E-model: An analytical tool for fast adaptation of IEEE 802.11 ah RAW grouping strategies

Baños-Gonzalez, Victor; López-Aguilera, Elena; García-Villegas, Eduard

doi:10.1109/globecom42002.2020.9348179

Cited by 2 publications

(3 citation statements)

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“…As discussed in Section 3.1 , the Bianchi model for multi-rate environments [ 19 ] adapted to IEEE 802.11ah PHY and MAC [ 20 ] is employed for throughput estimation. The adapted Bianchi’s model provides an equation system to obtain a STA’s transmission probability (from which the collision probability P c is obtained as a function of the number of stations N per RAW slot, P c (N) ), which is solved by means of numerical methods thus entailing high complexity.…”

Section: Discussionmentioning

confidence: 99%

“…Targeting an efficient use of the spectrum, the fitness value used in the proposed GA considers the system’s potential throughput. As a starting point, the throughput computation given by the Bianchi model for multi-rate and heterogeneous environments [ 19 ] adapted to IEEE 802.11ah PHY and MAC [ 20 ] is used. This model concludes with the following expression for total system throughput, S :

where R is the number of RAW groups, each of them having k r slots, d r corresponds to the duration of the r th RAW group, and d total represents the total duration of all R RAW groups together.…”

Section: Problem Definition and Methodology: A Grouping Strategy For ...mentioning

confidence: 99%

“…S slot_k stands for the throughput corresponding to the k th slot; its computation is taken from the Bianchi model for multi-rate environments [ 19 ]. The reader is referred to [ 20 ] for further detail on the computation of S .…”

Section: Problem Definition and Methodology: A Grouping Strategy For ...mentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%