This study introduces an analytical model for a nonsaturated IEEE 802.11ax network, designed to capture the coexistence characteristics of uplink orthogonal frequency division multiple access (OFDMA)-based random access (UORA) and non-random access (UONRA) mechanisms under imperfect channels. Existing models fail to assess this realistic network due to three issues: 1. When accounting for frame aggregation, imperfect channels, and queue buffer, the network exhibits an overwhelming number of states, which renders the evaluation of its performance infeasible. 2. Existing bulk-service queue models fail to evaluate these queue characteristics influenced by imperfect channels. 3. The coexistence characteristics of the two mechanisms remain unexplored due to their complex interactions. To address Issue 1, we propose two designs: device updates and hardware implementation, which reduce the massive states and facilitate the network evaluation. To address Issue 2, we have developed a feedback-driven bulk-service queue model that captures the joint effects of imperfect channels and queue characteristics. To address Issue 3, we comprehensively analyzed the joint impact of the two mechanisms from a probabilistic perspective. Extensive simulations demonstrate that the proposed model accurately captures network performance (throughput, delay, and collision probability) and reduces the mean estimation error by a factor of 30 compared to existing studies.

This study explores the performance optimization of uplink orthogonal frequency division multiple access (OFDMA)-based random access (UORA) in IEEE 802.11ax networks. UORA supports multi-user transmission via two methods, where users transmit either fixed-size or variable-size aggregated MAC protocol data units. However, three critical issues arise. 1 Existing studies only focus on the fixed-size method with low practicality, and overlook the impact of traffic load which leads to inaccurate evaluation of the network performance. 2 The variable-size method has never been studied due to a complex scenario, where user frames append padding bits to fulfill the transmission opportunity constraint. 3 In realistic networks, the variable-size method sacrifices throughput to achieve high practicality and low latency. To address the first two issues, we proposed two novel models based on queueing theory that accurately capture the impact of these transmission methods and various parameters (e.g., the traffic load and padding bits) on throughput, packet loss rate, and latency. To address Issue 3, we design aDynamicSelectionAlgorithm ofTransmissionMethods (DSATM), which dynamically switches between the two transmission methods to enhance practicality, maximize throughput, and minimize latency. Finally, we conducted extensive simulations to verify the accuracy of our models and DSATM.