Do you want to publish a course? Click here

Tuning Channel Access to Enable Real-Time Applications in Wi-Fi 7

94   0   0.0 ( 0 )
 Added by Dmitry Bankov
 Publication date 2021
and research's language is English




Ask ChatGPT about the research

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.



rate research

Read More

In 2019 IEEE 802 LAN/MAN Standards Committee started the development of the next major amendment of the Wi-Fi standard: the IEEE 802.11be, also known as Wi-Fi 7. This new amendment will introduce many new functions and will improve the existing ones that will make Wi-Fi more efficient in many new scenarios. One of the scenarios is the service of Real-Time Applications with strict requirements on latency and reliability of communications. Providing low latencies can be challenging in Wi-Fi because of the unlicensed spectrum and related interference from neighboring devices. In this paper, we consider the usage of OFDMA transmissions for Real-Time Applications and design resource allocation algorithms that can provide the required latency and reliability in the presence of interference.
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.
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.
To enhance the mobility and convenience of the campus community, we designed and implemented the Pulse system, a visual interface for communicating the crowd information to the lay public including campus members and visitors. This is a challenging task which requires analyzing and reconciling the demands and interests for data as well as visual design among diverse target audiences. Through an iterative design progress, we study and address the diverse preferences of the lay audiences, whereby design rationales are distilled. The final prototype combines a set of techniques such as chart junk and redundancy encoding. Initial feedback from a wide audience confirms the benefits and attractiveness of the system.
Time-of-flight, i.e., the time incurred by a signal to travel from transmitter to receiver, is perhaps the most intuitive way to measure distances using wireless signals. It is used in major positioning systems such as GPS, RADAR, and SONAR. However, attempts at using time-of-flight for indoor localization have failed to deliver acceptable accuracy due to fundamental limitations in measuring time on Wi-Fi and other RF consumer technologies. While the research community has developed alternatives for RF-based indoor localization that do not require time-of-flight, those approaches have their own limitations that hamper their use in practice. In particular, many existing approaches need receivers with large antenna arrays while commercial Wi-Fi nodes have two or three antennas. Other systems require fingerprinting the environment to create signal maps. More fundamentally, none of these methods support indoor positioning between a pair of Wi-Fi devices without~third~party~support. In this paper, we present a set of algorithms that measure the time-of-flight to sub-nanosecond accuracy on commercial Wi-Fi cards. We implement these algorithms and demonstrate a system that achieves accurate device-to-device localization, i.e. enables a pair of Wi-Fi devices to locate each other without any support from the infrastructure, not even the location of the access points.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا