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

The downlink channel state information (CSI) estimation and low overheadacquisition are the major challenges for massive MIMO systems in frequencydivision duplex to enable high MIMO gain. Recently, numerous studies have beenconducted to harness the power of deep neural networks for better channelestimation and feedback. However, existing methods have yet to fully exploitthe intrinsic correlation features present in CSI. As a consequence, distinctnetwork structures are utilized for handling these two tasks separately. Toachieve joint channel estimation and feedback, this paper proposes anencoder-decoder based network that unveils the intrinsic frequency-domaincorrelation within the CSI matrix. The entire encoder-decoder network isutilized for channel compression. To effectively capture and restructurecorrelation features, a self-mask-attention coding is proposed, complemented byan active masking strategy designed to improve efficiency. The channelestimation is achieved through the decoder part, wherein a lightweightmultilayer perceptron denoising module is utilized for further accurateestimation. Extensive experiments demonstrate that our method not onlyoutperforms state-of-the-art channel estimation and feedback techniques injoint tasks but also achieves beneficial performance in individual tasks.

Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems