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

Kassa, Lemlem; Deng, Jing; Davis, Mark S.; Cai, Jingye

doi:10.5121/csit.2022.121521

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…In [16], Kassa et al proposed a novel method to optimize the frame size of the wireless local area network (WLAN) in downlink multi-user multiple-input multiple-output (MUMIMO) channels. The authors use a machine learning approach based on the artificial neural network (ANN) to predict the optimal frame size for each user based on the channel state information (CSI), to optimize the throughput for each host.…”

Section: Literature Reviewmentioning

confidence: 99%

An Application of Throughput Request Satisfaction Method for Maximizing Concurrent Throughput in WLAN for IoT Application System

Wu,

Funabiki,

Roy

et al. 2024

Sensors

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With the wide applications of the Internet of Things (IoT) in smart home systems, IEEE 802.11n Wireless Local Area Networks (WLANs) have become a frequently chosen communication technology due to their adaptability and affordability. In a high-density network of devices such as the smart home scenerio, a host often meets interferences from other devices and unequal Received Signal Strength (RSS) from Access Points (APs). This results in throughput unfairness/insufficiency problems between hosts communicating concurrently in WLAN. Previously, we have studied the throughput request satisfaction method to address this problem. It calculates the target throughput from measured single and concurrent throughputs of hosts and controls the actual throughput at this target one by applying traffic shaping at the AP. However, the insufficiency problem of maximizing the throughput is not solved due to interferences from other hosts. In this paper, we present an extension of the throughput request satisfaction method to maximize the throughput of a high-priority host under concurrent communications. It recalculates the target throughput to increase the actual throughput as much as possible while the other hosts satisfy the least throughput. For evaluations, we conduct experiments using the test-bed system with Raspberry Pi as the AP devices in several topologies in indoor environments. The results confirm the effectiveness of our proposal.

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Section: Literature Reviewmentioning

confidence: 99%

An Application of Throughput Request Satisfaction Method for Maximizing Concurrent Throughput in WLAN for IoT Application System

Wu,

Funabiki,

Roy

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Machine Learning based Frame Size Optimization in WLAN Downlink MU-MIMO Channel with the Least Cost of Delay

Kassa¹,

Deng²,

Davis³

et al. 2022

IJCNC

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The key enhancement in the medium access control (MAC) layer is frame aggregation introduced by the IEEE 802.11n/ac standard to accommodate the growing traffic demand in the WLAN by allowing multiple packets aggregated per transmission. Frame aggregation efficiently reduces control overhead in the MAC layer, such as the MAC header and thus it helps to enhance transmission efficiency and throughput performance of WLAN. However, heterogeneous traffic demand among streams in the WLAN downlink MU-MIMO channel creates a challenge to efficiently utilize the benefits of frame aggregation. Transmission efficiency is also compromised during frame size setting determination because when a frame size is larger, the impact of the overhead frame can be lower, but they are also more vulnerable to transmission errors. Thus, this trade-off between maximizing frame size and minimizing overhead frames should be addressed by employing an adaptive frame aggregation technique to derive the optimal frame size that would maximize the throughput in WLAN downlink MU-MIMO channel. Moreover, when frame aggregation approach is employed, more frames must wait before transmission in a buffer which causes a delay in the performance of WLAN. Thus, analysing the trade-off between maximizing throughput and minimizing delay is a critical issue that should also be addressed to enhance the performance of WLAN. However, the majority of the existing adaptive aggregation algorithms in the WLAN downlink MU-MIMO channel are focused to maximize the throughput or minimize the delay. The main contribution of this paper is to propose a machine learning-based frame size optimization algorithm by extending our earlier approach in considering the cost of delay to maximize the system throughput of WLAN. The effectiveness of the proposed scheme is evaluated over the FIFO Baseline Approach and earlier conventional approaches under the effects of various traffic patterns, channel conditions, and the number of STAs.

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

Cited by 2 publications

References 19 publications

An Application of Throughput Request Satisfaction Method for Maximizing Concurrent Throughput in WLAN for IoT Application System

An Application of Throughput Request Satisfaction Method for Maximizing Concurrent Throughput in WLAN for IoT Application System

Machine Learning based Frame Size Optimization in WLAN Downlink MU-MIMO Channel with the Least Cost of Delay

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