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

This paper considers the transceiver design for uplink massive multiple-inputmultiple-output (MIMO) systems with channel sparsity in the angular domain.Recent progress has shown that sparsity-learning-based blind signal detectionis able to retrieve the channel and data by using massage-passing based sparsematrix factorization methods. Short pilots sequences are inserted into userpackets to eliminate the so-called phase and permutation ambiguities inherentin sparse matrix factorization. In this paper, to exploit the knowledge ofthese short pilot sequences more efficiently, we propose a semi-blindchannel-and-signal estimation (SCSE) scheme in which the knowledge of the pilotsequences are integrated into the message passing algorithm for sparse matrixfactorization. The SCSE algorithm involves enumeration over all possible userpermutations, and so is time-consuming when the number of users is relativelylarge. To reduce complexity, we further develop the simplified SCSE (S-SCSE) toaccommodate systems with a large number of users. We show that our semi-blindsignal detection scheme substantially outperforms the state-of-the-art blinddetection and training-based schemes in the short-pilot regime.

Semi-Blind Channel-and-Signal Estimation for Uplink Massive MIMO With Channel Sparsity