A new packet scheduling algorithm for access points in crowded WLANs

Yao, Yi; Sheng, Bo; Mi, Ningfang

doi:10.1016/j.adhoc.2015.06.001

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…One of the critical issues in wireless networks is management and resource allocation to achieve more effective throughput and perfect fairness among users, where throughput and fairness are traditional benchmarks for evaluating the performance of a wireless network. Therefore, there are a lot of studies on this topic to improve the performance of the wireless network (Pilosof et al, 2003;Bottigliengo et al, 2004;Bhagwat et al, 1996;Urvoy-Keller & Beylot, 2008;Yao et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

“…To improve the performance of Wi-Fi, we need to achieve high efficiency and high fairness, which are often in conflict with each other. In other words, more efficiency leads to less fairness and vice versa (Lodwal et al, 2019;Yao et al, 2015;Tabany & Guy, 2015;Pantelidou & Ephremides, 2009;Walrand, 2010).…”

Section: Wi-fi Scheduling Basedmentioning

confidence: 99%

“…In (Yao et al, 2015) presents a new algorithm used for packet scheduling, called DAT in a crowded 802.11 WLAN. This work allows access points (APs) to automatically modify the time windows for supplying each active WLAN, dynamically shifting the weight between fairness and efficiency.…”

Section: Packet Schedulingmentioning

confidence: 99%

See 2 more Smart Citations

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

MASRİ¹,

ERDAL²

2022

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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Wireless mobile communications have experienced tremendous growth in the number of users, data rate requirements, and coverage in recent years. As the data rate and system throughput requirements increase, researchers and system designers need to develop efficient methods to meet these requirements with reasonable effort and cost. In this paper, we discuss an efficient approach to deal with diminishing the overhead of Downlink (DL) and feedback using Machine Learning (ML) for 802.11ax. In particular, the antennas that are related to the router were divided into two gatherings. We used good samples of Channel State Information (CSI) that were taken from an open-access dataset and used it to train our linear regression model. The first group of antennas was used as input to our model and the second group was used as the output of the model. In the online mode, we need to estimate only one group of antennas and for the second group, we can predict it using the trained linear regression model by using the estimated CSI group as input to the model. Therefore, the output of the model using as the CSI for the second group. In this way, we can reduce the overhead of the DL in the router as shown in the result table so the router will work more efficiently compared to the existing systems. From the results table in the last section, the average sum rate has increased between 20% and 30%.

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Section: Related Workmentioning

confidence: 99%

Section: Wi-fi Scheduling Basedmentioning

confidence: 99%

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Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

MASRİ¹,

ERDAL²

2022

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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“…The IEEE 802.11 (Wi-Fi) based wireless media access control (MAC) protocol has become a dominant technology that provides convenient access to the internet in a wireless LAN, in which a single access point (AP) can serve the file requests from multiple clients [1]. However, the effective performance of Wi-Fi, in terms of throughput, delay, and stability, is still incomparable to its wireline counterpart.…”

Section: Introductionmentioning

confidence: 99%

CoFi: Coding-Assisted File Distribution over a Wireless LAN

Diao

2019

Symmetry

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The wireless channel is volatile in nature, due to various signal attenuation factors including path-loss, shadowing, and multipath fading. Existing media access control (MAC) protocols, such as the widely adopted 802.11 wireless fidelity (Wi-Fi) family, advocate masking harsh channel conditions with persistent retransmission and backoff, in order to provide a packet-level best-effort service. However, the asymmetry of the network environment of client nodes in space is not fully considered in the method, which leads to the decline of the transmission efficiency of the good ones. In this paper, we propose CoFi, a coding-assisted file distribution protocol for 802.11 based wireless local area networks (LANs). CoFi groups data into batches and transmits a random linear combination of packets within each batch, thereby reducing redundant packet and acknowledgement (ACK) retransmissions when the channel is lossy. In addition, CoFi adopts a MAC layer caching scheme that allows clients to store the overheard coded packets and use such cached packets to assist nearby peers. With this measure, it further improves the effective throughput and shortens the buffering delay when running applications such as bulk data transmission and video streaming. Our trace based simulation demonstrates that CoFi can maintain a similar level of packet delay to 802.11, but increases the throughput performance by a significant margin in a lossy wireless LAN. Furthermore, we perform a reverse-engineering on CoFi and 802.11 using a simple analytical framework, proving that they asymptotically approach different fairness measures, thus resulting in a disparate performance.

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An Enhanced Delay Sensitive Data Packet Scheduling Algorithm to Maximizing the Network Lifetime

Padmavathy¹,

Jayashree

2016

Wireless Pers Commun

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A new packet scheduling algorithm for access points in crowded WLANs

Cited by 9 publications

References 24 publications

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

CoFi: Coding-Assisted File Distribution over a Wireless LAN

An Enhanced Delay Sensitive Data Packet Scheduling Algorithm to Maximizing the Network Lifetime

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