UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learning

To keep as FFHQ dataset (image size 1024 1024). Nouveau VAE the Metaverse up-to-date, uplink data collection for object (NVAE) proposed by Vahdat et al. [10] further improved the modeling and updating are essential for VR applications. The performance of VAE and achieved satisfying results on various efficiency of data transmission has a direct impact on user high-quality image datasets. Li et al. [11] found that devices experience once there are demands to update the VR background, can select different scales of sub-models that requires less which is different from the traditional VR applications computational energy at the cost of reconstruction quality, and whose contents are not frequently updated. The 3-D modeling formulated the relationship between them. of remote area VR backgrounds including buildings (indoor and outdoor), roads, and natural environments are based on To cope with the challenge of wireless network coverage numerous photos taken on location, e.g., more than 1500 in remote areas, UAV-assisted data collection is considered as images with the average size of 10Mb are required to model a practical solution to set up flexible wireless networks for an area with historic buildings [?]. The data collection with heterogeneous user requirements [?], especially the research such large size poses requirements for both high transmission on UAV-enabled communication resource allocation, trajectory efficiency and wide network coverage.