11 accepted papers
This paper presents a new method on transmitting data over linear time-variant (LTV) channels when no channel state estimation (CSI) is accessible. The considered receiver is equipped with multiple antennas and observes a superposition of delay-Doppler shifted versions of the transmitted signal. The…
A Turing machine is a model describing the fundamental limits of any realizable computer, digital signal processor (DSP), or field programmable gate array (FPGA). This paper shows that there exist very simple linear time-invariant (LTI) systems which can not be simulated on a Turing machine. In part…
Although the Hilbert transform and the spectral factorization are of central importance in signal processing, both operations can generally not be calculated in closed form. Therefore, algorithmic solutions are prevalent which provide an approximation of the true solution. Then it is important to ef…
It is common to model rapidly varying communication channels by time-varying linear systems. The output of a time-varying linear system can be described by a superposition of time-frequency (delay-Doppler) shifts of the input signal. This paper investigates a novel message transmission scheme over t…
This paper considers the problem of approximating continuous functions of finite Dirichlet energy from samples of these functions. It will be shown that there exists no sampling-based method which is able to approximate every function in this space from its samples. Specifically, we are going to sho…
We investigate the impact of having additional information in a form of linear constraints in the channel identification problem. With those constraints taken into account, the problem turns into solving a linear system that is associated with a block matrix where each submatrix is either a Gabor sy…
This paper considers the problem of determining the sparse covariance matrix X of an unknown data vector x by observing the covariance matrix Y of a compressive measurement vector y = Ax. We construct deterministic sensing matrices A for which the recovery of a k-sparse covariance matrix X from m va…
Approximating the transfer function of stable causal linear systems by a basis expansion is a common task in signal- and system theory. This paper characterizes a scale of signal spaces, containing stable causal transfer functions, with a very simple basis (the Fourier basis) but which is not comput…
We study support patterns for covariance matrices that appear in the problem of stochastic time-varying channel identification. The problem reduces to solving a linear system that is associated with a matrix in the form of a Kronecker product of a Gabor system matrix with itself, and therefore solva…
Many important non-adaptive approximation methods are know to diverge for almost all functions from certain Banach space X. One can show that a corresponding adaptive method will improve this behavior in the sense that it converges to the desired result for almost all functions in X. However, even t…
This paper considers the problem of recovering a k-sparse, N-dimensional complex signal from Fourier magnitude measurements. It proposes a Fourier optics setup such that signal recovery up to a global phase factor is possible with very high probability whenever M ≳ 4k log <sub xmlns:mml="http://www.…