K-Means Based Grouping of Stations with Dynamic AID Assignment in IEEE 802.11ah Networks

Oliveira, Eduardo Carvalho; Soares, Stephanie M.; Carvalho, Marcelo M.

doi:10.1109/msn57253.2022.00034

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…In [10], the authors used ANNs to find the optimal number of RAW groups given the network size, data rate, and RAW duration. Using ML methods such as Kmeans, the authors implemented traffic classification and grouping schemes that can dynamically adapt to various network conditions (e.g., received signal strength, multiple rates, traffic load, and traffic arrival interval) [11,[30][31][32]. In a recent study [33], the authors employed a recurrent neural network based on gated recurrent units to estimate the optimal number of RAW slots, enhancing the performance in dense IEEE 802.11ah IoT network.…”

Section: Ai-based Methods For Raw Mechanismmentioning

confidence: 99%

“…Researchers in [10] used neural networks to decide the optimal number of RAW groups and the number of slots in each RAW for given network conditions. Moreover, machine learning (ML) methods such as K-means have been used to solve grouping problems [11]. It is noteworthy that deep reinforcement learning (DRL) integrates deep learning (DL) and reinforcement learning (RL) by using deep neural networks (DNNs) to approximate value functions or optimal policies, thereby enabling the handling of high-dimensional and complex state and action spaces.…”

Section: Energy Efficiency Scalabilitymentioning

confidence: 99%

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Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer

Jiang,

Gong,

Deng

et al. 2024

Sensors

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The IEEE 802.11ah standard is introduced to address the growing scale of internet of things (IoT) applications. To reduce contention and enhance energy efficiency in the system, the restricted access window (RAW) mechanism is introduced in the medium access control (MAC) layer to manage the significant number of stations accessing the network. However, to achieve optimized network performance, it is necessary to appropriately determine the RAW parameters, including the number of RAW groups, the number of slots in each RAW, and the duration of each slot. In this paper, we optimize the configuration of RAW parameters in the uplink IEEE 802.11ah-based IoT network. To improve network throughput, we analyze and establish a RAW parameters optimization problem. To effectively cope with the complex and dynamic network conditions, we propose a deep reinforcement learning (DRL) approach to determine the preferable RAW parameters to optimize network throughput. To enhance learning efficiency and stability, we employ the proximal policy optimization (PPO) algorithm. We construct network environments with periodic and random traffic in an NS-3 simulator to validate the performance of the proposed PPO-based RAW parameters optimization algorithm. The simulation results reveal that using the PPO-based DRL algorithm, optimized RAW parameters can be obtained under different network conditions, and network throughput can be improved significantly.

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Section: Ai-based Methods For Raw Mechanismmentioning

confidence: 99%

Section: Energy Efficiency Scalabilitymentioning

confidence: 99%

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer

Jiang,

Gong,

Deng

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In [10], authors used ANNs to find the optimal number of RAW groups given the network size, data rate, and RAW duration. Using ML methods such as K-means, the authors implemented traffic classification and grouping schemes that can dynamically adapt to various network conditions (e.g., received signal strength, multiple rates, traffic load, and traffic arrival interval) [11,[30][31][32]. In the recent study [33], the authors employed recurrent neural network based on gated recurrent units to estimate the optimal number of RAW slots, enhancing the performance in dense IEEE 802.11ah IoT network.…”

Section: Ai-based Methods For Raw Mechanismmentioning

confidence: 99%

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer

Jiang,

Gong,

Deng

et al. 2024

Preprint

View full text Add to dashboard Cite

The IEEE 802.11ah standard is introduced to address the growing scale of Internet of Things (IoT) applications. To reduce contention and enhance energy efficiency within the system, the restricted access window (RAW) mechanism is introduced at the medium access control (MAC) layer to manage the significant number of stations accessing the network. However, the RAW parameters need to be appropriately determined to achieve optimal network performance. In this paper, we optimize the configuration of RAW parameters in the IEEE 802.11ah-based IoT network. We analyze and propose an RAW parameters optimization problem with the objective of improving network throughput and formulate it as a Markov decision process. To cope with the complex and dynamic network conditions, we propose a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm to find the optimal RAW parameters. We construct network environments with periodic and random traffic in the NS-3 simulator to validate the performance of the proposed PPO-based RAW parameters optimization algorithm. Simulation results reveal that using the PPO-based DRL algorithm, optimal RAW parameters can be obtained under different network conditions, thereby significantly improving network throughput.

show abstract

K-Means Based Grouping of Stations with Dynamic AID Assignment in IEEE 802.11ah Networks

Cited by 2 publications

References 13 publications

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer

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