论文标题
物联网的精简版分布式语义通信系统
A Lite Distributed Semantic Communication System for Internet of Things
论文作者
论文摘要
深度学习（DL）的快速发展和图像Internet（IoT）的广泛应用使设备比以前更明智，并使他们能够执行更聪明的任务。但是，由于其有限的计算能力，任何物联网设备的训练和运行DL模型都具有挑战性。在本文中，我们考虑了一个IoT网络，其中云/边缘平台在IoT设备基于训练的模型中执行数据收集和传输时，云/边缘平台执行基于DL的语义通信（DEEPSC）模型培训和更新。为了使IoT设备负担得起，我们提出了一个基于DL的Lite分布式语义通信系统，名为L-Deepsc，用于较低的复杂性文本传输，其中从IoT设备到云/边缘的数据传输在语义级别起作用，以提高传输效率。特别是，通过修剪模型冗余并降低了重量分辨率，L-Deepsc对于IoT设备而言是负担得起的，并且物联网设备之间的模型重量传输所需的带宽显着降低。通过分析L-DeepSC训练期间褪色通道对前向传播和背部传播的影响，我们开发了通道状态信息（CSI）辅助训练处理，以减少褪色通道对传播的影响。同时，我们量身定制语义星座，以使其可在容量有限的IoT设备上实现。模拟表明，与传统方法相比，所提出的L-Deepsc在低信噪比（SNR）区域中实现了竞争性能。特别是，虽然它可以达到40倍的压缩率，而不会降低性能。
The rapid development of deep learning (DL) and widespread applications of Internet-of-Things (IoT) have made the devices smarter than before, and enabled them to perform more intelligent tasks. However, it is challenging for any IoT device to train and run DL models independently due to its limited computing capability. In this paper, we consider an IoT network where the cloud/edge platform performs the DL based semantic communication (DeepSC) model training and updating while IoT devices perform data collection and transmission based on the trained model. To make it affordable for IoT devices, we propose a lite distributed semantic communication system based on DL, named L-DeepSC, for text transmission with low complexity, where the data transmission from the IoT devices to the cloud/edge works at the semantic level to improve transmission efficiency. Particularly, by pruning the model redundancy and lowering the weight resolution, the L-DeepSC becomes affordable for IoT devices and the bandwidth required for model weight transmission between IoT devices and the cloud/edge is reduced significantly. Through analyzing the effects of fading channels in forward-propagation and back-propagation during the training of L-DeepSC, we develop a channel state information (CSI) aided training processing to decrease the effects of fading channels on transmission. Meanwhile, we tailor the semantic constellation to make it implementable on capacity-limited IoT devices. Simulation demonstrates that the proposed L-DeepSC achieves competitive performance compared with traditional methods, especially in the low signal-to-noise (SNR) region. In particular, while it can reach as large as 40x compression ratio without performance degradation.
