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

Most detection algorithms in spatial modulation (SM) are formulated as linearregression via the regularized least-squares (RLS) method. In this method, thetransmit signal is estimated by minimizing the residual sum of squarespenalized with some regularization. This paper studies the asymptoticperformance of a generic RLS-based detection algorithm employed for recovery ofSM signals. We derive analytically the asymptotic average mean squared errorand the error rate for the class of bi-unitarily invariant channel matrices.  The analytic results are employed to study the performance of SM detectionvia the box-LASSO. The analysis demonstrates that the performancecharacterization for i.i.d. Gaussian channel matrices is valid for matriceswith non-Gaussian entries, as well. This justifies the partially approvedconjecture given in [1]. The derivations further extend the former studies toscenarios with non-i.i.d. channel matrices. Numerical investigations validatethe analysis, even for practical system dimensions.

RLS-Based Detection for Massive Spatial Modulation MIMO