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/

Global: Zendy Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper proposes a tensor-based parametric modeling and estimationframework in multiple-input multiple-output (MIMO) systems assisted byintelligent reflecting surfaces (IRSs). We present two algorithms that exploitthe tensor structure of the received pilot signal to estimate the concatenatedchannel. The first one is an iterative solution based on the alternating leastsquares algorithm. In contrast, the second method provides closed-formestimates of the involved parameters using the high order single valuedecomposition. Our numerical results show that our proposed tensor-basedmethods provide improved performance compared to competing state-of-the-artchannel estimation schemes, thanks to the exploitation of the algebraic tensorstructure of the combined channel without additional computational complexity.

Tensor-based modeling/estimation of static channels in IRS-assisted MIMO systems