Optimizing Information Freshness of IEEE 802.11ax Uplink OFDMA-Based Random Access

TL;DR

This paper investigates the Age of Information (AoI) performance of IEEE 802.11ax UORA, providing analytical models and optimization algorithms to improve real-time data freshness in WiFi 6 networks.

Contribution

It introduces the first analytical framework for AoI in UORA networks, deriving closed-form expressions and efficient parameter optimization algorithms.

Findings

Proposed UORA parameter optimization algorithms outperform baseline policies.

Derived closed-form and lower bound expressions for AAoI.

Simulation validates the effectiveness of the optimization approach.

Abstract

The latest WiFi standard, IEEE 802.11ax (WiFi 6), introduces a novel uplink random access mechanism called uplink orthogonal frequency division multiple access-based random access (UORA). While existing work has evaluated the performance of UORA using conventional performance metrics, such as throughput and delay, its information freshness performance has not been thoroughly investigated in the literature. This is of practical significance as WiFi 6 and beyond are expected to support real-time applications. This paper presents the first attempt to fill this gap by investigating the information freshness, quantified by the Age of Information (AoI) metric, in UORA networks. We establish an analytical framework comprising two discrete-time Markov chains (DTMCs) to characterize the transmission states of stations (STAs) in UORA networks. Building on the formulated DTMCs, we derive an analytical expression for the long-term average AoI (AAoI), facilitating the optimization of UORA parameters for enhanced AoI performance through exhaustive search. To gain deeper design insights and improve the effectiveness of UORA parameter optimization, we derive a closed-form expression for the AAoI and its approximated lower bound for a simplified scenario characterized by a fixed backoff contention window and generate-at-will status updates. By analyzing the approximated lower bound of the AAoI, we propose efficient UORA parameter optimization algorithms that can be realized with only a few comparisons of different possible values of the parameters to be optimized. Simulation results validate our analysis and demonstrate that the AAoI achieved through our proposed parameter optimization algorithm closely approximates the optimal AoI performance obtained via exhaustive search, outperforming the round-robin and max-AoI policies in large and low-traffic networks.