AT&T Labs

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.

Pilot-symbol-aided channel estimation for OFDM in wireless systems

Abstract: We investigate pilot-symbol-aided parameter estimation for OFDM systems. We first derive a minimum mean-square error 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 SNRs for a 10% word error rate (WER) are 6.8 dB and 7.3 dB for the typical urban channels with 40 Hz and 200 Hz Doppler frequencies, respectively, and are 8 dB and 8.3 dB for the hilly-terrain 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.

Supermodes for optical transmission.

Abstract: In this paper, the concept of supermode is introduced for long-distance optical transmission systems. The supermodes exploit coupling between the cores of a multi-core fiber, in which the core-to-core distance is much shorter than that in conventional multi-core fiber. The use of supermodes leads to a larger mode effective area and higher mode density than the conventional multi-core fiber. Through simulations, we show that the proposed coupled multi-core fiber allows lower modal dependent loss, mode coupling and differential modal group delay than few-mode fibers. These properties suggest that the coupled multi-core fiber could be a good candidate for both spatial division multiplexing and single-mode operation.

Measuring personalization of web search

Abstract: Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where different users receive different results for the same search query. The increasing personalization is leading to concerns about Filter Bubble effects, where certain users are simply unable to access information that the search engines' algorithm decides is irrelevant. Despite these concerns, there has been little quantification of the extent of personalization in Web search today, or the user attributes that cause it. In light of this situation, we make three contributions. First, we develop a methodology for measuring personalization in Web search results. While conceptually simple, there are numerous details that our methodology must handle in order to accurately attribute differences in search results to personalization. Second, we apply our methodology to 200 users on Google Web Search; we find that, on average, 11.7% of results show differences due to personalization, but that this varies widely by search query and by result ranking. Third, we investigate the causes of personalization on Google Web Search. Surprisingly, we only find measurable personalization as a result of searching with a logged in account and the IP address of the searching user. Our results are a first step towards understanding the extent and effects of personalization on Web search engines today.

PAC Generalization Bounds for Co-training

Abstract: The rule-based bootstrapping introduced by Yarowsky, and its co-training variant by Blum and Mitchell, have met with considerable empirical success. Earlier work on the theory of co-training has been only loosely related to empirically useful co-training algorithms. Here we give a new PAC-style bound on generalization error which justifies both the use of confidences — partial rules and partial labeling of the unlabeled data — and the use of an agreement-based objective function as suggested by Collins and Singer. Our bounds apply to the multiclass case, i.e., where instances are to be assigned one of labels for k ≥ 2.

Tradeoffs for packet classification

Abstract: We present an algorithmic framework for solving the packet classification problem that allows various access time versus memory tradeoffs. It reduces the multidimensional packet classification problem to solving a few instances of the one-dimensional IP lookup problem. It gives the best known lookup performance with moderately large memory space. Furthermore, it efficiently supports a reasonable number of additions and deletions to the rulesets without degrading the lookup performance. We perform a thorough experimental study of the tradeoffs for the two-dimensional packet classification problem on rulesets derived from datasets collected from AT&T WorldNet, an Internet service provider.