Analyses for Age of Information Supporting URLLC Over Multimedia Wireless Networks
Xi Zhang, Qixuan Zhu, H. Vincent Poor
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28 May 2020
Ultra-reliable and low-latency communication (URLLC) networks aim at providing a wide range of delay-sensitive multimedia services and applications by satisfying users' stringent requirements on the delay-bounded quality of service (QoS).
The age of information (AoI) theory characterizes the freshness of information, which is the time-difference between the current time and the time-stamp of the latest observation.
This paper proposes to apply the AoI theory to mitigate the transmission latency and improve performances in URLLC networks, by analyzing the AoI over a stationary and ergodic first-come-first-serve M/M/1 channel using a stochastic hybrid system (SHS) model.
By applying the SHS model, the transitions of AoI between states are triggered by stochastic events, and the probability that a transition occurs depends on both the continuous and discrete components of the current SHS state.
We also derive the joint probability density function and expectation of AoI through the investigation of moment dynamics by using the SHS model.
Finally, we evaluate and validate our derived results of distribution and expectation of AoI in URLLC networks through numerical analyses.
The age of information (AoI) theory characterizes the freshness of information, which is the time-difference between the current time and the time-stamp of the latest observation.
This paper proposes to apply the AoI theory to mitigate the transmission latency and improve performances in URLLC networks, by analyzing the AoI over a stationary and ergodic first-come-first-serve M/M/1 channel using a stochastic hybrid system (SHS) model.
By applying the SHS model, the transitions of AoI between states are triggered by stochastic events, and the probability that a transition occurs depends on both the continuous and discrete components of the current SHS state.
We also derive the joint probability density function and expectation of AoI through the investigation of moment dynamics by using the SHS model.
Finally, we evaluate and validate our derived results of distribution and expectation of AoI in URLLC networks through numerical analyses.