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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper, we develop a deep learning (DL)-guided hybrid beam and powerallocation approach for multiuser millimeter-wave (mmWave) networks, whichfacilitates swift beamforming at the base station (BS). The followingpersisting challenges motivated our research: (i) User and vehicular mobility,as well as redundant beam-reselections in mmWave networks, degrade theefficiency; (ii) Due to the large beamforming dimension at the BS, thebeamforming weights predicted by the cutting-edge DL-based methods often do notsuit the channel distributions; (iii) Co-located user devices may cause asevere beam conflict, thus deteriorating system performance. To address theaforementioned challenges, we exploit the synergy of supervised learning andsuper-resolution technology to enable low-overhead beam- and power allocation.In the first step, we propose a method for beam-quality prediction. It is basedon deep learning and explores the relationship between high- and low-resolutionbeam images (energy). Afterward, we develop a DL-based allocation approach,which enables high-accuracy beam and power allocation with only a portion ofthe available time-sequential low-resolution images. Theoretical and numericalresults verify the effectiveness of our proposed

Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks