A Lite Distributed Semantic Communication System for Internet of Things

By Huiqiang Xie, Zhijin Qin. IEEE Journal on Selected Areas in Communications, 2020

486 citations recorded by the source corpus.

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 $40\times $ compression ratio without performance degradation.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Neurology

View Paper: https://www.semanticscholar.org/paper/329edf5f6e1547d096b64918b5c053eb20df7413

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Electrical and Electronic Engineering

In Neurology

Citations in the corpus

Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges (2022, unknown): “system, named L-DeepSC [74], can be used.”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “In a distributed IoE network, a lightweight DL-based semantic communication system was proposed in [228] by training semantic feature extraction and”
Toward Semantic Communications: Deep Learning-Based Image Semantic Coding (2022, background): “Recent works mainly focus on semantic communications for text/speech modalities [2], [3], [4], [5], and therefore can be categorize as text semantic communications(TSC).”
Task-Oriented Multi-User Semantic Communications (2021, background): “Moreover, Xie et al. [11] also proposed an environment-friendly semantic communication system, named L-DeepSC, for the capacity-limited devices.”
Semantic Communication Systems for Speech Transmission (2021, methodology): “Inspired by this, a lite distributed semantic communication system for text transmission, named L-DeepSC, has been proposed in [26] to address the challenge of IoT to perform the intelligent tasks by pruning parameters to reduce the size of the trained models as well as to reduce the communication c”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “For instance, edge DL approach has been developed in [58] to deliver low-latency semantic tasks (e.g., text messages) by learning the communication strategies in an end-to-end fashion based on JSCC.”
Semantic Communications: Principles and Challenges (2021, methodology): “The bilingual evaluation understudy (BLEU) score is a commonly used metric to measure the quality of text after machine translation [36], which has been exploited to measure semantic communication systems for text transmission [12], [14].”
A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges (2022, background): “Therefore, in [144], the authors propose a lightweight semantic communication system for low-complexity text transmission in IoT.”
Deep Learning Enabled Semantic Communication Systems (2020, methodology): “Following the concept of semantic communications proposed in this paper, we have developed L-DeepSC [36] and DeepSCS [37] for text and speech transmission.”
Federated Learning for 6G: Applications, Challenges, and Opportunities (2021, background): “For those applications, the intelligent communication depends on the background and interface language model [48].”