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

Current object re-identification (ReID) system follows the centralized
processing paradigm, i.e., all computations are conducted in the cloud server
and edge devices are only used to capture and send images. As the number of
videos experiences a rapid escalation, this paradigm has become impractical due
to the finite computational resources. In such a scenario, the ReID system
should be converted to fit in the cloud-edge collaborative processing paradigm,
which is crucial to boost the scalability and practicality of ReID systems.
However, current relevant work lacks research on this issue, making it
challenging for ReID methods to be adapted effectively. Therefore, we pioneer a
cloud-edge collaborative inference framework for ReID systems and particularly
propose a distribution-aware correlation modeling network (DaCM) to make the
desired image return to the cloud server as soon as possible via learning to
model the spatial-temporal correlations among instances. DaCM embeds the
spatial-temporal correlations implicitly included in the timestamps into a
graph structure, and it can be applied in the cloud to regulate the size of the
upload window and on the edge device to adjust the sequence of images,
respectively. Traditional ReID methods can be combined with DaCM seamlessly,
enabling their application within our proposed edge-cloud collaborative
framework. Extensive experiments demonstrate that our method obviously reduces
transmission overhead and significantly improves performance. We will release
our code and model.

通过云 - 边缘协作处理范例，我们提出了一种分布感知的相关建模网络（DaCM），用于重新识别系统，将希望的图像尽快返回云服务器，通过学习建模实例之间的时空关联性。我们的方法明显减少传输开销并显著提高性能。