This paper studies multiuser immersive communications networks in which different user equipment may demand various extended reality (XR) services. In such heterogeneous networks, time-frequency resource allocation needs to be more adaptive since XR services are usually multi-modal and latency-sensitive. To this end, we develop a scalable time-frequency resource allocation method based on multi-numerology and mini-slot. To appropriately determining the discrete parameters of multi-numerology and mini-slot for multiuser immersive communications, the proposed method first presents a novel flexible time-frequency resource block configuration, then it leverages the deep reinforcement learning to maximize the total quality-of-experience (QoE) under different users' QoE constraints. The results confirm the efficiency and scalability of the proposed time-frequency resource allocation method.
@article{arxiv.2309.08906,
title = {Scalable Multiuser Immersive Communications with Multi-numerology and Mini-slot},
author = {Ming Hu and Jiazhi Peng and Lifeng Wang and Kai-Kit Wong},
journal= {arXiv preprint arXiv:2309.08906},
year = {2023}
}