Energy-Efficient Downlink Semantic Generative Communication with
  Text-to-Image Generators
Hyein Lee Jihong Park Sooyoung Kim Jinho Choi
In this paper, we introduce a novel semantic generative communication (SGC)
framework, where generative users leverage text-to-image (T2I) generators to
create images locally from downloaded text prompts, while non-generative users
directly download images from a base station (BS). Although generative users
help reduce downlink transmission energy at the BS, they consume additional
energy for image generation and for uploading their generator state information
(GSI). We formulate the problem of minimizing the total energy consumption of
the BS and the users, and devise a generative user selection algorithm.
Simulation results corroborate that our proposed algorithm reduces total energy
by up to 54% compared to a baseline with all non-generative users.