Statistical AoI, Delay, and Error-Rate Bounded QoS Provisioning for Satellite-Terrestrial Integrated Networks
<|reference_start|>Statistical AoI, Delay, and Error-Rate Bounded QoS Provisioning for Satellite-Terrestrial Integrated Networks: Massive ultra-reliable and low latency communications (mURLLC) has emerged to support wireless time/error-sensitive services, which has attracted significant research attention while imposing several unprecedented challenges not encountered before. By leveraging the significant improvements in space-aerial-terrestrial resources for comprehensive 3D coverage, satellite-terrestrial integrated networks have been proposed to achieve rigorous and diverse quality-of-services (QoS) constraints of mURLLC. To effectively measure data freshness in satellite communications, recently, age of information (AoI) has surfaced as a novel QoS criterion for ensuring time-critical applications. Nevertheless, because of the complicated and dynamic nature of network environments, how to efficiently model multi-dimensional statistical QoS provisioning while upper-bounding peak AoI, delay, and error-rate for diverse network segments is still largely open. To address these issues, in this paper we propose statistical QoS provisioning schemes over satellite-terrestrial integrated networks in the finite blocklength regime. In particular, first we establish a satellite-terrestrial integrated wireless network architecture model and an AoI metric model. Second, we derive a series of fundamental statistical QoS metrics including peak-AoI bounded QoS exponent, delay-bounded QoS exponent, and error-rate bounded QoS exponent. Finally, we conduct a set of simulations to validate and evaluate our proposed statistical QoS provisioning schemes over satellite-terrestrial integrated networks.<|reference_end|>
@article{wang2024statistical,
    title={Statistical AoI, Delay, and Error-Rate Bounded QoS Provisioning for
  Satellite-Terrestrial Integrated Networks},
    author={Jingqing Wang, Wenchi Cheng, H. Vincent Poor},
    journal={arXiv preprint arXiv:2406.05165},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05165},
    primaryClass={eess.SY cs.SY}
}