  Recently, deep joint source channel coding (DJSCC) techniques have been
extensively studied and have shown significant performance with limited
bandwidth and low signal to noise ratio. Most DJSCC work considers
discrete-time analog transmission, while combining it with orthogonal frequency
division multiplexing (OFDM) creates serious high peak-to-average power ratio
(PAPR) problem. This paper conducts a comprehensive analysis on the use of
various OFDM PAPR reduction techniques in the DJSCC system, including both
conventional techniques such as clipping, companding, SLM and PTS, and deep
learning-based PAPR reduction techniques such as PAPR loss and clipping with
retraining. Our investigation shows that although conventional PAPR reduction
techniques can be applied to DJSCC, their performance in DJSCC is different
from the conventional split source channel coding. Moreover, we observe that
for signal distortion PAPR reduction techniques, clipping with retraining
achieves the best performance in terms of both PAPR reduction and recovery
accuracy. It is also noticed that signal non-distortion PAPR reduction
techniques can successfully reduce the PAPR in DJSCC without compromise to
signal reconstruction.
