English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

In this self-contained chapter, we revisit a fundamental problem ofmultivariate statistics: estimating covariance matrices from finitely manyindependent samples. Based on massive Multiple-Input Multiple-Output (MIMO)systems we illustrate the necessity of leveraging structure and consideringquantization of samples when estimating covariance matrices in practice. Wethen provide a selective survey of theoretical advances of the last decadefocusing on the estimation of structured covariance matrices. This review isspiced up by some yet unpublished insights on how to benefit from combinedstructural constraints. Finally, we summarize the findings of our recentlypublished preprint "Covariance estimation under one-bit quantization" to showhow guaranteed covariance estimation is possible even under coarse quantizationof the samples.

New challenges in covariance estimation: multiple structures and coarse quantization