Machine Learning for Wireless Network Topology Inference

Testi, Enrico; Favarelli, Elia; Pucci, Lorenzo; Giorgetti, Andrea

doi:10.1109/icspcs47537.2019.9008717

Cited by 16 publications

(4 citation statements)

References 21 publications

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“…However, our literature review did not identify any papers directly related to applying ML for improving the performance of either HWMP or other 802.11s functionalities. Testi et al [304] analyze network topology inference using external sensors for a simulated 802.11s network, but no specific mesh functionalities are considered. The lack of dedicated 802.11s research is most likely the result of the limited deployment of 802.11s by the industry.…”

Section: B Mesh Networkmentioning

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.

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Section: B Mesh Networkmentioning

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

“…Using reinforcement learning to converge to optimal topologies gave rise to slow and fluctuating convergence to other sub-optimal solutions [25]. Topology analysis in high-frequency networks may safely assume very dense but limited clusters of agents [19]. Thus, the number of clusters in topologies are trivial, and the communication ranges are relatively longer than the distance of agents.…”

Section: Related Workmentioning

confidence: 99%

OpTopNET: A Learning Optimal Topology Synthesizer for Ad-hoc Robot Networks

Macktoobian¹,

Zhan²,

Zhao³

2022

Preprint

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In this paper, we synthesize a machine-learning stacked ensemble model a vector of which predicts the optimal topology of a robot network. This problem is technically a multi-task classification problem. However, we divide it into a class of multi-class classification problems that can be more efficiently solved. For this purpose, we first compose an algorithm to create ground-truth topologies associated with various configurations of a robot network. This algorithm incorporates a complex collection of nonlinear optimality criteria that our learning model successfully manages to learn. Then, we propose a stacked ensemble model whose output is the topology prediction for the particular robot associated with it. Each stacked ensemble instance constitutes three low-level estimators whose outputs will be aggregated by a high-level boosting blender. The results of the simulations, applying our model to a network of 10 robots, represents over %80 accuracy in the prediction of optimal topologies corresponding to various configurations of this complex optimal topology learning problem.

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“…However, our literature review did not identify any papers directly related to applying ML for improving the performance of either HWMP or other 802.11s functionalities. A paper on network topology inference uses external sensors and an ML approach to infer the topology of a simulated 802.11s network, but no specific mesh functionalities are considered [258]. The lack of dedicated 802.11s research is most likely the result of the limited deployment of 802.11s by the industry.…”

Section: B Mesh Networkmentioning

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

Machine Learning for Wireless Network Topology Inference

Cited by 16 publications

References 21 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

OpTopNET: A Learning Optimal Topology Synthesizer for Ad-hoc Robot Networks

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

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