Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah

Tian, Le; Khorov, Evgeny; Latré, Steven; Famaey, Jeroen

doi:10.3390/s17071559

Cited by 61 publications

(41 citation statements)

References 30 publications

(62 reference statements)

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“…For training simplicity, we assume each station sends one packet per second and a small buffer size of 10 packets is used. The built model can be further used by the RAW optimization algorithms, such as TAROA [9], [10] and MoROA [11], [12], to calculate RAW performance under arbitrary data transmission intervals.…”

Section: A Training Scenariosmentioning

confidence: 99%

“…To the best of our knowledge, this is the first RAW model that supports heterogeneous stations in terms of different modulation and coding schemes (MCSs) and packet sizes. The built model can be used as an input for real-time optimization algorithms such as [9], [10], [11], [12] to get optimal RAW configurations for IEEE 802.11ah heterogeneous networks, achieving highest performance.…”

Section: Introductionmentioning

confidence: 99%

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Optimization-Oriented RAW Modeling of IEEE 802.11ah Heterogeneous Networks

Tian

López-Aguilera

García-Villegas

et al. 2019

IEEE Internet Things J.

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The new medium access method of IEEE 802.11ah, called Restricted Access Window (RAW), divides stations into different groups, and only allows stations in the same group to access the channel simultaneously, in order to reduce collisions and thus achieve better performance (e.g., throughput). However, the existing station grouping strategies only support homogeneous scenarios where all stations use the same modulation and coding scheme (MCS) and packet size. A surrogate model is an efficient mathematical model that represents the behavior of a complex system, trained with a limited set of labeled input-output data samples. In this paper, we present a surrogate model that can accurately predict RAW performance under a given Restricted Access Window (RAW) configuration in heterogeneous networks. Different from the homogeneous scenario, heterogeneous networks are defined by a large number of parameters, leading to an enormous design space, i.e., the order of 10 17 possible data points. This is too big to achieve feasible training convergence. In this paper, we present a novel training methodology that leads to a new design space with highly reduced size, i.e., the order of 10 5 data points. The surrogate model converges when less than 6000 labeled data points are used for training, which is only a tiny portion of the whole design space. The results show that, the relative error between model prediction and simulation results is less than 0.1 for 95% of the data points, in the areas of the design space studied. Its low complexity and high precision make the proposed model a valuable tool to develop real-time RAW optimization algorithms for heterogeneous IEEE 802.11ah networks.Index Terms-IEEE 802.11ah, RAW, surrogate model, heterogeneous networks.

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Section: A Training Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization-Oriented RAW Modeling of IEEE 802.11ah Heterogeneous Networks

Tian

López-Aguilera

García-Villegas

et al. 2019

IEEE Internet Things J.

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“…It estimates the network conditions every beacon interval based on network information obtained from the AP, and updates the RAW parameters (cf., Table 1). More details about E-TAROA can be found in our prior work [10,11].…”

Section: Raw Optimization Algorithmsmentioning

confidence: 99%

“…In this extended version, a dynamic RAW configuration interface (implemented in the S1gCtrl Class) is added to allow any user defined RAW optimization algorithm (e.g., Traffic-Aware RAW Optimization Algorithm (TAROA) [10], Enhanced Traffic-Aware RAW Optimization Algorithm (E-TAROA) [11]) to change the RAW parameters in real-time. The dynamic RAW configuration algorithm is executed when the AP generates the beacon frame.…”

Section: Raw Configuration Interfacementioning

confidence: 99%

“…Current research on the MAC layer of 802.11ah mainly focuses on performance of the RAW [5,[10][11][12] and TIM mechanisms [3,7,8]. Originally, much of this research was based on mathematical modeling of the saturated network state, which does not accurately capture real network behavior and is hard to adapt to non-saturated network conditions.…”

Section: Introductionmentioning

confidence: 99%

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Extension of the IEEE 802.11ah ns-3 simulation module

Tian

Šljivo

Santi

et al. 2018

Proceedings of the 10th Workshop on Ns-3 - WNS3 '18

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IEEE 802.11ah, marketed as Wi-Fi HaLow, is a new Wi-Fi standard for sub-1Ghz communications, aiming to address the major challenges of the Internet of Things (IoT), namely connectivity among a large number of densely deployed power-constrained stations. The standard was only published in May 2017 and hardware supporting Wi-Fi HaLow is not available on the market yet. As such, research on 802.11ah has been mostly based on mathematical and simulation models. Mathematical models generally introduce several simplifications and assumptions, which do not faithfully reflect real network conditions. As a solution, we previously developed an IEEE 802.11ah module for ns-3, publicly released in 2016. This initial release consisted of physical layer models for sub-1GHz communications and an implementation of the fast association and Restricted Access Window (RAW) channel access method. In this paper, we present an extension to our IEEE 802.11ah simulator. It contains several new features: an online RAW configuration interface, an energy state model, adaptive Modulation and Coding Scheme (MCS), and Traffic Indication Map (TIM) segmentation. This paper presents the details of our implementation, along with experimental results to validate each new feature. The extended Wi-Fi HaLow module can now support different scenarios with both uplink and downlink heterogeneous traffic, together with real-time RAW optimization, sleep management for energy conservation and adaptive MCS.

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