Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference

by   Tilahun M. Getu, et al.
University of Oulu
Ecole De Technologie Superieure (Ets)
Virginia Polytechnic Institute and State University

Semantic communication (SemCom) has emerged as a 6G enabler while promising to minimize power usage, bandwidth consumption, and transmission delay by minimizing irrelevant information transmission. However, the benefits of such a semantic-centric design can be limited by radio frequency interference (RFI) that causes substantial semantic noise. The impact of semantic noise due to interference can be alleviated using an interference-resistant and robust (IR^2) SemCom design. Nevertheless, no such design exists yet. To shed light on this knowledge gap and stimulate fundamental research on IR^2 SemCom, the performance limits of a text SemCom system named DeepSC is studied in the presence of single- and multi-interferer RFI. By introducing a principled probabilistic framework for SemCom, we show that DeepSC produces semantically irrelevant sentences as the power of single- and multi-interferer RFI gets very large. Corroborated by Monte Carlo simulations, these performance limits offer design insights – regarding IR^2 SemCom – contrary to the theoretically unsubstantiated sentiment that SemCom techniques (such as DeepSC) work well in very low signal-to-noise ratio regimes. The performance limits also reveal the vulnerability of DeepSC and SemCom to a wireless attack using RFI. Furthermore, our introduced probabilistic framework inspires the performance analysis of many text SemCom techniques, as they are chiefly inspired by DeepSC.


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