Aggregation-Aware Throughput Analysis for Heterogeneous IEEE 802.11 WLANs with MLO and BAW Constraints

Abstract

This paper investigates the impact of aggregation dynamics on throughput performance in heterogeneous IEEE 802.11 WLANs under IAMLO(Immediate-Access Multi-Link Operation)-based access. Existing analytical models typically rely on the fixed aggregation assumption, which fails to capture the stochastic nature of A-MPDU aggregation under packet errors, thus leading to significant throughput misestimation. To address this issue, we develop an aggregation-aware analytical framework that models the stochastic variation of aggregation length induced by packet errors and the block acknowledgment window (BAW) sliding mechanism. The proposed framework further incorporates aggregation dynamics into both payload and transmission duration modeling, and enables accurate throughput analysis for heterogeneous networks, where legacy devices, single-link devices (SLDs), and multi-link devices (MLDs) operate under IAMLO. Simulation results validate the proposed model and show that it significantly improves throughput estimation accuracy, compared with the fixed-aggregation baseline, the error of total throughput is reduced from 46% to 1%. Moreover, the analysis reveals a nonmonotonic relationship between the maximum aggregation size and system throughput, which indicates the existence of an optimal aggregation configuration under heterogeneous coexistence.