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

Increasing data rate in wireless networks can be accomplished through atwo-pronged approach, which are 1) increasing the network flow rate throughparallel independent routes and 2) increasing the user's link rate throughbeamforming codebook adaptation. Mobile relays are utilized to enable achievingthese goals given their flexible positioning. First at the network level, wemodel regularized Laplacian matrices, which are symmetric positive definite(SPD) ones representing relay-dependent network graphs, as points overRiemannian manifolds. Inspired by the geometric classification of differenttasks in the brain network, Riemannian metrics, such as Log-Euclidean metric(LEM), are utilized to choose relay positions that result in maximum LEM.Simulation results show that the proposed LEM-based relay positioning algorithmenables parallel routes and achieves maximum network flow rate, as opposed toother metrics (e.g., algebraic connectivity).  Second at the link level, we design unique relay-dependent beamformingcodebooks aimed to increase data rate over the spatially-correlated fadingchannels between a given relay and its neighboring users. To do so, we proposea geometric machine learning approach, which utilizes support vector machine(SVM) model to learn an SPD variant of the user's channel over Riemannianmanifolds. Consequently, LEM-based Riemannian metric is utilized forclassification of different channels, and a matched beamforming codebook isconstructed accordingly. Simulation results show that the proposedgeometric-based learning model achieves the maximum link rate after a shorttraining period.

Rethinking Maximum Flow Problem and Beamforming Design through Brain-inspired Geometric Lens