Time-variant channel estimation using discrete prolate spheroidal sequences

By Thomas Zemen, Christoph F. Mecklenbräuker. IEEE Transactions on Signal Processing, 2005

522 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey on High Mobility Wireless Communications: Challenges, Opportunities and Solutions (2016, background): “Other basis functions include polynomial basis [21], [22], Karhunen-Lòeve basis [23], and discrete prolate spheroidal sequences [24], [25].”
Improved bounds for the eigenvalues of prolate spheroidal wave functions and discrete prolate spheroidal sequences (2020, methodology): “time-variant channel estimation [10,11], wideband compressive radio receivers [12], compressed sensing of analog signals [6], target detection [13, 14], and a fast method [15] for computing Fourier extension series coefficients [16, 17].”
Basis expansion model for underwater acoustic channels? (2008, unknown): “This agrees well with the equallyspaced pilot pattern in the literature [7], [11], [12], [21].”
A Two-Stage Approach for the Estimation of Doubly Spread Acoustic Channels (2015, methodology): “There are several available models for the time-varying channels in wireless radio communications, such as the basis expansion model (BEM) [13]–[15], the radial basis function (RBF) model [16], [17], and the polynomial amplitude-variation channel model [18], [19].”
Pilot-Assisted Time-Varying Channel Estimation for OFDM Systems (2007, methodology): “For instance, the discrete prolate spheroidal BEM (DPS-BEM) corresponds to the DKL-BEM with a rectangular spectrum [6].”