| Autor: |
Rongpeng LI, Bingyan WANG, Honggang ZHANG, Zhifeng ZHAO |
| Jazyk: |
čínština |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Tongxin xuebao, Vol 44, Pp 70-76 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1000-436X |
| DOI: |
10.11959/j.issn.1000-436x.2023106 |
| Popis: |
To address the problem that existing semantic communication do not make sufficient use of prior knowledge and have limited decoding capability at the receiver side, a knowledge enhanced semantic communication framework was proposed, in which the receiver could more actively utilize the prior knowledge in the knowledge base for semantic reasoning and decoding, without extra modifications to the neural network structure of the transmitter.Specifically, a transformer-based knowledge extractor was designed to find relevant factual triples for the received noisy signal.Extensive simulation results on the WebNLG dataset demonstrate that the proposed framework has significantly improved performance on the basis of knowledge graph enhanced decoding. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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