SemantIC: Semantic Interference Cancellation Toward 6G Wireless Communications
Lin, Wensheng; Yan, Yuna; Li, Lixin; Han, Zhu; Matsumoto, Tad (2024-06-11)
IEEE
11.06.2024
W. Lin, Y. Yan, L. Li, Z. Han and T. Matsumoto, "SemantIC: Semantic Interference Cancellation Toward 6G Wireless Communications," in IEEE Communications Letters, vol. 28, no. 8, pp. 1810-1814, Aug. 2024, doi: 10.1109/LCOMM.2024.3412973.
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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-202410026152
https://urn.fi/URN:NBN:fi:oulu-202410026152
Tiivistelmä
Abstract
This letter proposes a novel anti-interference technique, semantic interference cancellation (SemantIC), for enhancing information quality towards the sixth-generation (6G) wireless networks. SemantIC only requires the receiver to concatenate the channel decoder with a semantic auto-encoder. This constructs a turbo loop which iteratively and alternately eliminates noise in the signal domain and the semantic domain. From the viewpoint of network information theory, the neural network of the semantic auto-encoder stores side information by training, and provides side information in iterative decoding, as an implementation of the Wyner-Ziv theorem. Simulation results verify the performance improvement by SemantIC without extra channel resource cost.
This letter proposes a novel anti-interference technique, semantic interference cancellation (SemantIC), for enhancing information quality towards the sixth-generation (6G) wireless networks. SemantIC only requires the receiver to concatenate the channel decoder with a semantic auto-encoder. This constructs a turbo loop which iteratively and alternately eliminates noise in the signal domain and the semantic domain. From the viewpoint of network information theory, the neural network of the semantic auto-encoder stores side information by training, and provides side information in iterative decoding, as an implementation of the Wyner-Ziv theorem. Simulation results verify the performance improvement by SemantIC without extra channel resource cost.
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