Data rate-based grouping using machine learning to improve the aggregate throughput of IEEE 802.11ah multi-rate IoT networks

Mahesh, Miriyala; Pavan, Badarla Sri; Harigovindan, V. P.

doi:10.1109/ants50601.2020.9342758

Cited by 10 publications

(11 citation statements)

References 11 publications

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“…The coexistence of STAs using different MCS impacts fairness and the available throughput [ 14 ] and should therefore be considered when configuring the RAW. In [ 15 ] and [ 16 ], authors deal with those heterogeneous scenarios by grouping STAs with the same PHY rate. However, as discussed in Section 4.1 , mixing STAs with different PHY rates in a particular way can result in a better fairness and throughput.…”

Section: Related Work On Raw Station Groupingmentioning

confidence: 99%

Genetic Algorithm-Based Grouping Strategy for IEEE 802.11ah Networks

García-Villegas

López‐García

López-Aguilera

2023

Sensors

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The IEEE 802.11ah standard is intended to adapt the specifications of IEEE 802.11 to the Internet of Things (IoT) scenario. One of the main features of IEEE 802.11ah consists of the Restricted Access Window (RAW) mechanism, designed for scheduling transmissions of groups of stations within certain periods of time or windows. With an appropriate configuration, the RAW feature reduces contention and improves energy efficiency. However, the standard specification does not provide mechanisms for the optimal setting of RAW parameters. In this way, this paper presents a grouping strategy based on a genetic algorithm (GA) for IEEE 802.11ah networks operating under the RAW mechanism and considering heterogeneous stations, that is, stations using different modulation and coding schemes (MCS). We define a fitness function from the combination of the predicted system throughput and fairness, and provide the tuning of the GA parameters to obtain the best result in a short time. The paper also includes a comparison of different alternatives with regard to the stages of the GA, i.e., parent selection, crossover, and mutation methods. As a proof of concept, the proposed GA-based RAW grouping is tested on a more constrained device, a Raspberry Pi 3B+, where the grouping method converges in around 5 s. The evaluation concludes with a comparison of the GA-based grouping strategy with other grouping approaches, thus showing that the proposed mechanism provides a good trade-off between throughput and fairness performance.

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Section: Related Work On Raw Station Groupingmentioning

confidence: 99%

Genetic Algorithm-Based Grouping Strategy for IEEE 802.11ah Networks

García-Villegas

López‐García

López-Aguilera

2023

Sensors

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“…Other applications of ML to 802.11ah include: improving coexistence with 802. 15.4g devices, a type of lowrate wireless personal area network (LR-WPAN), by avoiding interference with their transmissions using a Q-learning-based backoff mechanism [322], grouping sensors based on their traffic demands and channel conditions using a regressionbased model [323], grouping sensors based on their data rates by classifying them with NNs [324], and improving carrier frequency offset estimation using various types of DNNs [90].…”

Section: Sensor 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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“…A similar problem is also addressed in [273], where an MLP NN configures these parameters considering, i.a., network size and the MCS values used. Other applications of ML to 802.11ah include: improving coexistence with 802.15.4g devices, a type of low-rate wireless personal area network (LR-WPAN), by avoiding interference with their transmissions using a Q-learning-based backoff mechanism [274], grouping sensors based on their traffic demands and channel conditions using a regression-based model [275], grouping sensors based on their data rates by classifying them with NNs [276], and improving carrier frequency offset estimation using various types of DNNs [277].…”

Section: Sensor 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.

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Data rate-based grouping using machine learning to improve the aggregate throughput of IEEE 802.11ah multi-rate IoT networks

Cited by 10 publications

References 11 publications

Genetic Algorithm-Based Grouping Strategy for IEEE 802.11ah Networks

Genetic Algorithm-Based Grouping Strategy for IEEE 802.11ah Networks

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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