Dynamic Channel Bonding Using Laser Chaos Decision Maker in WLANs

Kanemasa, Hiroshi; Li, Aohan; Naruse, Makoto; Chauvet, Nicolas; Hasegawa, Mikio

doi:10.1109/icaiic51459.2021.9415227

Cited by 6 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is realized that DyB is preferred over SCB. 205 3) Uniform channel bonding (UCB): Under UCB, every user should be given a fair proportion of the channel bandwidth in the CB. In this technique, the access channels are reinforced in two or four of the contiguous/coterminous channels.…”

Section: Schemes For Cbmentioning

confidence: 99%

A study on the channel bonding in IoT networks: Requirements, applications, and challenges

Kandar

Chyne

Sur

et al. 2023

Int J Communication

View full text Add to dashboard Cite

The most well-known sort of remote Internet connection is wireless local area networks (WLANs) due to its unsophisticated operation and deployment. Subsequently, the quantity of gadgets getting to the Internet through WLANs, for example, PCs, cell phones, or wearables, is expanding radically at the equivalent time that applications' throughput necessities do. To provide wireless networks with supplementary spectral resources, the researchers are considering the aggregation of frequency spectrums in licensed, unlicensed, and shared access (SA) bands. Channel aggregation/channel bonding (CA/CB) techniques accumulate quite a few channels together as one channel for the purpose of achieving better bandwidth utilization. In this study, we focus on reliable CA/CB techniques in different wireless networks. CA/CB procedures are utilized for empowering higher information rates by transmitting in more extensive channels, accordingly expanding range proficiency with the assured secure channel for communication. We also discuss the spectral scarcity issues in today's wireless IoT network. This paper presents an extensive survey on CA/CB procedures and methods, issues and challenges, and open research areas related to IoT devices. We analyze the performance of channel CA/CB strategies in the different wireless networks too.

show abstract

Section: Schemes For Cbmentioning

confidence: 99%

A study on the channel bonding in IoT networks: Requirements, applications, and challenges

Kandar

Chyne

Sur

et al. 2023

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Out-of-the-box MABs mainly decide which are the best channel widths to be used when no further information, neither from the network nor from user requirements, is considered. The goal is to maximize WLAN performance [156], [157], [162]. When traffic loads and other performance metrics are considered, such as delay and throughput, DRL techniques are successfully applied [158], [159].…”

Section: Channel Bondingmentioning

confidence: 99%

“…HA-DBCA uses a polling-based adaptive mechanism for contention-free access and UCB to identify the stations that are starving, and so allow them to transmit their data during the contention-free access. The channel bonding problem is also modelled as a MAB by Kanemasa et al [162]: chaotically oscillating waveforms generated by semiconductor lasers guide the exploration of the different available actions. Then, dynamically adapting the different thresholds used to select one or another action based on the amplitude of the generated waveform at sampling instants shows that such a technique can outperform default MABs such as UCB and ε-greedy in terms of throughput.…”

Section: Channel Bondingmentioning

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

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

“…Out of the box MABs are mainly used to decide which are the best channel widths to be used when no further information, neither from the network nor from the user requirements, is considered, and the goal is to maximize WLAN performance [133], [134], [138]. Then, when traffic loads Figure 11.…”

Section: Channel Bondingmentioning

confidence: 99%

“…HA-DBCA introduces a polling-based adaptive mechanism for contention-free access and uses UCB to identify the stations that are starving, and so allow them to transmit their data during the contention-free access. The channel bonding problem is also modelled as a MAB in [138]. However, in this work, the authors rely on chaotically oscillating waveforms generated by semiconductor lasers to guide exploring the different available actions.…”

Section: Channel Bondingmentioning

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

Dynamic Channel Bonding Using Laser Chaos Decision Maker in WLANs

Cited by 6 publications

References 7 publications

A study on the channel bonding in IoT networks: Requirements, applications, and challenges

A study on the channel bonding in IoT networks: Requirements, applications, and challenges

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

Contact Info

Product

Resources

About