A Flexible Machine-Learning-Aware Architecture for Future WLANs

Wilhelmi, Francesc; Barrachina‐Muñoz, Sergio; Bellalta, Boris; Cano, Cristina; Jönsson, Anders; Ram, Vishnu

doi:10.1109/mcom.001.1900637

Cited by 32 publications

(19 citation statements)

References 9 publications

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“…Therefore, AI/ML algorithms and telecommunication technologies can evolve in parallel. The study in [91] utilises this architecture for ML-based association and handover in a WLAN.…”

Section: • Service Design •mentioning

confidence: 99%

Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

Koufos

Haloui

Dianati

et al. 2021

JSAN

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The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects.

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“…Therefore, AI/ML algorithms and telecommunication technologies can evolve in parallel. The study in [91] utilises this architecture for ML-based association and handover in a WLAN.…”

Section: • Service Design •mentioning

confidence: 99%

Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

Koufos

Haloui

Dianati

et al. 2021

JSAN

View full text Add to dashboard Cite

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“…Finally, we remark the importance of cost-effectively predicting the performance in WLANs, which may open the door to novel mechanisms using these predictions as heuristics for online optimization. The incorporation of these kinds of models to WLANs is expected to be enabled by ML-aware architectural solutions [41].…”

Section: Lessons Learnedmentioning

confidence: 99%

Machine learning for performance prediction of channel bonding in next-generation IEEE 802.11 WLANS

Wilhelmi¹,

Góez²,

Soto³

et al. 2021

ITU-J FET

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With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and standardization organizations are progressing on the definition of mechanisms and procedures to address the increasing complexity of future 5G and beyond communications. In this context, the International Telecommunication Union (ITU) organized the First AI for 5G Challenge to bring industry and academia together to introduce and solve representative problems related to the application of Machine Learning (ML) to networks. In this paper, we present the results gathered from Problem Statement 13 (PS-013), organized by Universitat Pompeu Fabra (UPF), whose primary goal was predicting the performance of next-generation Wireless Local Area Networks (WLANs) applying Channel Bonding (CB) techniques. In particular, we provide an overview of the ML models proposed by participants (including artificial neural networks, graph neural networks, random forest regression, and gradient boosting) and analyze their performance on an open data set generated using the IEEE 802.11ax-oriented Komondor network simulator. The accuracy achieved by the proposed methods demonstrates the suitability of ML for predicting the performance of WLANs. Moreover, we discuss the importance of abstracting WLAN interactions to achieve better results, and we argue that there is certainly room for improvement in throughput prediction through ML.

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“…The mentioned FG-ML5G group associated to the ITU-T, has defined a logical interoperable architecture for future networks, which incorporates a ML overlay that operates on the top of any specified underlay network technology [9]. For instance, based on this architecture, the authors in [6] discussed ML in the context of IEEE 802.11 Wireless Local Area Networks (WLANs). This architecture facilitates deploying ML applications in different network scenarios and is adopted in the CAI testbed.…”

Section: Ai Integrationmentioning

confidence: 99%

“…Over the years, the networks were not appropriately designed to accommodate AI-oriented tasks such as data collection, processing, and output distribution. Current systems are typically designed to deliver content, without considering the characteristics from each application requesting network resources [6]. When properly integrated, AI can leverage the efficiency of modern and sophisticated networks by using, e. g., big data techniques.…”

Section: Introductionmentioning

confidence: 99%