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Register a new userA Learning Approach to Wi-Fi Access
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Added by
Thomas Sandholm
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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We show experimentally that workload-based AP-STA associations can improve system throughput significantly. We present a predictive model that guides optimal resource allocations in dense Wi-Fi networks and achieves 72-77% of the optimal throughput with varying training data set sizes using a 3-day trace of real cable modem traffic.
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Given that the accuracy of range-based positioning techniques generally increases with the number of available anchor nodes, it is important to secure more of these nodes. To this end, this paper studies an unsupervised learning technique to obtain the coordinates of unknown nodes that coexist with anchor nodes. As users use the location services in an area of interests, the proposed method automatically discovers unknown nodes and estimates their coordinates. In addition, this method learns an appropriate calibration curve to correct the distortion of raw distance measurements. As such, the positioning accuracy can be greatly improved using more anchor nodes and well-calibrated distance measurements. The performance of the proposed method was verified using commercial Wi-Fi devices in a practical indoor environment. The experiment results show that the coordinates of unknown nodes and the calibration curve are simultaneously determined without any ground truth data.
Real-Time Applications (RTA) are among the most important use cases for future Wi-Fi 7, defined by the IEEE 802.11be standard. This paper studies two backward-compatible channel access approaches to satisfy the strict quality of service (QoS) requirements of RTA on the transmission latency and packet loss rate that have been considered in the 802.11be Task Group. The first approach is based on limiting the transmission duration of non-RTA frames in the network. The second approach is based on preliminary channel access to ensure the timely delivery of RTA frames. With the developed mathematical model of these approaches, it is shown that both of them can satisfy the RTA QoS requirements. At the same time, the preliminary channel access provides up to 60% higher efficiency of the channel usage by the non-RTA traffic in scenarios with very strict RTA QoS requirements or with low intensity of the RTA traffic.
We propose and experimentally evaluate a novel method that dynamically changes the contention window of access points based on system load to improve performance in a dense Wi-Fi deployment. A key feature is that no MAC protocol changes, nor client side modifications are needed to deploy the solution. We show that setting an optimal contention window can lead to throughput and latency improvements up to 155%, and 50%, respectively. Furthermore, we devise an online learning method that efficiently finds the optimal contention window with minimal training data, and yields an average improvement in throughput of 53-55% during congested periods for a real traffic-volume workload replay in a Wi-Fi test-bed.
Wi-Fi is among the most successful wireless technologies ever invented. As Wi-Fi becomes more and more present in public and private spaces, it becomes natural to leverage its ubiquitousness to implement groundbreaking wireless sensing applications such as human presence detection, activity recognition, and object tracking, just to name a few. This paper reports ongoing efforts by the IEEE 802.11bf Task Group (TGbf), which is defining the appropriate modifications to existing Wi-Fi standards to enhance sensing capabilities through 802.11-compliant waveforms. We summarize objectives and timeline of TGbf, and discuss some of the most interesting proposed technical features discussed so far. We also introduce a roadmap of research challenges pertaining to Wi-Fi sensing and its integration with future Wi-Fi technologies and emerging spectrum bands, hoping to elicit further activities by both the research community and TGbf.
Data traffic over cellular networks is exhibiting an ongoing exponential growth, increasing by an order of magnitude every year and has already surpassed voice traffic. This increase in data traffic demand has led to a need for solutions to enhance capacity provision, whereby traffic offloading to Wi-Fi is one means that can enhance realised capacity. Though offloading to Wi-Fi networks has matured over the years, a number of challenges are still being faced by operators to its realization. In this article, we carry out a survey of the practical challenges faced by operators in data traffic offloading to Wi-Fi networks. We also provide recommendations to successfully address these challenges.
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