% and an average symbol error rate(SER) as low as
1.3%.

基于神经网络的跨技术通信系统（NNCTC）通过将 CTC 管道中的信号处理组件转化为神经模型，实现了无需标记数据的端到端训练，从而使开发复杂性大大降低，并展示了各种 CTC 链接的可扩展性潜力。NNCTC 系统在错误性能上优于 WEBee 和 WIDE 设计，其平均分组接收率 (PRR) 达到 92.3％，平均符号错误率 (SER) 仅为 1.3％。

NNCTC：基于神经网络的物理层跨技术通信

NNCTC: Physical Layer Cross-Technology Communication via Neural Networks

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.

本论文提出了一种深度联合源 - 信道编码方案，以实现高效可靠的跨技术通信，通过利用现有的 CTC 编码算法作为领域知识来指导编码解码器对 CTC 链路特性进行更好的学习，进一步提高了 CTC 的效率，通过广泛的仿真验证，本方案与现有的 CTC 方案相比，可以将传输开销减少高达 97.63％，结构相似性指数提高了 734.78％。