Energy-Efficient Downlink Semantic Generative Communication with Text-to-Image Generators
This work addresses energy efficiency in downlink communication for systems with text-to-image generators, offering a specific optimization for wireless networks.
The paper tackles the problem of minimizing total energy consumption in a semantic generative communication framework by selecting which users generate images locally from text prompts versus downloading images directly, achieving up to 54% reduction in total energy compared to a baseline.
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.