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

Deep learning provides powerful means to learn from spectrum data and solvecomplex tasks in 5G and beyond such as beam selection for initial access (IA)in mmWave communications. To establish the IA between the base station (e.g.,gNodeB) and user equipment (UE) for directional transmissions, a deep neuralnetwork (DNN) can predict the beam that is best slanted to each UE by using thereceived signal strengths (RSSs) from a subset of possible narrow beams. Whileimproving the latency and reliability of beam selection compared to theconventional IA that sweeps all beams, the DNN itself is susceptible toadversarial attacks. We present an adversarial attack by generating adversarialperturbations to manipulate the over-the-air captured RSSs as the input to theDNN. This attack reduces the IA performance significantly and fools the DNNinto choosing the beams with small RSSs compared to jamming attacks withGaussian or uniform noise.

Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond