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messages with an arbitrary set of other source nodes via the relay. By allowing an arbitrary information exchange pattern, the consid-ered channel model is a unified one. It includes several existing channel models as special cases:K-user MIMO Y channel, multi-pair MIMO two-way relay channel, generalized

Caractérisation des problèmes conjoints de détection et d'estimation

Responses of soil biological processes to elevated atmospheric [CO2] and nitrogen addition in a poplar plantation

Implementational aspects of code-based cryptography

de nationalité suisse et originaire de Zurich (ZH) et Lucerne (LU) acceptée sur proposition du jury:

Abstract not found

Abstract not found

Abstract — We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian noise. Our analysis is based on the theory of reproducing kernel Hilbert spaces (RKHS). After a characterization of the RKHS associated with the SLGM, we derive a lower bound on the minimum variance achievable by estimators with a prescribed bias function, including the important special case of unbiased estimation. This bound is obtained via an orthogonal projection of the prescribed mean function onto a subspace of the RKHS associated with the SLGM. It provides an approximation to the minimum achievable variance (Barankin bound) that is tighter than any known bound. Our bound holds for an arbitrary system matrix, including the overdetermined and underdetermined cases. We specialize

We explore two fundamental questions at the intersection of sampling theory and information theory: how is channel capacity affected by sampling below the channel’s Nyquist rate, and what sub-Nyquist sampling strategy should be employed to maximize capacity. In particular, we first derive