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.

Multiple transmit antenna differential detection from generalized orthogonal designs

Abstract: We explicitly construct multiple transmit antenna differential encoding/decoding schemes based on generalized orthogonal designs. These constructions generalize the two transmit antenna differential detection scheme that we proposed before (Tarokh and Jafarkhani 2000).

Characterizing radio resource allocation for 3G networks

Abstract: 3G cellular data networks have recently witnessed explosive growth. In this work, we focus on UMTS, one of the most popular 3G mobile communication technologies. Our work is the first to accurately infer, for any UMTS network, the state machine (both transitions and timer values) that guides the radio resource allocation policy through a light-weight probing scheme. We systematically characterize the impact of operational state machine settings by analyzing traces collected from a commercial UMTS network, and pinpoint the inefficiencies caused by the interplay between smartphone applications and the state machine behavior. Besides basic characterizations, we explore the optimal state machine settings in terms of several critical timer values evaluated using real network traces. Our findings suggest that the fundamental limitation of the current state machine design is its static nature of treating all traffic according to the same inactivity timers, making it difficult to balance tradeoffs among radio resource usage efficiency, network management overhead, device radio energy consumption, and performance. To the best of our knowledge, our work is the first empirical study that employs real cellular traces to investigate the optimality of UMTS state machine configurations. Our analysis also demonstrates that traffic patterns impose significant impact on radio resource and energy consumption. In particular, We propose a simple improvement that reduces YouTube streaming energy by 80% by leveraging an existing feature called fast dormancy supported by the 3GPP specifications.

Near-optimal sparse fourier representations via sampling

Abstract: (MATH) We give an algorithm for finding a Fourier representation R of B terms for a given discrete signal signal A of length N, such that $\|\signal-\repn\|_2^2$ is within the factor (1 +e) of best possible $\|\signal-\repn_\opt\|_2^2$. Our algorithm can access A by reading its values on a sample set T ⊆[0,N), chosen randomly from a (non-product) distribution of our choice, independent of A. That is, we sample non-adaptively. The total time cost of the algorithm is polynomial in B log(N)log(M)e (where M is the ratio of largest to smallest numerical quantity encountered), which implies a similar bound for the number of samples.

Adaptive antenna arrays for OFDM systems with cochannel interference

Abstract: Orthogonal frequency-division multiplexing (OFDM) is one of the promising techniques for future mobile wireless data systems. For OFDM systems with cochannel interference, adaptive antenna arrays can be used for interference suppression. This paper focuses on a key issue for adaptive antenna arrays, that is, parameter estimation for the minimum mean square error (MMSE) diversity combiner (DC). Using the instantaneous correlation estimation approach developed in the paper, an original parameter estimator for the MMSE-DC is derived. Based on the original estimator, we propose an enhanced parameter estimator. Extensive computer simulation demonstrates that the MMSE-DC using the proposed parameter estimators can effectively suppress both synchronous and asynchronous interference in OFDM systems for packet and continuous data transmission.

Fast, small-space algorithms for approximate histogram maintenance

Abstract: (MATH) A vector A of length N is defined implicitly, via a stream of updates of the form "add 5 to A3." We give a sketching algorithm, that constructs a small sketch from the stream of updates, and a reconstruction algorithm, that produces a B-bucket piecewise-constant representation (histogram) H for A from the sketch, such that ||A—H||≤(1+e)||A—Hopt||, where the error ||A—H|| is either $\ell_1$ (absolute) or $\ell_2$ (root-mean-square) error. The time to process a single update, time to reconstruct the histogram, and size of the sketch are each bounded by poly(B,log(N),log||A,1/e. Our result is obtained in two steps. First we obtain what we call a robust histogram approximation for A, a histogram such that adding a small number of buckets does not help improve the representation quality significantly. From the robust histogram, we cull a histogram of desired accruacy and B buckets in the second step. This technique also provides similar results for Haar wavelet representations, under $\ell_2$ error. Our results have applications in summarizing data distributions fast and succinctly even in distributed settings.