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.

Capacity of MIMO systems with antenna selection

Abstract: We consider the capacity of multiple-input multiple-output systems with reduced complexity. One link-end uses all available antennas, while the other chooses the L out of N antennas that maximize capacity. We derive an upper bound on the capacity that can be expressed as the sum of the logarithms of ordered chi-square-distributed variables. This bound is then evaluated analytically and compared to the results obtained by Monte Carlo simulations. Our results show that the achieved capacity is close to the capacity of a full-complexity system provided that L is at least as large as the number of antennas at the other link-end. For example, for L = 3, N = 8 antennas at the receiver and three antennas at the transmitter, the capacity of the reduced-complexity scheme is 20 bits/s/Hz compared to 23 bits/s/Hz of a full-complexity scheme. We also present a suboptimum antenna subset selection algorithm that has a complexity of N/sup 2/ compared to the optimum algorithm with a complexity of (N/sub L/).

A sparse sampling algorithm for near-optimal planning in large Markov decision processes

Abstract: An issue that is critical for the application of Markov decision processes (MDPs) to realistic problems is how the complexity of planning scales with the size of the MDP. In stochastic environments with very large or even infinite state spaces, traditional planning and reinforcement learning algorithms are often inapplicable, since their running time typically scales linearly with the state space size. In this paper we present a new algorithm that, given only a generative model (simulator) for an arbitrary MDP, performs near-optimal planning with a running time that has no dependence on the number of states. Although the running time is exponential in the horizon time (which depends only on the discount factor 7 and the desired degree of approximation to the optimal policy), our results establish for the first time that there are no theoretical barriers to computing near-optimal policies in arbitrarily large, unstructured MDPs. Our algorithm is based on the idea of sparse sampling. We prove that a randomly sampled look-ahead tree that covers only a vanishing fraction of the full look-ahead tree nevertheless suffices to compute near-optimal actions from any state of an MDP. Practical implementations of the algorithm are discussed, and we draw ties to our related recent results on finding a near-best strategy from a given class of strategies in very large partially observable MDPs [KMN99].

Influence sets based on reverse nearest neighbor queries

Abstract: Inherent in the operation of many decision support and continuous referral systems is the notion of the “influence” of a data point on the database. This notion arises in examples such as finding the set of customers affected by the opening of a new store outlet location, notifying the subset of subscribers to a digital library who will find a newly added document most relevant, etc. Standard approaches to determining the influence set of a data point involve range searching and nearest neighbor queries.In this paper, we formalize a novel notion of influence based on reverse neighbor queries and its variants. Since the nearest neighbor relation is not symmetric, the set of points that are closest to a query point (i.e., the nearest neighbors) differs from the set of points that have the query point as their nearest neighbor (called the reverse nearest neighbors). Influence sets based on reverse nearest neighbor (RNN) queries seem to capture the intuitive notion of influence from our motivating examples.We present a general approach for solving RNN queries and an efficient R-tree based method for large data sets, based on this approach. Although the RNN query appears to be natural, it has not been studied previously. RNN queries are of independent interest, and as such should be part of the suite of available queries for processing spatial and multimedia data. In our experiments with real geographical data, the proposed method appears to scale logarithmically, whereas straightforward sequential scan scales linearly. Our experimental study also shows that approaches based on range searching or nearest neighbors are ineffective at finding influence sets of our interest.

Adding Structure to Unstructured Data

Abstract: We develop a new schema for unstructured data. Traditional schemas resemble the type systems of programming languages. For unstructured data, however, the underlying type may be much less constrained and hence an alternative way of expressing constraints on the data is needed. Here, we propose that both data and schema be represented as edge-labeled graphs. We develop notions of conformance between a graph database and a graph schema and show that there is a natural and efficiently computable ordering on graph schemas. We then examine certain subclasses of schemas and show that schemas are closed under query applications. Finally, we discuss how they may be used in query decomposition and optimization.

Entanglement Breaking Channels

Abstract: This paper studies the class of stochastic maps, or channels, for which (I⊗Φ)(Γ) is always separable (even for entangled Γ). Such maps are called entanglement breaking, and can always be written in the form Φ(ρ)=∑kRkTrFkρ where each Rk is a density matrix and Fk>0. If, in addition, Φ is trace-preserving, the {Fk} must form a positive operator valued measure (POVM). Some special classes of these maps are considered and other characterizations given. Since the set of entanglement-breaking trace-preserving maps is convex, it can be characterized by its extreme points. The only extreme points of the set of completely positive trace preserving maps which are also entanglement breaking are those known as classical-quantum or CQ. However, for d≥3, the set of entanglement breaking maps has additional extreme points which are not extreme CQ maps.