A Reliable A-MSDU Frame Aggregation Scheme in 802.11n Wireless Networks

Saif, Anwar; Othman, Mohamed

doi:10.1016/j.procs.2013.09.026

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…Still, the challenges of frame aggregation at low frequency [37,76] are often valid also for higher frequencies. For instance, related work shows that high levels of aggregation increase packet error rates in 802.11n/ac [35,36,49]. We extend this analysis for the case of 802.11ad.…”

Section: Related Workmentioning

confidence: 71%

“…Existing studies show that aggregation can achieve up to 95% channel utilization [33], and that hybrid of A-MSDU and A-MPDU performs best [34]. Related work discusses improvements to these 802.11 aggregation schemes, such as including additional headers to allow A-MSDU to retransmit individual packets [35], and compressing the per-packet subheaders in an A-MSDU frame [36]. Moreover, other approaches allow aggregating packets addressed to different stations.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Zero Overhead Device Tracking in 60 GHz Wireless Networks using Multi-Lobe Beam Patterns

Loch

Assasa

Palacios

et al. 2017

Proceedings of the 13th International Conference on Emerging Networking EXperiments and Technologies

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Millimeter wave (mmWave) technology is one of the main pillars of the next generation Wireless Local Area Networks (WLANs) and 5G mobile networks. The main reason lies in the quantum leap of capacity it provides with respect to wireless networks operating in the sub-6-GHz band. Nevertheless, efficient and reliable communication in the mmWave band demands novel techniques to tackle all the barriers associated with wireless propagation in this band. For

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Section: Related Workmentioning

confidence: 71%

Section: Related Workmentioning

confidence: 99%

Zero Overhead Device Tracking in 60 GHz Wireless Networks using Multi-Lobe Beam Patterns

Loch

Assasa

Palacios

et al. 2017

Proceedings of the 13th International Conference on Emerging Networking EXperiments and Technologies

View full text Add to dashboard Cite

show abstract

“…This work was later implemented using NS-2 in [28]. Similarly, the authors of [29] seek improvements for error-prone channels by adding control bits to every subframe with the aim of enabling per-subframe retransmissions. Conversely, Kim et al [22] propose a method that deals with frame size estimation based on the Extended Kalman Filter for saturated networks.…”

Section: Solutions For Frame Length Optimizationmentioning

confidence: 99%

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

2020

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Software-Defined Networking (SDN) is gaining a lot of traction in wireless systems with several practical implementations and numerous proposals being made. Despite instigating a shift from monolithic network architectures towards more modulated operations, automated network management requires the ability to extract, utilise and improve knowledge over time. Beyond simply scrutinizing data, Machine Learning (ML) is evolving from a simple tool applied in networking to an active component in what is known as Knowledge-Defined Networking (KDN). This work discusses the inclusion of ML techniques in the specific case of Software-Defined Wireless Local Area Networks (SD-WLANs), paying particular attention to the frame length optimization problem. With this in mind, we propose an adaptive ML-based approach for frame size selection on a per-user basis by taking into account both specific channel conditions and global performance indicators. By relying on standard frame aggregation mechanisms, the model can be seamlessly embedded into any Enterprise SD-WLAN by obtaining the data needed from the control plane, and then returning the output back to this in order to efficiently adapt the frame size to the needs of each user. Our approach has been gauged by analysing a multitude of scenarios, with the results showing an average improvement of 18.36% in goodput over standard aggregation mechanisms.

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“…This work is later implemented using NS-2 [14]. Similarly, in [15] the authors pursue improvements for error-prone channels by adding control bits to every subframe to enable per-subframe retransmissions. The work in [16] deals with adaptive frame size estimation based on Extended Kalman Filter for saturated networks.…”

Section: B Frame Length Selection In Wlansmentioning

confidence: 99%

aiOS: An Intelligence Layer for SD-WLANs

Coronado

Thomas

Bayhan

et al. 2020

NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium

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Software-Defined Networking promises to deliver a more manageable network whose behaviour could be easily changed using applications written in high-level declarative languages running on top of a logically centralized control plane resulting, on the one hand, in the mushrooming of complex point solutions to very specific problems and, on the other hand, in the creation of a multitude of network configuration options. This fact is especially true for 802.11-based Software-Defined WLANs (SD-WLANs). It is our standpoint that to tame this increase in complexity, future SD-WLANs must follow an Artificial Intelligence (AI) native approach. In this paper we present aiOS, an AI-based Operating System for SD-WLANs. Then, we use aiOS to implement several Machine Learning (ML) models for user-adaptive frame length selection in SD-WLANs. An extensive performance evaluation carried out on a real-world testbed shows that this approach improves the aggregated network throughput by up to 55%. Finally, we release the entire implementation including the controller, the ML models, and the programmable data-path under a permissive license for academic use.

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A Reliable A-MSDU Frame Aggregation Scheme in 802.11n Wireless Networks

Cited by 11 publications

References 18 publications

Zero Overhead Device Tracking in 60 GHz Wireless Networks using Multi-Lobe Beam Patterns

Zero Overhead Device Tracking in 60 GHz Wireless Networks using Multi-Lobe Beam Patterns

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

aiOS: An Intelligence Layer for SD-WLANs

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