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 IR2 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.

翻译：尽管基于深度学习的语义通信通过最小化无关信息传输——降低功耗、带宽消耗和传输延迟——已成为6G赋能技术，但其优势可能受到射频干扰的制约，该干扰会引发大量语义噪声。虽然采用抗干扰鲁棒语义通信设计可减轻此类语义噪声的影响，但目前尚无此类设计方案。为激发对IR² SemCom的基础研究，本文研究了在（多干扰源）射频干扰环境下，名为DeepSC的流行文本语义通信系统的性能极限。通过引入基于原理的语义通信概率框架，我们证明当（多干扰源）射频干扰功率极大时，DeepSC会生成语义无关的语句。同时推导了DeepSC在多干扰源射频干扰下的实际性能极限及中断概率下界，并提出了基于终身学习的通用型IR² SemCom系统。通过仿真与计算机实验验证了所推导的极限，实验结果进一步证实了DeepSC在面对基于射频干扰的无线攻击时的脆弱性。