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

For massive MIMO AF relays, symbol detection becomes a practical issue whenthe number of antennas is not large enough, since linear methods arenon-optimal and optimal methods are exponentially complex. This paper proposesa new detection algorithm that offers Bayesian-optimal MSE at the cost of$O(n^3)$ complexity per iteration. The algorithm is in essence a hybrid of twomethods recently developed for deep learning, with particular optimization forrelay. As a hybrid, it inherits from the two a state evolution formulism, wherethe asymptotic MSE can be precisely predicted through a scalar equivalentmodel. The algorithm also degenerates easily to many results well-known whensingle-hop considered.

Symbol Detection for Massive MIMO AF Relays Using Approximate Bayesian Inference