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

TL;DR

This work investigates information freshness in IEEE 802.11ax UORA networks by developing a dual discrete-time Markov chain framework to quantify the long-term average AoI, $\overline{\Delta}$. It derives exact AAoI expressions and a tractable lower bound, enabling efficient AoI-oriented optimization of UORA parameters, notably the backoff window and its depth. The authors propose an efficient optimization algorithm based on the LB approximations and validate accuracy via Monte Carlo simulations, showing near-optimal AoI performance compared with exhaustive search and superior results to round-robin and max-AoI policies in large or low-traffic networks. The results provide practical design insights for AoI-aware WiFi uplink scheduling and demonstrate a scalable methodology for optimizing UORA under time-sensitive traffic.

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.

Figures (11)

• Figure 1: Transmission process in IEEE 802.11ax UORA networks, where SIFS stands for Short Interframe Space.
• Figure 2: The DTMC of the number of active STAs, i.e., $\mathcal{M}_1$, where $(i|j)$ stands for $P_{i,j}$.
• Figure 3: The DTMC of the state of the OBO counter, i.e., $\mathcal{M}_2$.
• Figure 4: The AoI evolution of an STA in the considered model.
• Figure 5: Successful transmission rate $q$ and conditional accessing rate $\rho$ versus packet arrival rate $\lambda$.

Theorems & Definitions (12)

• Theorem 1
• proof
• Theorem 2
• proof
• Corollary 1
• proof
• Theorem 3
• proof
• Theorem 4
• proof