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

This article investigates beam alignment for multi-user millimeter wave(mmWave) massive multi-input multi-output system. Unlike the existing worksusing machine learning (ML), an alignment method with partial beams using ML(AMPBML) is proposed without any prior knowledge such as user locationinformation. The neural network (NN) for the AMPBML is trained offline usingsimulated environments according to the mmWave channel model and is thendeployed online to predict the beam distribution vector using partial beams.Afterwards, the beams for all users are all aligned simultaneously based on theindices of the dominant entries of the obtained beam distribution vector.Simulation results demonstrate that the AMPBML outperforms the existingmethods, including the adaptive compressed sensing, hierarchical search, andmulti-path decomposition and recovery, in terms of the total training timeslots and the spectral efficiency.

Machine Learning for Beam Alignment in Millimeter Wave Massive MIMO