OFDMA Backoff Control Scheme for Improving Channel Efficiency in the Dynamic Network Environment of IEEE 802.11ax WLANs

Kim, Youngboo; Kwon, Lam; Park, Eun-Chan

doi:10.3390/s21155111

Cited by 18 publications

(20 citation statements)

References 29 publications

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“…As a result, the transmission of information about the appearance of frames in the queue causes additional delays. To avoid this problem, the AP can leave a part of the channel for random access [23][24][25][26][27][28][29] so that an STA can attempt to transmit as soon as it has a frame. However, with random access, STAs transmit data according to the access method that is similar to the ALOHA [30,31], the efficiency of which significantly reduces with a large number of contending STAs.…”

Section: Related Workmentioning

confidence: 99%

Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks

et al. 2022

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Real-time applications (RTA) are an important use case for IEEE 802.11be, a new amendment to the Wi-Fi standard. This amendment introduces new complicated mechanisms to provide low delay and high reliability for RTA, but many of them are not supported by legacy devices that may be present in future Wi-Fi networks. In contrast, the preliminary channel access (PCA) method is designed to satisfy strict RTA requirements even in the presence of legacy devices and does not require significant changes to the Wi-Fi protocol. However, it significantly reduces the capacity for non-RTA traffic. This paper introduces a Smart PCA method, which improves the performance of all the stations in scenarios with multiple RTA stations. Extensive simulation shows that the Smart PCA method guarantees low delays for intensive RTA traffic in these scenarios. Moreover, it doubles the network capacity for the stations with non-RTA traffic.

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Section: Related Workmentioning

confidence: 99%

Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks

et al. 2022

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“…Several centralized UORA studies have focused on achieving an optimal UORA performance in real network scenarios [14], [15], [16], and [17]. To address the limitations of centralized UORA, a recent research effort [18] proposed a decentralized approach in which stations determine their back-off counter value in a distributed manner. This research is inspired by the benefits of decentralized UORA [18] and aims to solve resource allocation issues in highly dense networks.…”

Section: Introductionmentioning

confidence: 99%

Collision-Based Up-Link OFDMA Random Access Mechanism for Wi-Fi 6

Rehman,

Hussain,

Ali

et al. 2023

IEEE Access

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The IEEE 802.11ax standard defines High-Efficiency WLAN (HEW), which is known as 6th Generation Wi-Fi (Wi-Fi 6) network. HEW uses an up-link OFDMA-based Random Access (UORA) mechanism for channel access in dense multi-user transmissions. Similar to legacy Wi-Fi networks, UORA recommends adopting a random OFDMA Back-Off (OBO) procedure within the OFDMA contention window (OCW) range to access the available channel resources randomly. IEEE 802.11ax follows a centralized channel resource allocation mechanism, in which an access point (AP) is responsible for calculating and configuring the OCW range and notifying the respective Random Access stations (RA-STAs). However, IEEE 802.11ax does not specify a method to determine reasonable OCW ranges based on the number of RA-STAs for uplink transmissions. In a centralized UORA approach, one of the major challenges for an AP is to accurately and quickly evaluate the exact number of RA-STAs to minimize collision probabilities without the aid of a specific signaling mechanism. The centralized approach in Wi-Fi 6 can significantly degrade the performance of the UORA owing to an inappropriate range of OCW, particularly when the number of random access stations changes dynamically. In this study, a Collision-based Distributed OBO control (CODOBO_CTRL) scheme is proposed where each RA-STA independently determines its OBO counter, instead of an AP-guided centralized mechanism. The motivation for CODOBO_CTRL is to improve the performance of UORA in highly dynamic networks using primitive network parameters. Considering the success or failure of recent UORA transmissions, in the CODOBO_CTRL scheme, each random access station actively adjusts the OBO counter using the relationship between realistic network parameters: channel bandwidth and a collision factor. The results of a simulation study validate that the achieved throughput of the proposed scheme is almost equal to that of the OPT_UORA scheme in a static network, and 11% higher specifically in highly dynamic scenarios. Furthermore, compared to the standard UORA and OBO_CTRL schemes, the CODOBO_CTRL scheme demonstrates improvements of up to 207% and 12%, respectively, in terms of throughput performance.

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“…The efficiency of UORA can be improved by adaptive grouping [6], [7], spatial clustering [8], sub- channel hopping [9], complementary probability instead of backoff [10], additional carrier sensing [11], retransmission awareness [12], OBO modifications [2], [13], grouping-based channel access [14], and considering adjacent channel interference [15] 1 . None of the above research uses ML methods, although the application of reinforcement learning (RL) to improve UORA operation is suggested as future work by Kim et al [13]. In fact, with the proliferation of the use of ML solutions to improve Wi-Fi performance [17], extending UORA with ML is the logical next step.…”

Section: Introductionmentioning

confidence: 99%

“…• We evaluate RL-OBO using a simulation model to confirm the accuracy of the RL-based solution (Section V-D). Unlike most of the literature [6]- [12], [14], which considers only static scenarios, we follow [13] Algorithm 1 Legacy UORA (802.11ax) and study dynamic network loads and station churn. • We compare the operation of RL-OBO with a previous approach in Section V-F.…”

Section: Introductionmentioning

confidence: 99%

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Improving IEEE 802.11ax UORA Performance: Comparison of Reinforcement Learning and Heuristic Approaches

2022

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Machine learning (ML) has gained attention from the network research community because it can help solve difficult problems and potentially lead to groundbreaking achievements. In the Wi-Fi domain, ML is applied to solve challenges such as efficient channel access and fair coexistence with other technologies in unlicensed bands. In this paper, we address the performance of uplink orthogonal frequency division multiple random access (UORA) in IEEE 802.11ax networks. Optimization of UORA is a good case for applying ML because of its inherent complexity and dependence on situation and time-dependent parameters. In particular, we use deep reinforcement learning to tune UORA parameters. Our simulation results show that even though the ML-based solution leads to close to optimal results, its operation is comparable to a much simpler, non-ML heuristic. Therefore, we conclude that ML-based solutions to improve IEEE 802.11 performance need not exceed well-designed heuristics.

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OFDMA Backoff Control Scheme for Improving Channel Efficiency in the Dynamic Network Environment of IEEE 802.11ax WLANs

Cited by 18 publications

References 29 publications

Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks

Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks

Collision-Based Up-Link OFDMA Random Access Mechanism for Wi-Fi 6

Improving IEEE 802.11ax UORA Performance: Comparison of Reinforcement Learning and Heuristic Approaches

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