Performance optimization of QoS-supported dense WLANs using machine-learning-enabled enhanced distributed channel access (MEDCA) mechanism
- Authors
- Ali, Rashid; Nauman, Ali; Zikria, Yousaf Bin; Kim, Byung-Seo; Kim, Sung Won
- Issue Date
- Sep-2020
- Publisher
- SPRINGER LONDON LTD
- Keywords
- QoS-supported WLANs; MAC layer channel access; Machine learning; Dense WLANs; EDCA
- Citation
- NEURAL COMPUTING & APPLICATIONS, v.32, no.17, pp.13107 - 13115
- Journal Title
- NEURAL COMPUTING & APPLICATIONS
- Volume
- 32
- Number
- 17
- Start Page
- 13107
- End Page
- 13115
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/11577
- DOI
- 10.1007/s00521-019-04416-1
- ISSN
- 0941-0643
- Abstract
- Quality of service (QoS) implementation in a wireless local area network (WLAN) enables the prediction of network performance and utilization of effective bandwidth for multimedia applications. In QoS-supported WLAN, enhanced distributed channel access (EDCA) adjusts back-off parameters to implement priority-based channel access at the medium access control (MAC) layer. Although conventional QoS-supported EDCA in WLANs can provide a certain degree of QoS guarantee, the performance of best effort data (low-priority) traffic is sacrificed owing to the blind use of a binary exponential back-off (BEB) mechanism for collision avoidance among WLAN stations (STAs). In EDCA, the BEB mechanism exponentially increases the contention window (CW[AC]) for any specific priority access category (AC) when collision occurs and resets it to its initial size after successful data transmission. This increase and reset ofCW[AC] is performed regardless of the network density inference, i.e., a scarce WLAN does not require an unnecessary exponential increase inCW[AC]. Similarly, a dense WLAN causes more collisions ifCW[AC] is reset to its initial minimum size. Machine-learning algorithms can scrutinize an STA's experience for WLAN inference. Therefore, in this study, we propose a machine-learning-enabled EDCA (MEDCA) mechanism for QoS-supported MAC layer channel access in dense WLANs. This mechanism utilizes a Q-learning algorithm, which is one of the prevailing models of machine learning, to infer the network density and adjust its back-offCW[AC] accordingly. Simulation results show that MEDCA performs better as compared to the conventional EDCA mechanism in QoS-supported dense WLANs.
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Collections - Graduate School > Software and Communications Engineering > 1. Journal Articles
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