Design of MAC Layer Resource Allocation Schemes for IEEE 802.11ax: Future Directions

Ali, Rashid; Kim, Sung Won; Kim, Byung-Seo; Park, Yongwan

doi:10.1080/02564602.2016.1242387

Cited by 40 publications

(30 citation statements)

References 61 publications

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“…We Future Internet 2020, 12, 3 9 of 16 now only consider one transmission cycle to facilitate the evaluation of DMAC. In each CP, the number of nodes that successfully contend for the channel is calculated as: E[n] = np tr p s (12) In the TP, the time that spends on transmitting a packet is given by:…”

Section: Performance Analysismentioning

confidence: 99%

“…T SIFS is time of a SIFS, and T DATA is the time of data transmission. Hence, when combing Equation (1)-(5), (12), and (13), we can obtain the saturated throughput of DMAC, denoted as:…”

Section: Performance Analysismentioning

confidence: 99%

“…In 802.11ax, a full channel is divided into multiple orthogonal sub-channels based on OFDMA technology, so as to support multi-user parallel access, and the AP is in charge of scheduling both downlink (DL) and uplink (UL) transmissions. In addition, in the UL transmission phase, a hybrid channel allocation mechanism, including random access and scheduled access, is also proposed [12].In this paper, from another perspective, we focus on how to avoid the intensive channel contention and solve the performance anomaly in dense multi-rate WLANs. We introduce a decoupling MAC mechanism, namely decoupling MAC mechanism (DMAC), to achieve these goals.…”

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confidence: 99%

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confidence: 99%

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Decoupling-Based Channel Access Mechanism for Improving Throughput and Fairness in Dense Multi-Rate WLANs

2019

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Legacy IEEE 802.11 Medium Access Control (MAC) adopts the Distributed Coordination Function (DCF) mechanism, which provides the same access opportunity for all contenders. However, in dense multi-rate Wireless Local Area Networks (WLANs), the pure distributed control mechanism will cause high collision rate and performance anomaly, which results in low network utilization and wasting valuable channel resources. In this paper, we present a decoupling MAC mechanism (DMAC) based on the idea of contention/reservation to reduce collision and realize collision free data transmission. In proposed mechanism, the channel access time is partitioned into channel contention process and data transmission process. The proposed algorithm makes full use of the distributed random channel access mechanism and performs a centralized collision-free data transmission. Wherein, we also design an adaptive algorithm to adjust the length of the contention period to improve the channel utilization. Furthermore, we further propose two airtime fairness algorithms Improve-DMAC1 (I-DMAC1) and Improve-DMAC2 (I-DMAC2) for delay sensitive network and high throughput network scenarios, respectively, to solve the performance anomaly in multi-rate WLANs, based on DMAC. We verify the effectiveness of these decoupling algorithms through extensive simulations. Moreover, the simulation results show that the proposed algorithms achieve better performance than the 802.11 standard and other protocols.

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Section: Performance Analysismentioning

confidence: 99%

“…T SIFS is time of a SIFS, and T DATA is the time of data transmission. Hence, when combing Equation (1)-(5), (12), and (13), we can obtain the saturated throughput of DMAC, denoted as:…”

Section: Performance Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Decoupling-Based Channel Access Mechanism for Improving Throughput and Fairness in Dense Multi-Rate WLANs

2019

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“…Both academic and industrial communities have recognized the significant attention given to future WLANs (IEEE 802.11) for IoT-based eHealth systems. One of their motivating services is the promisingly high throughput to support extensively advanced technologies even in densely deployed devices environment [10,11]. However, unlicensed WLAN would face huge challenges in the future to access the shared channel resources, especially for highly dense IoT device deployments.…”

Section: Introductionmentioning

confidence: 99%

Q-learning-enabled channel access in next-generation dense wireless networks for IoT-based eHealth systems

Ali

Qadri

Zikria

et al. 2019

J Wireless Com Network

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One of the key applications for the Internet of Things (IoT) is the eHealth service that targets sustaining patient health information in digital environments, such as the Internet cloud with the help of advanced communication technologies. In eHealth systems, wireless networks, such as wireless local area networks (WLAN), wireless body sensor networks (WBSN), and wireless medical sensor networks (WMSNs), are prominent technologies for early diagnosis and effective cures. The next generation of these wireless networks for IoT-based eHealth services is expected to confront densely deployed sensor environments and radically new applications. To satisfy the diverse requirements of such dense IoT-based eHealth systems, WLANs will have to face the challenge of assisting medium access control (MAC) layer channel access in intelligent adaptive learning and decision-making. Machine learning (ML) offers services as a promising machine intelligence tool for wireless-enabled IoT devices. It is anticipated that upcoming IoT-based eHealth systems will independently access the most desired channel resources with the assistance of sophisticated wireless channel condition inference. Therefore, in this study, we briefly review the fundamental models of ML and discuss their employment in the persuasive applications of IoT-based systems. Furthermore, we propose Q-learning (QL) that is one of the reinforcement learning (RL) paradigms as the future ML paradigm for MAC layer channel access in next-generation dense WLANs for IoT-based eHealth systems. Our goal is to contribute to refining the motivation, problem formulation, and methodology of powerful ML algorithms for MAC layer channel access in the framework of future dense WLANs. This paper also presents a case study of next-generation WLAN IEEE 802.11ax that utilizes the QL algorithm for intelligent MAC layer channel access. The proposed QL-based algorithm optimizes the performance of WLAN, especially for densely deployed devices environment.

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Connectivity through Wireless Communications and Sensors

Abadi¹,

Khalaj²

2022

Industry 4.0 Vision for Energy and Materials

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Design of MAC Layer Resource Allocation Schemes for IEEE 802.11ax: Future Directions

Cited by 40 publications

References 61 publications

Decoupling-Based Channel Access Mechanism for Improving Throughput and Fairness in Dense Multi-Rate WLANs

Decoupling-Based Channel Access Mechanism for Improving Throughput and Fairness in Dense Multi-Rate WLANs

Q-learning-enabled channel access in next-generation dense wireless networks for IoT-based eHealth systems

Connectivity through Wireless Communications and Sensors

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