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

Massive MIMO system yields significant improvements in spectral and energyefficiency for future wireless communication systems. The regularizedzero-forcing (RZF) beamforming is able to provide good performance with thecapability of achieving numerical stability and robustness to the channeluncertainty. However, in massive MIMO systems, the matrix inversion operationin RZF beamforming becomes computationally expensive. To address thiscomputational issue, we shall propose a novel randomized sketching based RZFbeamforming approach with low computational complexity. This is achieved bysolving a linear system via randomized sketching based on the preconditionedRichard iteration, which guarantees high quality approximations to the optimalsolution. We theoretically prove that the sequence of approximations obtainediteratively converges to the exact RZF beamforming matrix linearly fast as thenumber of iterations increases. Also, it turns out that the system sum-rate forsuch sequence of approximations converges to the exact one at a linearconvergence rate. Our simulation results verify our theoretical findings.

Large-Scale Beamforming for Massive MIMO via Randomized Sketching