  Recently, semantic communication (SC) has been regarded as one of the
potential paradigms of 6G. Current SC frameworks require channel state
information (CSI) to handle severe signal distortion induced by channel fading.
Since the channel estimation overhead for obtaining CSI cannot be neglected, we
therefore propose a generative adversarial network (GAN) based SC framework
(Ti-GSC) that doesn't require CSI. In Ti-GSC, two main modules, i.e., an
autoencoder-based encoder-decoder module (AEDM) and a GAN-based signal
distortion suppression module (GSDSM) are included where AEDM first encodes the
data at the source before transmission, and then GSDSM suppresses the
distortion of the received signals in both syntactic and semantic dimensions at
the destination. At last, AEDM decodes the distortion-suppressed signal at the
destination. To measure signal distortion, syntactic distortion and semantic
distortion terms are newly added to the total loss function. To achieve better
training results, joint optimization-based training (JOT) and alternating
optimization-based training (AOT) are designed for the proposed Ti-GSC.
Experimental results show that JOT is more efficient for Ti-GSC. Moreover,
without CSI, bilingual evaluation understudy (BLEU) score achieved by Ti-GSC is
about 40% and 62% higher than that achieved by existing SC frameworks in Rician
and Rayleigh fading, respectively. (*Due to the notification of arXiv "The
Abstract field cannot be longer than 1,920 characters", the appeared Abstract
is shortened. For the full Abstract, please download the Article.)
