A utility-based resource allocation scheme for IEEE 802.11 WLANs via a machine-learning approach

Wang, Chiapin; Kuo, Wen-Hsing

doi:10.1007/s11276-014-0708-z

Cited by 7 publications

(7 citation statements)

References 31 publications

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“…Nodes rely on CSMA/CA with exponential backoff to access the channel. Some of them fix their CW min value to a standard value (e.g., 16), while others act aggressively and choose low CW min values, allowing them to receive an unfair share of the channel airtime. Let ω be the CW min value of node I, and let w j be the CW min value of node D j ∈ D, j = 1, ..., N .…”

Section: A System Modelmentioning

confidence: 99%

Intelligent-CW: AI-based Framework for Controlling Contention Window in WLANs

Abyaneh

Hirzallah

Krunz

2019

2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

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The heterogeneity of technologies that operate over the unlicensed 5 GHz spectrum, such as LTE-Licensed-Assisted-Access (LAA), 5G New Radio Unlicensed (NR-U), and Wi-Fi, calls for more intelligent and efficient techniques to coordinate channel access beyond what current standards offer. Wi-Fi standards require nodes to adopt a fixed value for the minimum contention window (CW min ), which prohibits a node from reacting to aggressive nodes that set their CW min to small values. To address this problem, we propose a framework called Intelligent-CW (ICW) that allows nodes to adapt their CW min values based on observed transmissions, ensuring they receive their fair share of the channel airtime. The CW min value at a node is set based on a random forest, a machine learning model that includes a large number of decision trees. We train the random forest in a supervised manner over a large number of WLAN scenarios, including different misbehaving and aggressive scenarios. Under aggressive scenarios, our simulation results reveal that ICW provides nodes with higher throughput (153.9% gain) and 64% lower frame latency than standard techniques. In order to measure the fairness contribution of individual nodes, we introduce a new fairness metric. Based on this metric, ICW is shown to provide 10.89× improvement in fairness in aggressive scenarios compared to standard techniques.

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Section: A System Modelmentioning

confidence: 99%

Intelligent-CW: AI-based Framework for Controlling Contention Window in WLANs

Abyaneh

Hirzallah

Krunz

2019

2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

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“…Their QoS constraints are elastic and different with the traffic types, so the accessing probability of them should be precisely selected to balance the energy and preferred QoS. Therefore, the arrangement of the accessing probability is a network utility maximization (NUM) model, which takes the utility function to quantify the "satisfaction" of traffic's QoS [22,25,28].…”

Section: Frame Queuesmentioning

confidence: 99%

“…For resource allocation in cloud and wireless communication, most researches commonly formulate the issue into optimization problems with the subjects of resource limitations, for example, energy consumption [18,20,21], bandwidth [22], financial cost [23], processing time [24], utility function [25], secrecy outage probability [26], and caching [27] The assumption of these approaches is that the resource requirements of different clients or traffic should be explicit, while the summation of total resource is beyond the limitation. In the circumstance of UAV cloud, these approaches are not suitable anymore because the model is different.…”

Section: Introductionmentioning

confidence: 99%

BP Network Control for Resource Allocation and QoS Ensurance in UAV Cloud

Gao

et al. 2018

Journal of Sensors

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Unmanned aerial vehicle (UAV) cloud can greatly enhance the intelligence of unmanned systems by dynamically unloading the compute-intensive applications to cloud. For the uncertain nature of UAV missions and fast-changing environment, different UAV applications may have different quality of service (QoS) requirements. This paper proposes a mixed QoS ensurance and energy-balanced (MQEB) architecture for UAV cloud from a view of control theory, which can support both hard and soft QoS ensurance with the consideration of energy saving. The hard and soft QoS requirements are decoupled by being normalized into a two-level cascaded feedback loop. The former is time slot loop (TS-Loop) to enforce the absolute QoS ensurance for real-time applications, and the latter is contention window loop (CW-Loop) to enforce the plastic QoS ensurance for non-real-time applications. Finally, the back propagating (BP) neuron network is used for parameters' self-tuning and controller design. The hardware experiments demonstrate the feasibility of MQEB. In heavy load, MQEB has greater throughput and better energy efficiency, and in light load, MQBE has lower total power consumption.

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“…Chiapin Wang et al purposed to use BP neural networks (NN) for management of WLAN resource allocation. [16] By implementing NN, the system is able to regulate itself accordingly and dynamically so that utility can be maximized. Pochiang Lin et al tackle the optimization of frame-size using a ML-based adaptive approach.…”

Section: Machine Learning: An Intelligent Futurementioning

confidence: 99%

A Survey of IEEE 802.11 Protocols: Comparison and Prospective

Chen¹

2017

Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 20

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Abstract. Since the base version of IEEE 802.11 released in 1997, wireless local area network (WLAN) has been developing rapidly, and it has gone through many versions and amendments. The 802.11 protocol is now very sophisticated through 20 years of modification. Therefore, it is very hard to understand all improvements, which confuses researchers sometimes, making it not easy to discover the trend of development. This paper tries to summarize the key technologies that pushed WLAN forward among all important amendments, and then lists the latest research focus for the next generation of 802.11 family. The paper would provide assist researchers to find their field of interest.

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A utility-based resource allocation scheme for IEEE 802.11 WLANs via a machine-learning approach

Cited by 7 publications

References 31 publications

Intelligent-CW: AI-based Framework for Controlling Contention Window in WLANs

Intelligent-CW: AI-based Framework for Controlling Contention Window in WLANs

BP Network Control for Resource Allocation and QoS Ensurance in UAV Cloud

A Survey of IEEE 802.11 Protocols: Comparison and Prospective

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