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

While deep learning based end-to-end automatic speech recognition (ASR)
systems have greatly simplified modeling pipelines, they suffer from the data
sparsity issue. In this work, we propose a self-training method with an
end-to-end system for semi-supervised ASR. Starting from a Connectionist
Temporal Classification (CTC) system trained on the supervised data, we
iteratively generate pseudo-labels on a mini-batch of unsupervised utterances
with the current model, and use the pseudo-labels to augment the supervised
data for immediate model update. Our method retains the simplicity of
end-to-end ASR systems, and can be seen as performing alternating optimization
over a well-defined learning objective. We also perform empirical
investigations of our method, regarding the effect of data augmentation,
decoding beamsize for pseudo-label generation, and freshness of pseudo-labels.
On a commonly used semi-supervised ASR setting with the WSJ corpus, our method
gives 14.4% relative WER improvement over a carefully-trained base system with
data augmentation, reducing the performance gap between the base system and the
oracle system by 50%.

本文提出了一种基于端到端系统的自学习半监督自动语音识别方法，通过对无监督数据的伪标记迭代地增强有标记的数据，从而提高系统性能，实现数据增强效果，获得了 14.4% 的相对识别错误率改进。