4,116 research outputs found
Load-aware Channel Selection for 802.11 WLANs with Limited Measurement
It has been known that load unaware channel selection in 802.11 networks
results in high level interference, and can significantly reduce the network
throughput. In current implementation, the only way to determine the traffic
load on a channel is to measure that channel for a certain duration of time.
Therefore, in order to find the best channel with the minimum load all channels
have to be measured, which is costly and can cause unacceptable communication
interruptions between the AP and the stations. In this paper, we propose a
learning based approach which aims to find the channel with the minimum load by
measuring only limited number of channels. Our method uses Gaussian Process
Regressing to accurately track the traffic load on each channel based on the
previous measured load. We confirm the performance of our algorithm by using
experimental data, and show that the time consumed for the load measurement can
be reduced up to 46% compared to the case where all channels are monitored.Comment: accepted to IC
Why It Takes So Long to Connect to a WiFi Access Point
Today's WiFi networks deliver a large fraction of traffic. However, the
performance and quality of WiFi networks are still far from satisfactory. Among
many popular quality metrics (throughput, latency), the probability of
successfully connecting to WiFi APs and the time cost of the WiFi connection
set-up process are the two of the most critical metrics that affect WiFi users'
experience. To understand the WiFi connection set-up process in real-world
settings, we carry out measurement studies on million mobile users from
representative cities associating with million APs in billion WiFi
sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS
App market. To the best of our knowledge, we are the first to do such large
scale study on: how large the WiFi connection set-up time cost is, what factors
affect the WiFi connection set-up process, and what can be done to reduce the
WiFi connection set-up time cost. Based on the measurement analysis, we develop
a machine learning based AP selection strategy that can significantly improve
WiFi connection set-up performance, against the conventional strategy purely
based on signal strength, by reducing the connection set-up failures from
to and reducing time costs of the connection set-up
processes by more than times.Comment: 11pages, conferenc
Building accurate radio environment maps from multi-fidelity spectrum sensing data
In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g., Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated
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