aiOS: An Intelligence Layer for SD-WLANs

Coronado, Estefanía; Thomas, Abin; Bayhan, Suzan; Riggio, Roberto

doi:10.1109/noms47738.2020.9110260

Cited by 7 publications

(15 citation statements)

References 38 publications

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“…In this work, we introduce aiOS, the first open source O-RAN nearreal-time RAN intelligent controller (RIC). 1 We extend our previous work [7] as follows:…”

Section: Introductionmentioning

confidence: 93%

“…Conversely, academia actively looks into the application of AI/ML to various aspects of (SD-)WLANs. Due to space constraints, we mention only two particular works [7,10]. The first study [10] introduces deep learning into the low-level Wi-Fi stack, while the second work [7] focuses on the higher resource management layers, such as enhanced distributed channel access (EDCA) optimization and mobility management.…”

Section: Standardizationmentioning

confidence: 99%

“…The former category comprises approaches such as deep learning, while the latter covers regression and classification techniques. A particularly promising solution is random forest (and derivatives), which combines interpretability, fast training, and a relatively good ability to generalize results [7].…”

Section: Network-related Challengesmentioning

confidence: 99%

“…From this dataset, the variables selected for the models' construction were the channel utilization, and specifi c information of the MCS, for example, success probability, attempted bytes in the last 100 ms, and expected throughput in perfect channel conditions. Please refer to [7] for more details about the training.…”

Section: Descriptionmentioning

confidence: 99%

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AI-Empowered Software-Defined WLANs

et al. 2021

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The complexity of wireless and mobile networks is growing at an unprecedented pace. This trend is proving current network control and management techniques based on analytical models and simulations to be impractical, especially if combined with the data deluge expected from future applications such as augmented reality. This is particularly true for software-defined wireless local area networks (SD-WLANs). It is our belief that to battle this growing complexity, future SD-WLANs must follow an artificial intelligence (AI) -native approach. In this article, we introduce aiOS, which is an AI-based platform that builds toward the autonomous management of SD-WLANs. Our proposal is aligned with the most recent trends in in-network AI promoted by the ITU Telecommunication Standardization Sector (ITU-T) and with the architecture for disaggregated radio access networks promoted by the Open Radio Access Network Alliance. We validate aiOS in a practical use case, namely frame size optimization in SD-WLANs, and we consider the long-term evolution, challenges, and scenarios for AI-assisted network automation in the wireless and mobile networking domain.

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“…In this work, we introduce aiOS, the first open source O-RAN nearreal-time RAN intelligent controller (RIC). 1 We extend our previous work [7] as follows:…”

Section: Introductionmentioning

confidence: 93%

Section: Standardizationmentioning

confidence: 99%

Section: Network-related Challengesmentioning

confidence: 99%

Section: Descriptionmentioning

confidence: 99%

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AI-Empowered Software-Defined WLANs

et al. 2021

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“…To optimally select the frame size under dynamic conditions, ML techniques are used, including supervised learning [61], [62], [64], online learning [55], and ANN [60], [63] models. Their use is reported in the 802.11n standard to maximize goodput [61], [62], in 802.11 networks to maximize throughput [60], and in 802.11ac for addressing the energy-throughput trade-off [55]. Furthermore, ML methods are also reported to estimate the aggregation levels in 802.11ac, which is not typically accessible by non-rooted mobile handsets [64].…”

Section: Frame Aggregationmentioning

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

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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.

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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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aiOS: An Intelligence Layer for SD-WLANs

Cited by 7 publications

References 38 publications

AI-Empowered Software-Defined WLANs

AI-Empowered Software-Defined WLANs

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

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