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

Wireless links are increasingly used to deliver critical services, whileintentional interference (jamming) remains a very serious threat to suchservices. In this paper, we are concerned with the design and evaluation of auniversal anti-jamming building block, that is agnostic to the specifics of thecommunication link and can therefore be combined with existing technologies. Webelieve that such a block should not require explicit probes, sounding,training sequences, channel estimation, or even the cooperation of thetransmitter. To meet these requirements, we propose an approach that relies onadvances in Machine Learning, and the promises of neural accelerators andsoftware defined radios. We identify and address multiple challenges, resultingin a convolutional neural network architecture and models for a multi-antennasystem to infer the existence of interference, the number of interferingemissions and their respective phases. This information is continuously fedinto an algorithm that cancels the interfering signal. We develop a two-antennaprototype system and evaluate our jamming cancellation approach in variousenvironment settings and modulation schemes using Software Defined Radioplatforms. We demonstrate that the receiving node equipped with our approachcan detect a jammer with over 99% of accuracy and achieve a Bit Error Rate(BER) as low as $10^{-6}$ even when the jammer power is nearly two orders ofmagnitude (18 dB) higher than the legitimate signal, and without requiringmodifications to the link modulation. In non-adversarial settings, our approachcan have other advantages such as detecting and mitigating collisions.

Towards an AI-Driven Universal Anti-Jamming Solution with Convolutional Interference Cancellation Network