4.6 Article

Performance Analyses of Uplink MU-OFDMA Hybrid Access MAC in IEEE 802.11ax WLANs

Journal

IEEE SYSTEMS JOURNAL
Volume 16, Issue 4, Pages 5108-5119

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2022.3211860

Keywords

Delay bounds; IEEE 802.11ax; Markov chain modeling; orthogonal frequency division multiple access (OFDMA); performance analysis; stochastic geometry

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This article analyzes the uplink-multiuser-orthogonal frequency division multiple access-hybrid access (HA) mechanism of the 802.11ax system under error-prone channel conditions. It proposes an enhanced Markov chain model for analyzing such a WLAN system and derives vital systems performance metrics. The analysis also considers different physical and medium access control layer aspects.
Future wireless local area networks (WLANs) are expected to have numerous stations (STAs) with diverse Quality of Service requirements. IEEE 802.11ax is the current WLAN standard proposed to meet these requirements. This article analyzes the uplink-multiuser-orthogonal frequency division multiple access-hybrid access (HA) mechanism of the 802.11ax system under error-prone channel conditions. We propose an enhanced Markov chain model for analyzing such a WLAN system. By solving the system model, we derive vital systems performance metrics, such as aggregate and per-STA throughput, random access (RA) capability packet loss probability, mean delay, jitter, fairness, and worst-case delay bounds. Our analysis also considers different physical (PHY) and medium access control (MAC) layer aspects, such as channel bandwidth, the number of resource units (RUs), packet aggregation feature, fading, path-loss, modulation and coding schemes (MCS), etc. Furthermore, we have studied the MCS link adaptation-based dynamic HA process where the RUs are dynamically adapted between scheduled access and RA mechanisms. Additionally, to capture the role of PHY and MAC layer parameters on multi-Basic service set scenarios, we have integrated the Markov chain-based modeling with the stochastic geometry-based model. Our analytical model has been validated against extensive simulations, and the tradeoff between the metrics is studied.

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