Delay Violation Probability and Age of Information Interplay in the Two-user Multiple Access Channel

Abstract

In this paper, we study the interplay between delay violation probability and average Age of Information (AoI) in a two-user wireless multiple access channel with multipacket reception (MPR) capability. We consider a system in which users have heterogeneous traffic characteristics: one has stringent delay constraints, while the other measures a source and transmits status updates in order to keep the AoI low. We show the effect of sensor sampling rate on the delay violation probability and that of the service rate of the delay-sensitive user on information freshness.

Performance Analysis of a MIMO System with Bursty Traffic in the presence of Energy Harvesting Jammer

“…We would like to emphasize that the purpose here is to study the effect of multiple antennas, jamming attacks, and the Alamouti coding scheme on the AAoI and not to derive the metric from scratch. Furthermore, the results obtained in this work can be utilized in other models for AAoI, for example, at the generate at-will policy [48] or in different queuing disciplines with or without packet management [47].…”

A Min-plus Model of Age-of-Information with Worst-case and Statistical Bounds

“…The derivation of the age of information bound is similar to that of the worst-case virtual delay bound [17], [18] that follows by substitution of E(t) defined in (9) and S(t) in ( 10) into (8). A major difference is that the age of information bound depends on the lower envelope function of the arrivals whereas the virtual delay does not.…”

“…A focus of age of information research are wireless channels, such as multiple access channels [1], [9], memoryless onoff channels [6], fading channels [9], and wireless networks with interference constraints [10]. With the general aim of minimizing the age of information, a frequent subject of inves-t D T A (1) T D (1) T A (2) T A (3) T D (2) T A (4) T D (3) Fig.…”

“…tigation is the optimal update rate [2], [6], [11], [12]. Typical update processes are periodic [1], [6], [12] or random [2], [5], [9]- [11], [13]. Predominantly, these works use analytical models, such as queueing theory [2], [11], [14], to derive time averages of mean and peak age of information.…”

“…Most closely related to this work are two papers that apply techniques common to the network calculus: [9] derives statistical delay bounds using a (min, ×)-algebra with Mellin transform and moment bound; and [6] employs a max-plus algebra to derive statistical age of information bounds by Chernoff's bound. The max-plus formulation works with time stamps of packet arrivals and departures.…”