IntroductionMotivationPacket Delivery ModelTestbedsNode ConfigurationMeasurement ToolsPacket Delivery EvaluationMeasurement setupRSSIs and Multiple AntennasResultsApplication to Rate SelectionRate Selection AlgorithmsTrace-driven SimulatorRate Adaptation ResultsRelated WorkConclusionReferencesPredictable 802.11 Packet Delivery fromWireless Channel MeasurementsDaniel Halperin∗, Wenjun Hu∗, Anmol Sheth†, and David Wetherall∗†University of Washington∗and Intel Labs Seattle†ABSTRACTRSSI is known to be a fickle indicator of whether a wireless linkwill work, for many reasons. This greatly complicates operationbecause it requires testing and adaptation to find the best rate, trans-mit power or other parameter that is tuned to boost performance.We show that, for the first time, wireless packet delivery can beaccurately predicted for commodity 802.11 NICs from only thechannel measurements that they provide. Our model uses 802.11nChannel State Information measurements as input to an OFDM re-ceiver model we develop by using the concept of effective SNR. Itis simple, easy to deploy, broadly useful, and accurate. It makespacket delivery predictions for 802.11a/g SISO rates and 802.11nMIMO rates, plus choices of transmit power and antennas. We re-port testbed experiments that show narrow transition regions (<2 dBfor most links) similar to the near-ideal case of narrowband, fre-quency-flat channels. Unlike RSSI, this lets us predict the highestrate that will work for a link, trim transmit power, and more. We usetrace-driven simulation to show that our rate prediction is as goodas the best rate adaptation algorithms for 802.11a/g, even over dy-namic channels, and extends this good performance to 802.11n.Categories and Subject DescriptorsC.2.1 [Computer-Communication Networks]: Network Archi-tecture and Design—Wireless CommunicationGeneral TermsDesign, Experimentation1. INTRODUCTIONWireless LANs based on 802.11 are used almost everywhere,from airports to zoos and in urban, suburban and rural areas. Mod-ern wireless NICs provide a large and growing range of physicallayer configurations to obtain good performance across this rangeof environments. With 802.11n, the latest version of the standardthat ships on most laptops, combinations of modulation, coding andspatial streams offer rates from 6 Mbps to 600 Mbps [1]. Other im-portant choices include transmit power, channel, and antennas.For good performance, reliability and coverage, the physical layersettings should match the RF channel over which the wireless sig-nals are sent. This is evident in rate adaptation schemes [5, 10, 14,28] that determine the highest rate for transmission, since a goodscheme has a large effect on throughput. Other work adapts trans-mit power to reduce co-channel interference [17, 21, 25].Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.SIGCOMM’10, August 30–September 3, 2010, New Delhi, India.Copyright 2010 ACM 978-1-4503-0201-2/10/08 ...$10.00.In theory, it is simple to select the physical layer configurationbecause this is directly determined by the specifics of the RF chan-nel. The signal-to-noise ratio (SNR) is the gold standard for per-formance in narrowband channels. Textbook formulas relate theerror rate of different modulations to the SNR [27]. The best rate orrequired transmit power is then simple to compute.In practice, 802.11 LANs have never used channel measurementsas more than a coarse indicator of expected performance. Therehave simply been too many ways in which the observed measure-ments and actual performance fail to match the predictions of the-ory. For example, the most accessible channel measurement is re-ceived signal strength indication (RSSI), which serves as a proxyfor the true SNR. RSSI measurements are samples that may varyover packet reception, be mis-calibrated, or be corrupted by in-terference, all of which are known to be issues in practice [6, 10,22]. Even if RSSI were perfect, it does not reflect the frequency-selective fading of 802.11 channels, which are not close to narrow-band. Nor does it account for imperfect receivers that may greatlydegrade performance [3, 10]. Due to these factors, the minimumRSSI at which a rate starts to work varies by more than 10 dB forreal links [22, 30, 31].To reconcile these viewpoints, a form of guided search is widelyused in practice to select operating points [21, 24, 29]. Packet de-livery is simply tested for a rate or transmit power to see how wellit works. If the loss rate is too high, a lower rate (or more power) isused, otherwise a higher rate (or less power) is tested. SampleRateis a well-known algorithm of this kind for finding transmit rates [5].This approach is very effective for slowly varying channels and sim-ple configurations (e.g., a few rates with fixed transmit power andchannel) since the best setting will soon be found.However, search becomes less effective as channels change morequickly and the configuration space becomes more complex. Bothof these factors are trends: 802.11 clients are increasingly usedwhen they are truly mobile, both walking and in vehicles; and NICsthat are now being deployed with 802.11n depend on multiple an-tennas, which adds another dimension to and increases the size ofthe search space. Also, tuning combinations such as rate and poweris much more complex.For rate selection, recent work has made headway by measur-ing symbol-level details of packet reception. In particular, SoftRateuses the output of soft-Viterbi decoding for each symbol to estimatethe bit error rate (BER) [28]. This allows it to predict the effects onpacket delivery of changing the rate. AccuRate uses symbol er-ror vectors for the same purpose [23]. However, these methodsare not defined for selecting other useful parameters, such as trans-mit power, and they do not extend from 802.11a/g to 802.11n, e.g.,when selecting antennas or numbers of spatial streams.In this work, we return to the basic problem of using theory toconnect the performance of 802.11 NICs on real links to measuredchannels in practice. The opportunity to make progress has arisenfor two reasons. First, 802.11n