Impact of Channel Memory on the Data Freshness

TL;DR

This paper analyzes how channel memory affects data freshness in networks with different packet arrival models, deriving explicit AoI expressions for FCFS and pLGFS policies over Gilbert-Elliott channels.

Contribution

It provides new analytical expressions for average AoI under various arrival models and policies, highlighting the impact of channel memory on data freshness.

Findings

AoI increases monotonically with channel memory under pLGFS.

AoI grows faster with channel memory under FCFS due to queuing delays.

Explicit AoI formulas are derived for multiple network models.

Abstract

In this letter, we investigate the impact of channel memory on the average age of information (AoI) for networks with various packet arrival models under first-come-first-served (FCFS) and preemptive last-generated-first-served (pLGFS) policies over Gilbert-Elliott (GE) erasure channel. For networks with Bernoulli arrival model, we first derive the average AoI under the pLGFS queuing policy, and then characterize the AoI gap between the FCFS and pLGFS policies. For networks with Bernoulli arrival and generate-at-will arrival models, the AoI performances under the FCFS and pLGFS policies are derived explicitly. For networks with periodic arrival model, we derive the closed-form expression for the average AoI under pLGFS over a general GE channel and propose a numerical algorithm for calculating that under FCFS efficiently. It is revealed that for pLGFS policy, the average AoI increases monotonically with channel memory $\eta$ at $\frac{\eta}{1-\eta}$ over the symmetric GE channel. For FCFS, the average AoI increases even faster due to the queuing delay, with an additional term related to the packet arrival rate.