Predictable 802.11 packet delivery from wireless channel measurements

By Daniel Halperin, Wenjun Hu, Anmol Sheth, David Wetherall. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2010

764 citations recorded by the source corpus.

RSSI is known to be a fickle indicator of whether a wireless link will work, for many reasons. This greatly complicates operation because it requires testing and adaptation to find the best rate, transmit power or other parameter that is tuned to boost performance. We show that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide. Our model uses 802.11n Channel State Information measurements as input to an OFDM receiver model we develop by using the concept of effective SNR. It is simple, easy to deploy, broadly useful, and accurate. It makes packet delivery predictions for 802.11a/g SISO rates and 802.11n MIMO rates, plus choices of transmit power and antennas. We report testbed experiments that show narrow transition regions (<2 dB for most links) similar to the near-ideal case of narrowband, frequency-flat channels. Unlike RSSI, this lets us predict the highest rate that will work for a link, trim transmit power, and more. We use trace-driven simulation to show that our rate prediction is as good as the best rate adaptation algorithms for 802.11a/g, even over dynamic channels, and extends this good performance to 802.11n.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/585f525f10b89689467c28b5d99c1eeb4950a1d8

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Citations in the corpus

LiFS: low human-effort, device-free localization with fine-grained subcarrier information (2016, methodology): “The performance of RASS and Pilot is not as good as LiFS since the CSI measurements vary over time [9].”
CSI-Based Indoor Localization (2013, background): “Nowadays, adaptive transmission systems in wireless communication always improve the throughput by utilizing some knowledge of the channel state to adapt or allocate transmitter resources [6].”
Non-Invasive Detection of Moving and Stationary Human With WiFi (2015, background): “…modiﬁcation, a group of 30 subcarrier channel measurements can be revealed to upper layer users in the format of Channel State Information (CSI) [9]: where H k is the CSI at the subcarrier k with central frequency of w k , and (cid:107) H k (cid:107) and (cid:54) H k denote its amplitude and…”
DeepFi: Deep learning for indoor fingerprinting using channel state information (2015, background): “It is now possible to obtain channel state information (CSI) from some advanced WiFi network interface cards (NIC), which can be used as fingerprints to improve the performance of indoor localization [7], [8].”
A Survey of Wireless Path Loss Prediction and Coverage Mapping Methods (2013, background): “In this way, frequency and time selective fades can be simulated, if not predicted exactly, which allows for the analysis of their effect on modulation schemes (e.g., [38], [84]).”
We Can Hear You with Wi-Fi! (2014, background): “[21] and [20] present a combination of acoustic speech and mouth movement image to achieve higher accuracy of automatic speech recognition in noisy environment.”
From RSSI to CSI (2013, methodology): “Since current off-the-shelf CSIs are only available with Intel 5300 NIC and the modified driver [Halperin et al. 2010], it poses a challenge to employ CSIs on mobile handhelds.”
Tool release: gathering 802.11n traces with channel state information (2011, background): “It explains why some high-RSSI links perform poorly, and enables accurate selection of 802.11n rates and transmit power levels across a wide variety of real wireless links.”
MegaMIMO : Scaling Wireless Capacity with User Demands (2012, methodology): “11 modulations (BPSK, 4QAM, 16QAM, and 64QAM), coding rates, and choose between them using the effective-SNR bitrate selection algorithm [13].”
FILA: Fine-grained indoor localization (2012, methodology): “We modify the driver as in [9] to collect the CSI values from the NICs.”
Widar2.0: Passive Human Tracking with a Single Wi-Fi Link (2018, methodology): “Linux 802.11n CSI Tool [10] is installed in devices to collect CSI measurements.”
PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices (2017, methodology): “Unlike RSS, the channel state information (CSI) represents fine-grained channel information, which is now available for several off-the-shelf WiFi network interface cards (NIC), e.g., Intel WiFi Link 5300 NIC [11] and the Atheros AR9580 chipset [12].”
A Survey on Wireless Indoor Localization from the Device Perspective (2016, methodology): “The superiority of CSI was first proved in Halperin et al. [2010], and Halperin et al. apply CSI for throughput improvement by adaptively adjusting the transmission rate. Inspired by this work, FILA [Wu et al. 2012a] initiates the use of CSI for locating the target in complicated indoor environments”
Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi (2017, methodology): “To obtain CSI measurements from Wi-Fi data frames, all mini-desktops are installed with the Linux 802.11n CSI Tool [4] and set up to inject in monitor mode on Channel 161 at 5.825 GHz.”
CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach (2016, methodology): “By modifying the device driver, we can now obtain channel state information (CSI) from some advanced WiFi network interface cards (NICs), such as the Intel WiFi link (IWL) 5300 NIC [8], [9].”
JMB: scaling wireless capacity with user demands (2012, methodology): “We evaluate the SNR reduction in JMB in three effective SNR [13] ranges: low (6-12 dB), medium (12-18 dB) and high (> 18 dB).”