Survey on OFDMA based MAC protocols for the next generation WLAN

Li, Bo; Qu, Qiao; Yan, Zhongjiang; Yang, Mao

doi:10.1109/wcncw.2015.7122542

Cited by 40 publications

(14 citation statements)

References 10 publications

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“…After finding the appropriate alien AP or alternative beam direction for associated AP, STA with our algorithm can resume its operation of Tx and Rx. The results of throughput gain simulation show that the proposed algorithm is more effective in densely deployed scenario where 64 STAs affect an STA within a BSS coverage than other scenarios (4,8,16,32 STAs). Thus, the performance of our algorithm for mitigating the interference is effective for both general and densely deployed IoT scenarios.…”

Section: Simulation Results and Discussionmentioning

confidence: 93%

Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks

Kwon

Kim

et al. 2018

Preprint

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Section: Simulation Results and Discussionmentioning

confidence: 93%

Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks

Kwon

Kim

et al. 2018

Preprint

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“…In addition, Li et al [77] propose OFDMA access MAC based on spatial clustering group, which inhibits the interference diffusion of OFDMA in space, and improves the area throughput. Li et al [13] survey the WLAN MAC based on OFDMA access.…”

Section: ) Ofdma Based Mu-macmentioning

confidence: 99%

Survey and Performance Evaluation of the Upcoming Next Generation WLANs Standard - IEEE 802.11ax

Yang

et al. 2019

Mobile Netw Appl

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With the ever-increasing demand for wireless traffic and quality of serives (QoS), wireless local area networks (WLANs) have developed into one of the most dominant wireless networks that fully influence human life. As the most widely used WLANs standard, Institute of Electrical and Electronics Engineers (IEEE) 802.11 will release the upcoming next generation WLANs standard amendment: IEEE 802.11ax. This article comprehensively surveys and analyzes the application scenarios, technical requirements, standardization process, key technologies, and performance evaluations of IEEE 802.11ax. Starting from the technical objectives and requirements of IEEE 802.11ax, this article pays special attention to high-dense deployment scenarios. After that, the key technologies of IEEE 802.11ax, including the physical layer (PHY) enhancements, multi-user (MU) medium access control (MU-MAC), spatial reuse (SR), and power efficiency are discussed in detail, covering both standardization technologies as well as the latest academic studies. Furthermore, performance requirements of IEEE 802.11ax are evaluated via a newly proposed systems and link-level integrated simulation platform (SLISP). Simulations results confirm that IEEE 802.11ax significantly improves the user experience in high-density deployment, while successfully achieves the average per user throughput requirement in project authorization request (PAR) by four times compared to the legacy IEEE 802.11. Finally, potential advancement beyond IEEE 802.11ax are discussed to complete this holistic study on the latest IEEE 802.11ax. To the best of our knowledge, this article is the first study to directly investigate and analyze the latest stable version of IEEE 802.11ax, and the first work to thoroughly and deeply evaluate the compliance of the performance requirements of IEEE 802.11ax.

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“…To elaborately distribute the network traffic to the UAVs, the mathematical model of the wireless access network and the numeric factors that affects the formation control should be designed. Also, there are valuable approaches to alleviate the risk of the packet collision such as Carrier Sense Multiple Access with Collision Detection (CSMA/CD) [28], variations of ALOHA protocol [29], and Orthogonal Frequency-Division Multiple Access (OFDMA) [30]. To give the generic view and enhance the flexibility to the model, we propose a mathematical expression of the aggregated throughput with a protocol-dependent parameter, which could be estimated by comparing the analytic solution and the simulated solution.…”

Section: Wireless Access Networkmentioning

confidence: 99%

Formation Control Algorithm of Multi-UAV-Based Network Infrastructure

Park

Kim

et al. 2018

Applied Sciences

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This paper addresses the analysis and the deployment of the network infrastructure based on multiple Unmanned Air Vehicles (UAVs). Despite the unprecedented potential to the mobility of the network infrastructure, there has been no effort to establish a mathematical model of the infrastructure and formation control strategies. We model the generic dynamics of the network infrastructure and derive the network throughput of the infrastructure. Through the parametrization of the model, we extract the generic factors of the network protocols and verify our model through the Network Simulator 3 (ns-3). By exploiting our network analysis model, we propose a novel formation control algorithm that determines the location of the UAVs to maximize the efficiency of the network. To achieve the objectives of the infrastructure, we define the formation-shaping effect as forces and elaborately design them using the generic factors. The formation algorithm continuously approaches to the optimized formation of a fleet of UAVs to enhance the overall throughput of the terrestrial devices. Our evaluations show that the algorithm guarantees remarkably higher throughput than the static formations. Through the dynamic transformation of the UAV formation, we believe that the multi-UAV-based network infrastructure could expand the boundary of the existing infrastructure while reducing the network traffic.

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Survey on OFDMA based MAC protocols for the next generation WLAN

Cited by 40 publications

References 10 publications

Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks

Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks

Survey and Performance Evaluation of the Upcoming Next Generation WLANs Standard - IEEE 802.11ax

Formation Control Algorithm of Multi-UAV-Based Network Infrastructure

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