Semantic communication with memory
File(s)
Published version
Author(s)
Xie, Huiqiang
Qin, Zhijin
Li, Geoffrey Ye
Type
Journal Article
Abstract
While semantic communication succeeds in efficiently transmitting due to the strong capability to extract the essential semantic information, it is still far from the intelligent or human-like communications. In this paper, we introduce an essential component, memory, into semantic communications to mimic human communications. Particularly, we investigate a deep learning (DL) based semantic communication system with memory, named Mem-DeepSC, by considering the scenario question answer task. We exploit the universal Transformer based transceiver to extract the semantic information and introduce the memory module to process the context information. Moreover, we derive the relationship between the length of semantic signal and the channel noise to validate the possibility of dynamic transmission. Specially, we propose two dynamic transmission methods to enhance the transmission reliability as well as to reduce the communication overheads by masking some unessential elements, which are recognized through training the model with mutual information. Numerical results show that the proposed Mem-DeepSC is superior to benchmarks in terms of answer accuracy and transmission efficiency, i.e., number of transmitted symbols.
Date Issued
2023-08-01
Date Acceptance
2023-05-03
Citation
IEEE Journal on Selected Areas in Communications, 2023, 41 (8), pp.2658-2669
ISSN
0733-8716
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
2658
End Page
2669
Journal / Book Title
IEEE Journal on Selected Areas in Communications
Volume
41
Issue
8
Copyright Statement
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Publication Status
Published
Date Publish Online
2023-06-21