Connectivity-Aware Fast Network Forming Aided By Digital Twin For Emergency Use

This paper studies a 6G use case for connecting users in emergency. A fast-forming network with a resource-limited aerial base station (BS) connecting with scattered communities of users is considered. Properly connecting each individual user in the service area is achieved by 1) applying device-to-device (D2D) communication within each community, 2) fairly assigning limited number of channels, and 3) optimally placing the flying BS. Q-learning, a common type of reinforcement learning (RL), is employed for autonomous BS placement. Two optimization objectives for BS placement are considered to maximize the per-user data rate in the worse condition and minimize the total BS transmitted power, respectively. To overcome resource limitations of the aerial BS, the RL training with many iterations is a done in the digital twin (DT) virtual space connected to the physical space via an aerial BS enabled by 6G technology. It is shown that, even the models used in DT is imperfect, nearly-optimal results can be obtained in DT. In particular, compared to RL training 100% in the physical space, a huge number of BS moves can be avoided and significant among of time and energy can be saved.