Heterogeneous Statistical QoS Provisioning Over Airborne Mobile Wireless Networks Academic Article uri icon


  • 1983-2012 IEEE. Airborne mobile wireless networks (AMWNs), which use spacecrafts and aircrafts such as satellites, airships, airplanes, unmanned aerial vehicles, and other high/medium/low-altitude platforms (HAPs/MAPs/LAPs) can efficiently support high dynamic network topologies and weakly connected communication links. Due to the dramatic dynamics of the AMWNs, it is very difficult to provide the deterministic delay-bounded quality of service (QoS) provisioning for time-sensitive real-time traffics (such as video and audio) over the AMWNs. Alternatively, the statistical delay-bounded QoS provisioning provides an efficient way for guaranteeing delay-bounded QoS requirements for real-time traffics over the AMWNs. On the other hand, because of the diversity of real-time traffics, it is highly demanded to consider the heterogeneity of delay-bounded QoS requirements for distinct real-time services under different HAPs/MAPs/LAPs in the AMWNs. In this paper, we establish the heterogeneous statistical QoS provisioning framework to support the diverse real-time services over the AMWNs. In particular, we formulate the optimization problem to maximize the aggregate effective capacity subject to heterogeneous statistical delay-bounded QoS requirements for both downlink and uplink transmissions-based AMWNs groups (AMWNGs) in the AMWNs. We solve the aggregate effective capacity maximization problems and derive the optimal heterogeneous statistical QoS-driven power allocation schemes for the AMWNs. The numerical analyses we obtained verify that our developed optimal heterogeneous statistical QoS-driven power allocation schemes can significantly increase the aggregate effective capacity for the AMWNs than the other existing schemes.

published proceedings


author list (cited authors)

  • Zhang, X. i., Cheng, W., & Zhang, H.

citation count

  • 42

complete list of authors

  • Zhang, Xi||Cheng, Wenchi||Zhang, Hailin

publication date

  • September 2018