Performance optimization of QoS-supported dense WLANs using machine-learning-enabled enhanced distributed channel access
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GREEN AND HUMAN INFORMATION TECHNOLOGY 2019
Performance optimization of QoS-supported dense WLANs using machine-learning-enabled enhanced distributed channel access (MEDCA) mechanism Rashid Ali1 • Ali Nauman1 • Yousaf Bin Zikria1 • Byung-Seo Kim2 • Sung Won Kim1 Received: 22 April 2019 / Accepted: 30 July 2019 Ó Springer-Verlag London Ltd., part of Springer Nature 2019
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 of CW[AC] is performed regardless of the network density inference, i.e., a scarce WLAN does not require an unnecessary exponential increase in CW[AC]. Similarly, a dense WLAN causes more collisions if CW[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-off CW[AC] accordingly. Simulation results show that MEDCA performs better as compared to the conventional EDCA mechanism in QoS-supported dense WLANs. Keywords QoS-supported WLANs MAC layer channel access Machine learning Dense WLANs EDCA
1 Introduction & Sung Won Kim [email protected] Rashid Ali [email protected] Ali Nauman [email protected] Yousaf Bin Zikria [email protected] Byung-Seo Kim [email protected] 1
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
2
Department of Computer and Information Communication Engineering, Hongik University, Seoul 04066, Republic of Korea
Recently, multimedia data traffic, such as audio and video, in wireless local area networks (WLANs) has been receiving much attention. With the increase in the popularity of WLAN-enabled smart devices, such as smartphones, laptops and tablets, the requirement of multimedia applications is becoming an interesting research area for academic and industrial researchers. One of the key research interests is the strict loss and delay b
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