TY - GEN
T1 - Background Knowledge Aware Semantic Coding Model Selection
AU - Zhao, Fangzhou
AU - Sun, Yao
AU - Cheng, Runze
AU - Imran, Muhammad Ali
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Semantic communication is deemed to break Shannon channel capacity by transmitting extracted semantics rather than all binary bits. One critical challenge in semantic communication system is how to select a matching semantic coding model (SCM) in light of complicated source information, diversified user background knowledge (BK) and dynamic wireless channel. In this paper, we mathematically model the relationship among different BKs by using graph theory, and introduce a metric to evaluate SCMs performance as per BK relationships. Then, we propose a Background knowledge Aware SCM SElection (BASE) scheme, where a deep learning algorithm is exploited to accurately predict SCM performance in context of the modeled BK, guiding the SCM selection. Numerical simulation results show that the BASE has superiorities in information recovery accuracy along with the probability of selecting the optimal SCM when compared with other benchmarks.
AB - Semantic communication is deemed to break Shannon channel capacity by transmitting extracted semantics rather than all binary bits. One critical challenge in semantic communication system is how to select a matching semantic coding model (SCM) in light of complicated source information, diversified user background knowledge (BK) and dynamic wireless channel. In this paper, we mathematically model the relationship among different BKs by using graph theory, and introduce a metric to evaluate SCMs performance as per BK relationships. Then, we propose a Background knowledge Aware SCM SElection (BASE) scheme, where a deep learning algorithm is exploited to accurately predict SCM performance in context of the modeled BK, guiding the SCM selection. Numerical simulation results show that the BASE has superiorities in information recovery accuracy along with the probability of selecting the optimal SCM when compared with other benchmarks.
KW - Background knowledge
KW - Deep learning
KW - Semantic coding model selection
KW - Semantic communication
UR - https://www.scopus.com/pages/publications/85152246177
U2 - 10.1109/ICCT56141.2022.10072458
DO - 10.1109/ICCT56141.2022.10072458
M3 - Conference contribution
AN - SCOPUS:85152246177
T3 - International Conference on Communication Technology Proceedings, ICCT
SP - 84
EP - 89
BT - 2022 IEEE 22nd International Conference on Communication Technology, ICCT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Communication Technology, ICCT 2022
Y2 - 11 November 2022 through 14 November 2022
ER -