  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.
