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

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