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 practical massive multiple-input multiple-output (MIMO) systems, theprecoding matrix is often obtained from the eigenvectors of channel matricesand is challenging to update in time due to finite computation resources at thebase station, especially in mobile scenarios. In order to reduce the precodingcomplexity while enhancing the spectral efficiency (SE), a novel precodingmatrix prediction method based on the eigenvector prediction (EGVP) isproposed. The basic idea is to decompose the periodic uplink channeleigenvector samples into a linear combination of the channel state information(CSI) and channel weights. We further prove that the channel weights can beinterpolated by an exponential model corresponding to the Dopplercharacteristics of the CSI. A fast matrix pencil prediction (FMPP) method isalso devised to predict the CSI. We also prove that our scheme achievesasymptotically error-free precoder prediction with a distinct complexityadvantage. Simulation results show that under the perfect non-delayed CSI, theproposed EGVP method reduces floating point operations by 80\% without losingSE performance compared to the traditional full-time precoding scheme. In morerealistic cases with CSI delays, the proposed EGVP-FMPP scheme has clear SEperformance gains compared to the precoding scheme widely used in currentcommunication systems.

Low-complexity eigenvector prediction-based precoding matrix prediction in massive MIMO with mobility