Scheduling the Data Transmission Interval in IEEE 802.11ad: A Reinforcement Learning Approach

Azzino, Tommy; Ropitault, Tanguy; Zorzi, Michele

doi:10.1109/icnc47757.2020.9049674

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…The specifics of the resource scheduler are out of the standard scope and remains an open research challenge [171]. Azzino et al [130] find the optimal duration of the contention-free period by observing the time-varying network load and using an RL-based approach. Their scheme preserves throughput for allocated streams while leaving more resources for contention-based traffic.…”

Section: A Beamformingmentioning

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

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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: A Beamformingmentioning

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

“…The definition of the resource scheduler is out of the standard scope and remains an open research challenge [104]. Azzino et al [103] propose an RL-based approach to find the optimal duration of the contention-free period by observing the time-varying network load. Their scheme is able to preserve the throughput for the allocated streams, while leaving more resources for contention-based traffic.…”

Section: A Beamformingmentioning

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

“…A seminal study on the use of Reinforcement Learning (RL) to solve the problem of jointly scheduling CBAPs and SPs in 802.11ad is in [8], where ns-3 was used to assess how the algorithm could decrease the Data Transmission Interval (DTI) occupancy while guaranteeing state-of-the-art QoS performance.…”

Section: State Of the Artmentioning

confidence: 99%

A Simulation Framework for Contention-Free Scheduling on WiGig

Drago,

Azzino,

Lecci

et al. 2021

Preprint

Self Cite

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The latest IEEE 802.11 amendments provide support to directional communications in the Millimeter Wave spectrum and, thanks to the wide bandwidth available at such frequencies, makes it possible to wirelessly approach several emergent use cases, such as virtual and augmented reality, telepresence, and remote control of industrial facilities. However, these applications require stringent Quality of Service, that only contention-free scheduling algorithms can guarantee. In this paper, we propose an end-to-end framework for the joint admission control and scheduling of periodic traffic streams over mmWave Wireless Local Area Networks based on Network Simulator 3, a popular full-stack open-source network simulator. Moreover, we design a baseline algorithm to handle scheduling requests, and we evaluate its performance with a full-stack perspective. The algorithm is tested in three scenarios, where we investigated different configurations and features to highlight the differences and trade-offs between contention-based and contention-free access strategies.

show abstract

Scheduling the Data Transmission Interval in IEEE 802.11ad: A Reinforcement Learning Approach

Cited by 13 publications

References 7 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

A Simulation Framework for Contention-Free Scheduling on WiGig

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