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23.11.2023 | Research

Deep Learning Enabled Task-Oriented Semantic Communication for Memory-Limited Devices

verfasst von: Hanmin Deng, Weiqi Wang, Min Liu

Erschienen in: Mobile Networks and Applications

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Abstract

In recent years, numerous achievements have been made in the field of deep learning, particularly in text processing. In the wave of intelligence, people’s demand for intelligent communication is becoming increasingly higher. Therefore, we consider utilizing deep learning models to design and optimize transceiver of semantic communication system. The research of semantic communication is in a booming stage, but there are still few applications in multi-user scenario. In general, the parameters of the semantic communication system transceiver based on the deep learning model are very large. Therefore, we study the multi-user semantic communication system based on the ALBERT model. The goal of the proposed semantic communication system is to intelligently and correctly send the corresponding text classification to the receiver. The channel state information (CSI) is very important for information transmission. Considering the multi-antenna multi-user uplink scenario, we adopt the conditional generative adversarial network (cGAN) model to estimate CSI and apply it to the proposed semantic communication system. In order to reduce the influence of channel estimation on the delay of communication system, we quantify the pilot at the receiver. The simulation results show that the performance of the semantic communication system proposed in this paper is better than that of the semantic communication system based on Transformer model and the traditional semantic communication system in the intelligent text classification task. Moreover, in the case of low signal-to-noise ratio, traditional communication is difficult to complete intelligent tasks.

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Metadaten
Titel
Deep Learning Enabled Task-Oriented Semantic Communication for Memory-Limited Devices
verfasst von
Hanmin Deng
Weiqi Wang
Min Liu
Publikationsdatum
23.11.2023
Verlag
Springer US
Erschienen in
Mobile Networks and Applications
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-023-02267-8