Advanced IEEE 802.11ax TCP aware scheduling under unreliable channels

Sharon, Oran; Alpert, Yaron

doi:10.1002/dac.4060

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…Some work addressed improving scheduling in the uplink by minimizing delay (Bankov et al, 2017;Bankov et al, 2018) and improving scheduling strategies for TCP traffic (Sharon & Alpert, 2019). While in (Naik et al, 2018) the performance analysis of uplink multiuser OFDMA and in (Sharon & Alpert, 2017) the scheduling strategies and throughput optimization were explained.…”

Section: 11ax Scheduling Basedmentioning

confidence: 99%

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

MASRİ¹,

ERDAL²

2022

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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Wireless mobile communications have experienced tremendous growth in the number of users, data rate requirements, and coverage in recent years. As the data rate and system throughput requirements increase, researchers and system designers need to develop efficient methods to meet these requirements with reasonable effort and cost. In this paper, we discuss an efficient approach to deal with diminishing the overhead of Downlink (DL) and feedback using Machine Learning (ML) for 802.11ax. In particular, the antennas that are related to the router were divided into two gatherings. We used good samples of Channel State Information (CSI) that were taken from an open-access dataset and used it to train our linear regression model. The first group of antennas was used as input to our model and the second group was used as the output of the model. In the online mode, we need to estimate only one group of antennas and for the second group, we can predict it using the trained linear regression model by using the estimated CSI group as input to the model. Therefore, the output of the model using as the CSI for the second group. In this way, we can reduce the overhead of the DL in the router as shown in the result table so the router will work more efficiently compared to the existing systems. From the results table in the last section, the average sum rate has increased between 20% and 30%.

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Section: 11ax Scheduling Basedmentioning

confidence: 99%

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

MASRİ¹,

ERDAL²

2022

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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“…The authors in [12] perform analysis for different node densities and traffic types in HEW network. In [13], the authors suggest a TCP-aware scheduling strategy for optimizing the transmission of TCP traffic. The number of stations and resource allocation algorithms are the only deployment options evaluated in [14,15], respectively, to measure the corresponding effects.…”

Section: Related Workmentioning

confidence: 99%

Link Quality Improvement of Long-Term Evolution and 802.11ax in High-Density Areas

Mina

2020

Wireless Communications and Mobile Computing

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The 802.11ax high-efficiency wireless (HEW) particularly designed for high-density areas. However, dense areas have specific requirements that demand precise deployment strategies by network developers. In dense networks, a large number of users are simultaneously connected to the same channel; hence, the available bandwidth is divided among the users in such a way that joining more users can eventually saturate the network. Furthermore, in dense areas, a large number of closely spaced users are transmitting data at the same time. In such a heavily frequency interfered environment, the wireless link quality extremely degrades, which can practically render the network unavailable. Thereby, it is essential to determine the appropriate deployment options regarding the specific networks’ settings and configurations. Hence, this work proposes a network architecture model to determine the dual-band HEW performance in dense deployments. The model additionally includes long-term evolution (LTE) as the cellular alternative for high-density areas which is utilized by the model as the reference point for corresponding comparison purposes with HEW. The model is implemented, and link quality parameters are measured based on different aspects of the deployment options. To further validate the model and determine the optimization levels provided by the options, the simulation and analytical results are compared.

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“…They conducted evaluations of MIMO and OFDMA transmissions in IEEE 802.11ax against the carrier-sense multiple access with collision avoidance (CSMA/CA) MAC of IEEE 802.11ac with the SU and MU modes for various numbers of STAs scenarios in both reliable and unreliable channel environments. The authors of [ 18 ] suggested advanced IEEE 802.11ax TCP-aware scheduling strategies to optimize the performance using MU UL and MU DL transmissions. They were based on transmission opportunities (TXOP) and can control achieved goodput versus delays.…”

Section: Related Workmentioning

confidence: 99%

Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks

Kwon

Kim

et al. 2018

Sensors

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The increasing use of Internet of Things (IoT) devices in specific areas results in an interference among them and the quality of communications can be severely degraded. To deal with this interference issue, the IEEE 802.11ax standard has been established in hyper-dense wireless networking systems. The 802.11ax adopts a new candidate technology that is called multiple network allocation vector in order to mitigate the interference problem. In this paper, we point out a potential problem in multiple network allocation vector which can cause delays to communication among IoT devices in hyper-dense wireless networks. Furthermore, this paper introduces an adaptive beam alignment algorithm for interference resolution, and analyzes the potential delays of communications among IoT devices under interference conditions. Finally, we simulate our proposed algorithm in densely deployed environments and show that the interference can be mitigated and the IEEE 802.11ax-based IoT devices can utilize air interface more fairly compared to conventional IEEE 802.11 distributed coordination function.

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Advanced IEEE 802.11ax TCP aware scheduling under unreliable channels

Cited by 10 publications

References 35 publications

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

Link Quality Improvement of Long-Term Evolution and 802.11ax in High-Density Areas

Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks

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