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

Massive multiple input multiple output(MIMO)-based fully-digital receiveantenna arrays eventuate a huge amount of circuit costs and complexity todirection of arrival(DOA) estimation, which is hard to satisfy the needs ofhigh precision and low cost in future green wireless communication. To addressthis challenge, a novel heterogeneous hybrid MIMO receiver is proposed in thispaper and a high performance DOA estimator called heterogeneous cross-minimumdistance (HCMD) is developed based on the structure. The antenna arrays arefirst divided into multiple groups, and each group adopts a different hybridstructure. The virtual antenna arrays of these groups are then used for DOAestimation to generate multiple candidate angle sets, where each set contains aunique true solution and multiple pseudo-solutions. Finally, the cross-distanceminimization method is applied to the multiple candidate angle sets to selectthe corresponding true solution for each group, and the final DOA estimation isgiven by combining the multiple true solutions. Simulation results show that asthe number of antennas tends to large-scale, the proposed method can rapidlyfind the true solution for each group and achieve excellent estimationperformance.

A New Heterogeneous Hybrid Massive MIMO Receive Structure of Rapidly Eliminating DOA Ambiguity via Machine Learning