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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Signal processing in wireless communications, such as precoding, detection,and channel estimation, are basically about solving inverse matrix problems,which, however, are slow and inefficient in conventional digital computers,thus requiring a radical paradigm shift to achieve fast, real-time solutions.Here, for the first time, we apply the emerging analog matrix computing (AMC)to the linear precoding of massive MIMO. The real-valued AMC concept isextended to process complex-valued signals. In order to adapt the MIMO channelmodels to RRAM conductance mapping, a new matrix inversion circuit isdeveloped. In addition, fully analog dataflow and optimized operationalamplifiers are designed to support AMC precoding implementation. Simulationresults show that the zero-forcing precoding is solved within 20 ns for a16x128 MIMO system, which is two orders of magnitude faster than theconventional digital approach. Meanwhile, the energy efficiency is improved by50x.

Extremely-Fast, Energy-Efficient Massive MIMO Precoding with Analog RRAM Matrix Computing