Spatio-spectral concentration theory is concerned with the optimal space localization of a signal bandlimited in the frequency domain. The question was first considered in a series of classic papers by Slepian, Pollak, and Landau [1961; 1961; 1962; 1978] who studied the problem on the real line; an introduction to the early work on the subject can be found in two articles by Slepian [1976; 1983...

In the distributed function computation problem, dichotomy theorems, initiated by Han-Kobayashi, seek to classify functions by whether the rate regions for function computation improve on the Slepian-Wolf regions or not. In this paper, we develop a general approach to derive converse bounds on the distributed function computation problem. By using this approach, we recover the sufficiency part,...

We analyze the asymptotic performance of ensembles of random binning Slepian-Wolf codes, where each type class of the source might have a different coding rate. In particular, we first provide the exact encoder excess rate exponent as well as the decoder error exponent. Then, using the error exponent expression, we determine the optimal rate function, namely, the minimal rate for each type clas...

This paper proposes two low-complexity two-dimensional channel estimators forMIMOOFDM systems derived from a joint time–frequency channel estimator. The estimators exploit both time and frequency correlations of the wireless channel via use of Slepianbasis expansions. The computational saving comes from replacing a two-dimensional Slepian-basis expansion with two serially concatenated one-dimen...

We consider a rather simple algorithm to address the fascinating field of numerical extrapolation of (analytic) band-limited functions. It relies on two main elements: namely, the lower frequencies are treated by projecting the known part of the signal to be extended onto the space generated by “Prolate Spheroidal Wave Functions” (PSWF, as originally proposed by Slepian), whereas the higher one...

In this paper, we provide a Rapid Orthogonal Approximate Slepian Transform (ROAST) for the discrete vector one obtains when collecting a finite set of uniform samples from a baseband analog signal. The ROAST offers an orthogonal projection which is an approximation to the orthogonal projection onto the leading discrete prolate spheroidal sequence (DPSS) vectors (also known as Slepian basis vect...