Learning Based Adaptive Fair QoS in IEEE 802.11ac Access Networks

Karmakar, Raja; Chattopadhyay, Samiran; Chakraborty, Sandip

doi:10.1109/comsnets.2019.8711461

Cited by 3 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Out-of-the-box MABs mainly decide which are the best channel widths to be used when no further information, neither from the network nor from user requirements, is considered. The goal is to maximize WLAN performance [156], [157], [162]. When traffic loads and other performance metrics are considered, such as delay and throughput, DRL techniques are successfully applied [158], [159].…”

Section: Channel Bondingmentioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Szott

Kosek-Szott

Gawłowicz

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable devices. The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied. To this end, we analyze over 250 papers in the field, providing readers with an overview of the main trends. Based on this review, we identify specific open challenges and provide general future research directions.

show abstract

Section: Channel Bondingmentioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Szott

Kosek-Szott

Gawłowicz

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…Out of the box MABs are mainly used to decide which are the best channel widths to be used when no further information, neither from the network nor from the user requirements, is considered, and the goal is to maximize WLAN performance [133], [134], [138]. Then, when traffic loads Figure 11.…”

Section: Channel Bondingmentioning

confidence: 99%

“…Karmakar et al [133] show that the default dynamic channel bonding operation can be improved by considering the individual needs of each station, as well as the access category (AC) they are using, selecting the most appropriate channel widths to use. With that goal in mind, a MAB algorithm, UCB, is used to learn when the use of secondary channels is required.…”

Section: Channel Bondingmentioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

Szott,

Kosek-Szott,

Gawłowicz

et al. 2021

Preprint

View full text Add to dashboard Cite

Wireless local area networks (WLANs) empowered by IEEE 802.11 (WiFi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as affordable and highly interoperable devices. The WiFi community is currently deploying WiFi 6 and developing WiFi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classic optimization approaches fail in such conditions, machine learning (ML) is well known for being able to handle complexity. Much research has been published on using ML to improve WiFi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describing the various areas where WiFi can be enhanced using ML. To this end, we analyze over 200 papers in the field providing readers with an overview of the main trends. Based on this review, we identify both open challenges in each WiFi performance area as well as general future research directions.

show abstract

“…Sharma [11] has investigated the capabilities of the IEEE 802.11e protocol to improve QoS support in WLAN for time critical applications. A high throughput multi-level scheduler, which uses an adaptive learning algorithm to sense the channel, is developed by [7], to support dynamic bandwidth allocation and improve QoS performance whilst ensuring fairness among contending stations to access the channel. Kosek [12] has proposed a new DEMS queuing mechanism to support down link-multiuser-multiple input multiple output (DL-MU-MIMO) transmissions.…”

Section: Related Workmentioning

confidence: 99%

“…When station accesses the channel through EDCAs for particular AC, it can continuously transport data from that AC for a fixed period of time without causing further contention. Based on the volume of the transmitted traffic, dynamic bandwidth channel access (DBCA) in IEEE 8802.11ac can assist to determine the channel width, on top of which the Transmit opportunity (TXOP) will be reserved following the EDCA contention procedure [7]. The performance of congested network can be enhanced through the application of this feature.…”

Section: Introductionmentioning

confidence: 99%

On the evaluation of the IEEE 802.11ac WLAN performance with QoS deployment

Farej

Ali

2021

IJEECS

View full text Add to dashboard Cite

The increase in the number of users on wireless local area networks (WLAN) and the development of large size applications have increased the demand for high-speed data rate and low latency. The IEEE 802.11ac was developed to provide very high throughput WLANs. Many enhancements are added to the medium access control (MAC) and physical (PHY) layers to increase data rate and improve network performance, these features enable the IEEE 802.11ac standard to provide quality of service (QoS) for multimedia applications. This paper concentrates on the impact of QoS on the system performance in term of delay and throughput. Four scenarios are proposed to investigate the network performance with different (from 1 up to 8) spatial stream (SS). The objective modular network testbed in C++ (OMNet++ modeler v5.5.1) is used to simulate and model these scenarios. For 8×8 SS, the results of simulation show the best throughput (maximum) and delay (minimum) values of (622, 484, 399.3, 382.96 Mbps) and (0.0211, 0.0589, 0.1037, 0.1202 sec) for 5, 15, 30 and 45 node number scenarios respectively. Although the number of nodes increases, the system performance decreases, however when QoS is deployed the performance is enhanced and its best improvement is obtained at the highest (45) node number scenario with values of 94.4% and 56.1% for throughput and delay respectively.

show abstract

Learning Based Adaptive Fair QoS in IEEE 802.11ac Access Networks

Cited by 3 publications

References 19 publications

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

On the evaluation of the IEEE 802.11ac WLAN performance with QoS deployment

Contact Info

Product

Resources

About