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

Recently, inspired by successful applications in many fields, deep learning(DL) technologies for CSI acquisition have received considerable researchinterest from both academia and industry. Considering the practical feedbackmechanism of 5th generation (5G) New radio (NR) networks, we propose twoimplementation schemes for artificial intelligence for CSI (AI4CSI), theDL-based receiver and end-to-end design, respectively. The proposed AI4CSIschemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE),feedback overhead, and computational complexity, and compared with legacyschemes. To demonstrate whether these schemes can be used in real-lifescenarios, both the modeled-based channel data and practically measuredchannels were used in our investigations. When DL-based CSI acquisition isapplied to the receiver only, which has little air interface impact, itprovides approximately 25\% SE gain at a moderate feedback overhead level. Itis feasible to deploy it in current 5G networks during 5G evolutions. For theend-to-end DL-based CSI enhancements, the evaluations also demonstrated theiradditional performance gain on SE, which is 6% -- 26% compared with DL-basedreceivers and 33% -- 58% compared with legacy CSI schemes. Considering itslarge impact on air-interface design, it will be a candidate technology for 6thgeneration (6G) networks, in which an air interface designed by artificialintelligence can be used.

Deep Learning-based Massive MIMO CSI Acquisition for 5G Evolution and 6G