FDoF: Enhancing Channel Utilization for 802.11ac

Lin, Chi-Han; Chen, Yi-Ting; Lin, Kate Ching-Ju; Chen, Wen-Tsuen

doi:10.1109/tnet.2017.2785880

Cited by 15 publications

(9 citation statements)

References 43 publications

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“…For more realistic results, this study will extend the limitation of this work to improve the system performance by considering the effect of channel error, which has a significant role in the determination of the optimal frame aggregation size before transmission. Focusing on the padding problem of downlink MU-MIMO, some research work has been carried out by [12,13] mainly to increase the transmission efficiency of multiuser (MU) frames and minimize space channel time. The proposed scheme replaces padding bits with the data frame of other stations.…”

Section: Related Workmentioning

confidence: 99%

“…Space channel time can be avoided by adding frame padding dummy bits at the tail of the shorter frame until it has the same transmission duration as the others in downlink MU-MIMO transmission. However, this approach leads to degradation in transmission efficiency [8,9,12,13]. Emphasizing the challenges of heterogeneous traffic demand and transmission bit rate among stations, ref.…”

Section: Introductionmentioning

confidence: 99%

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Performance of an Adaptive Aggregation Mechanism in a Noisy WLAN Downlink MU-MIMO Channel

et al. 2022

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This paper investigates an adaptive frame aggregation technique in the medium access control (MAC) layer for the Wireless Local Area Network (WALN) downlink Multi-User–Multiple-In Multiple-Out (MU-MIMO) channel. In tackling the challenges of heterogeneous traffic demand among spatial streams, we proposed a new adaptive aggregation algorithm which has a superior performance over the baseline First-in–First-Out (FIFO) scheme in terms of system throughput performance and channel utilization. However, this earlier work does not consider the effects of wireless channel error. In addressing the limitations of this work, this study contributes an enhanced version of the earlier model considering the effect of channel error. In this approach, a dynamic adaptive aggregation selection scheme is proposed by employing novel criteria for selecting the optimal aggregation policy in WLAN downlink MU-MIMO channel. Two simulation setups are conducted to achieve this approach. The simulation setup in Step 1 performs the dynamic optimal aggregation policy selection strategy as per the channel condition, traffic pattern, and number of stations in the network. Step 2 then performed the optimal wireless frame construction that would be transmitted in the wireless channel in adopting the optimal aggregation policy obtained from Step 1 that maximizes the system performance. The proposed adaptive algorithm not only achieve the optimal system throughput in minimizing wasted space channel time but also provide a good performance under the effects of different channel conditions, different traffic models such as Pareto, Weibull, and fBM, and number of users using the traffic mix of VoIP and video data. Through system-level simulation, our results again show the superior performance of our proposed aggregation mechanism in terms of system throughput performance and space channel time compared to the baseline FIFO aggregation approach.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance of an Adaptive Aggregation Mechanism in a Noisy WLAN Downlink MU-MIMO Channel

et al. 2022

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“…Moreover, some studies were contributed frame size aggregation schemes in WLAN downlink MU-MIMO channels to enhance the throughput performance [15][16][17][18][19][20][21][22]. The algorithm in [15] proposed a new approach aiming to enhance the system throughput performance of WLAN employing a dynamic adaptive aggregation selection scheme to determine the optimal length of frame size in downlink MU-MIMO transmission.…”

Section: Related Workmentioning

confidence: 99%

“…Some works in the literature have also been studied focusing on the padding problem. According to [20,21], they improved transmission efficiency in the downlink MU-MIMO channel by replacing padding bits with data frames from other users in one stream to fill the space of frame padding violating the rules of MU transmissions. However, these approaches increased the complexity of both the transmission and reception process in wireless communication which requires modification of the standard to allow the transmission to multiple destinations within a special stream.…”

Section: Related Workmentioning

confidence: 99%

Frame Size Optimization Using a Machine Learning Approach in WLAN Downlink MU-MIMO Channel

Kassa¹,

Deng²,

Davis³

et al. 2022

Data Science and Machine Learning

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Machine Learning (ML) is an innovative solution that can autonomously extract patterns and predict trends based on environmental measurements and performance indicators as input to provide self-driven intelligent network systems that can configure and optimize themselves. Under the effects of heterogeneous traffic demand among users and varying channel conditions in WLAN downlink MU-MIMO channels, achieving the maximum system throughput performance is challenging. In addressing these issues, the existing studies have proposed different approaches. However, most of these approaches did not consider a machine-learning based optimization solution. The main contribution of this paper is to propose a machine-learning based adaptive approach that can optimize system frame size that would maximize the system throughput of WLAN in the downlink MUMIMO channel. In this approach, the Access Point (AP) performs the maximum system throughput measurement and collects the “frame size-system throughput patterns” which contains knowledge about the effects of traffic condition, channel condition, and number of stations (STAs). Based on these patterns, our approach uses neural networks to correctly model the system throughput as a function of the system frame size. After training the neural network, we obtain the gradient information to adjust the system frame size. The performance of the proposed ML approach is evaluated over the FIFO aggregation algorithm under the effects of heterogenous traffic patterns for VoIP and Video traffic applications, channel conditions, and number of STAs.

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“…Hacı Bayram KARAKURT 1 Cemal KOÇAK [21]. Başka bir çalışmada da dinamik arabellek boyutu ve grafik teorisi ile "en kötü durum zaman karmaşıklığı" kullanılıp kanal kullanım oranı artırılmıştır [22].…”

Section: Kablosuz Yerel Alan Ağlarında Kanal Kullanımının Mac Katmanında Döngü Tablosu Ile Artırılmasıunclassified

Kablosuz Yerel Alan Ağlarında Kanal Kullanımının MAC Katmanında Döngü Tablosu ile Artırılması

Karakurt¹,

Koçak

2020

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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FDoF: Enhancing Channel Utilization for 802.11ac

Cited by 15 publications

References 43 publications

Performance of an Adaptive Aggregation Mechanism in a Noisy WLAN Downlink MU-MIMO Channel

Performance of an Adaptive Aggregation Mechanism in a Noisy WLAN Downlink MU-MIMO Channel

Frame Size Optimization Using a Machine Learning Approach in WLAN Downlink MU-MIMO Channel

Kablosuz Yerel Alan Ağlarında Kanal Kullanımının MAC Katmanında Döngü Tablosu ile Artırılması

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