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Institution

AT&T Labs

Company
About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.


Papers
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Proceedings ArticleDOI
07 Jun 1998
TL;DR: A minimum mean-square-error (MSE) channel estimator is derived, which makes full use of the time- and frequency-domain correlations of the frequency response of time-varying dispersive fading channels and can significantly improve the performance of OFDM systems in a rapid dispersion fading channel.
Abstract: Orthogonal frequency division multiplexing (OFDM) modulation is a promising technique for achieving the high-bit-rates required for a wireless multimedia service. Without channel estimation and tracking, OFDM systems have to use differential phase-shift keying (DPSK), which has a 3 dB signal-to-noise ratio (SNR) loss compared with coherent phase-shift keying (PSK). To improve the performance of OFDM systems by using coherent PSK, we investigate robust channel estimation for OFDM systems. We derive a minimum mean-square-error (MSE) channel estimator, which makes full use of the time- and frequency-domain correlations of the frequency response of time-varying dispersive fading channels. Since the channel statistics are usually unknown, we also analyze the mismatch of the estimator to channel statistics and propose a robust channel estimator that is insensitive to the channel statistics. The robust channel estimator can significantly improve the performance of OFDM systems in a rapid dispersive fading channel.

675 citations

Journal ArticleDOI
William DuMouchel1
TL;DR: Here, a baseline or null hypothesis expected frequency is constructed for each cell, and screening criteria for ranking the cell deviations of observed from expected count are suggested and compared.
Abstract: A common data mining task is the search for associations in large databases Here we consider the search for “interestingly large” counts in a large frequency table, having millions of cells, most of which have an observed frequency of 0 or 1 We first construct a baseline or null hypothesis expected frequency for each cell, and then suggest and compare screening criteria for ranking the cell deviations of observed from expected count A criterion based on the results of fitting an empirical Bayes model to the cell counts is recommended An example compares these criteria for searching the FDA Spontaneous Reporting System database maintained by the Division of Pharmacovigilance and Epidemiology In the example, each cell count is the number of reports combining one of 1,398 drugs with one of 952 adverse events (total of cell counts = 49 million), and the problem is to screen the drug-event combinations for possible further investigation

672 citations

Book ChapterDOI
10 Jan 1999
TL;DR: The semistructured data model consists of an edge-labeled graph, in which nodes correspond to objects and edges to attributes or values as mentioned in this paper, and it is used to describe the data that does not conform to traditional data models.
Abstract: In recent years there has been an increased interest in managing data that does not conform to traditional data models, like the relational or object oriented model. The reasons for this non-conformance are diverse. On the one hand, data may not conform to such models at the physical level: it may be stored in data exchange formats, fetched from the Web, or stored as structured files. One the other hand, it may not conform at the logical level: data may have missing attributes, some attributes may be of different types in different data items, there may be heterogeneous collections, or the schema may be too complex or changes too often. The term semistructured data has been used to refer to such data. The semistructured data model consists of an edge-labeled graph, in which nodes correspond to objects and edges to attributes or values. Figure 1 illustrates a semistructured database providing information about a city.

671 citations

Journal ArticleDOI
Ye Li1
TL;DR: The pilot-symbol-aided parameter estimation for orthogonal frequency division multiplexing (OFDM) systems is highly robust to Doppler frequency for dispersive fading channels with noise impairment even though it has some performance degradation for systems with lower Dopple frequencies.
Abstract: In this paper, we investigate pilot-symbol-aided parameter estimation for orthogonal frequency division multiplexing (OFDM) systems. We first derive a minimum mean-square error (MMSE) pilot-symbol-aided parameter estimator. Then, we discuss a robust implementation of the pilot-symbol-aided estimator that is insensitive to channel statistics. From the simulation results, the required signal-to-noise ratios (SNRs) for a 10% word error rate (WER) are 6.8 dB and 7.3 dB for the typical urban (TU) channels with 40 Hz and 200 Hz Doppler frequencies, respectively, and they are 8 dB and 8.3 dB for the hilly-terrain (HT) channels with 40 Hz and 200 Hz Doppler frequencies, respectively. Compared with the decision-directed parameter estimator, the pilot-symbol-aided estimator is highly robust to Doppler frequency for dispersive fading channels with noise impairment even though it has some performance degradation for systems with lower Doppler frequencies.

671 citations

Journal ArticleDOI
TL;DR: This paper focuses on the task of automatically routing telephone calls based on a user's fluently spoken response to the open-ended prompt of “ How may I help you? ”.

664 citations


Authors

Showing all 1881 results

NameH-indexPapersCitations
Yoshua Bengio2021033420313
Scott Shenker150454118017
Paul Shala Henry13731835971
Peter Stone130122979713
Yann LeCun121369171211
Louis E. Brus11334763052
Jennifer Rexford10239445277
Andreas F. Molisch9677747530
Vern Paxson9326748382
Lorrie Faith Cranor9232628728
Ward Whitt8942429938
Lawrence R. Rabiner8837870445
Thomas E. Graedel8634827860
William W. Cohen8538431495
Michael K. Reiter8438030267
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20225
202133
202069
201971
2018100
201791