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％。

深度联合源通道编码用于高效可靠的跨技术通信

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

This paper studies the notion of age in task-oriented communications that
aims to execute a task at a receiver utilizing the data at its transmitter. The
transmitter-receiver operations are modeled as an encoder-decoder pair that is
jointly trained while considering channel effects. The encoder converts data
samples into feature vectors of small dimension and transmits them with a small
number of channel uses thereby reducing the number of transmissions and
latency. Instead of reconstructing input samples, the decoder performs a task,
e.g., classification, on the received signals. Applying different deep neural
networks of encoder-decoder pairs on MNIST and CIFAR-10 image datasets, the
classifier accuracy is shown to increase with the number of channel uses at the
expense of longer service time. The peak age of task information (PAoTI) is
introduced to analyze this accuracy-latency tradeoff when the age grows unless
a received signal is classified correctly. By incorporating channel and traffic
effects, design guidelines are obtained for task-oriented communications by
characterizing how the PAoTI first decreases and then increases with the number
of channel uses. A dynamic update mechanism is presented to adapt the number of
channel uses to channel and traffic conditions, and reduce the PAoTI in
task-oriented communications.

研究了任务导向通信中年龄的概念，使用编码器 - 解码器对进行数据传输和任务执行，在不同深度神经网络上测试，提出了任务信息的峰值年龄概念，并通过调整通道使用数量来降低这一因素。