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

Motivated by the issue of inaccurate channel state information (CSI) at thebase station (BS), which is commonly due to feedback/processing delays andcompression problems, in this paper, we introduce a scalable idea of adoptingartificial intelligence (AI) aided CSI acquisition. The proposed schemeenhances the CSI compression, which is done at the mobile terminal (MT), alongwith accurate recovery of estimated CSI at the BS. Simulation-based resultscorroborate the validity of the proposed scheme. Numerically, nearly 100\%recovery of the estimated CSI is observed with relatively lower overhead thanthe benchmark scheme. The proposed idea can bring potential benefits in thewireless communication environment, e.g., ultra-reliable and low latencycommunication (URLLC), where imperfect CSI and overhead is intolerable.

Dealing with CSI Compression to Reduce Losses and Overhead: An Artificial Intelligence Approach