Joint Sampling and Transmission Optimization for Wireless Metaverse Services

The perception data collection and transmission are important for the state synchronization between virtual and physical space. Considering that the perception data sampling and uplink transmission will affect the users’ state analysis at edge servers, we study the joint sampling and transmission optimization scheme for multi-user wireless metaverse services. We first propose a novel perception data sampling and uplink transmission scheme, and we design a cross-timeslot residual packet completion mechanism. We formulate an objective function that aims to minimize users’ energy consumption under the constraint of the Age of Information (AoI). Then, we present a joint channel allocation and perception data sampling judgment optimization algorithm based on matching theory. Finally, we conduct simulation experiments to test our algorithm under different conditions by varying variables such as the number of users and channels. The results demonstrate that our algorithm is innovative and superior in reducing users’ energy consumption and AoI.