Realistic modeling of IEEE 802.11 WLAN considering rate adaptation and multi-rate retry

Kim, Igor; Kim, Young‐Tak

doi:10.1109/tce.2011.6131117

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…For example, the work in [18], models the performance of 802.11g WLAN where stations have different offered loads. Another work in [30] takes into account individual frame error rates at each station to model the performance of 802.11g WLAN, considering rate adaptation algorithm. However, modeling the network with diverse stations comes at a cost -the model becomes more complex.…”

Section: B Three-dimensional Markov Chainmentioning

confidence: 99%

Performance Analysis of Aggregation-Enabled IEEE 802.11 WLANs With Variable Aggregation Size

Seytnazarov,

Jeong,

Jeon

2023

IEEE Access

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Aggregation-enabled wireless local area network (WLAN) technologies such as IEEE 802.11n and 802.11ac are densely deployed for high-speed and wireless Internet access. Conventional approaches for performance modeling of 802.11n/ac assume fully saturated traffic, where stations always have enough packets to transmit aggregate frames of full or fixed aggregation size. However, such an assumption is unrealistic, since stations usually transmit aggregate frames with different aggregation sizes depending on several dynamic factors, such as offered traffic load, number of active stations, transmission rate, and random backoff time. In this work, we propose a new performance model of aggregation-enabled 802.11n/ac WLANs for different traffic loads and buffer sizes with the assumption that stations always have at least one packet to transmit. Unlike conventional models where stations always contend to transmit aggregate frames of maximum or other fixed aggregation size, the proposed model dynamically determines the aggregation size based on given offered traffic load, number of stations, transmission rate, and random backoff process. Performance evaluations showed that the proposed model produces remarkably accurate performance estimates for throughput, especially for higher traffic loads.

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Section: B Three-dimensional Markov Chainmentioning

confidence: 99%

Performance Analysis of Aggregation-Enabled IEEE 802.11 WLANs With Variable Aggregation Size

Seytnazarov,

Jeong,

Jeon

2023

IEEE Access

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“…The rate selection performance is analyzed in in-field experiments adaptation for MIMO systems using both window-based methods [42,43] and SNR directly [44]. Other work has studied the accuracy of throughput estimation using rate adaptation with Minstrel and multi-rate retry under various channel conditions in 802.11 WLAN [45]. In contrast, we leverage a multi-dimensional channel inference algorithm that uses link-layer performance and classification to perform rate selection.…”

Section: Rate Adaptation Algorithmsmentioning

confidence: 99%

Architecture and experimental evaluation of context-aware adaptation in vehicular networks

Liu

Wensowitch

et al. 2020

J Wireless Com Network

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In vehicular networks, the propagation environment changes rapidly for mobile nodes. To achieve high throughput, wireless devices need to be highly adaptive to these environmental changes by altering their transmission parameters across different layers of the network stack. Sensors in mobile and vehicular nodes can be used to form an understanding of the surrounding context. Such contextual awareness is particularly important in vehicular networks as the frequent context switching and increased channel fluctuations can cause existing adaptation protocols to fail to converge to the optimal transmission parameters. In this paper, we leverage information about the environmental context to enable improved rate adaptation performance in vehicular networks. In particular, we propose a classification-based link-level adaptation framework, which can effectively learn the relationship between context information (such as velocity, SNR, and channel type) and the throughput of various transmission modes. We then quantify the throughput improvement using the proposed scheme and show that our proposed framework can significantly enhance the performance of rate adaptation. With experiments on emulated and in-field channels, we observe that the throughput increases by up to 245% over protocols which use SNR alone to make rate decisions. Based on an analysis of attribute importance, we identify channel type as a key parameter that affects classification performance. Since channel type often cannot be directly obtained, we propose a multi-dimensional channel inference method for use when knowledge about the channel type is not available. We demonstrate that the proposed channel inference achieves an accuracy of up to 94% in previously encountered channels and can quickly signal that a channel has not yet been encountered. The robustness of the proposed methods are demonstrated using experimental data from two different hardware platforms and three different carrier frequency bands. Lastly, we evaluate the most predominant Linux-based rate selection algorithm (Minstrel), study the relative rate selection accuracy of our approach, and analyze the key role that the retry mechanism in Minstrel plays on its performance.

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“…The popularity of IEEE 802.11 [2] wireless local area networks (WLANs) is dictated by their low cost, easy installation, and high connection speed. To provide high QoS in these networks, information such as the achievable throughput [3], resource utilisation [4], average delay, and packet loss may require predictive estimations. The performance analysis of IEEE 802.11 networks has been studied in depth by many research works.…”

Section: Introductionmentioning

confidence: 99%

Delay model of multicast protocols co‐existing with legacy unicast in IEEE 802.11 wireless local area network

Kim

Park

2017

IET Communications

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Realistic modeling of IEEE 802.11 WLAN considering rate adaptation and multi-rate retry

Cited by 11 publications

References 18 publications

Performance Analysis of Aggregation-Enabled IEEE 802.11 WLANs With Variable Aggregation Size

Performance Analysis of Aggregation-Enabled IEEE 802.11 WLANs With Variable Aggregation Size

Architecture and experimental evaluation of context-aware adaptation in vehicular networks

Delay model of multicast protocols co‐existing with legacy unicast in IEEE 802.11 wireless local area network

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