Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference
Getu, Tilahun M.; Saad, Walid; Kaddoum, Georges; Bennis, Mehdi (2024-03-05)
IEEE
05.03.2024
T. M. Getu, W. Saad, G. Kaddoum and M. Bennis, "Performance Limits of a Deep Learning-Enabled Text Semantic Communication Under Interference," in IEEE Transactions on Wireless Communications, vol. 23, no. 8, pp. 10213-10228, Aug. 2024, doi: 10.1109/TWC.2024.3370497.
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202403262444
https://urn.fi/URN:NBN:fi:oulu-202403262444
Tiivistelmä
Abstract
Although deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler by minimizing irrelevant information transmission – minimizing power usage, bandwidth consumption, and transmission delay, its benefits can be limited by radio frequency interference (RFI) that causes substantial semantic noise. Such semantic noise’s impact can be alleviated using an interference-resistant and robust (IR 2 ) SemCom design, though no such design exists yet. To stimulate fundamental research on IR 2 SemCom, the performance limits of a popular text SemCom system named DeepSC are studied in the presence of (multi-interferer) RFI. By introducing a principled probabilistic framework for SemCom, we show that DeepSC produces semantically irrelevant sentences as the power of (multi-interferer) RFI gets very large. We also derive DeepSC’s practical limits and a lower bound on its outage probability under multi-interferer RFI, and propose a (generic) lifelong DL-based IR 2 SemCom system. We corroborate the derived limits with simulations and computer experiments, which also affirm the vulnerability of DeepSC to a wireless attack using RFI.
Although deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler by minimizing irrelevant information transmission – minimizing power usage, bandwidth consumption, and transmission delay, its benefits can be limited by radio frequency interference (RFI) that causes substantial semantic noise. Such semantic noise’s impact can be alleviated using an interference-resistant and robust (IR 2 ) SemCom design, though no such design exists yet. To stimulate fundamental research on IR 2 SemCom, the performance limits of a popular text SemCom system named DeepSC are studied in the presence of (multi-interferer) RFI. By introducing a principled probabilistic framework for SemCom, we show that DeepSC produces semantically irrelevant sentences as the power of (multi-interferer) RFI gets very large. We also derive DeepSC’s practical limits and a lower bound on its outage probability under multi-interferer RFI, and propose a (generic) lifelong DL-based IR 2 SemCom system. We corroborate the derived limits with simulations and computer experiments, which also affirm the vulnerability of DeepSC to a wireless attack using RFI.
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