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

Sparsely spread code division multiple access (SCDMA) is a promisingnon-orthogonal multiple access technique for future wireless communications. Inthis paper, we propose a novel trainable multiuser detector called sparsetrainable projected gradient (STPG) detector, which is based on the notion ofdeep unfolding. In the STPG detector, trainable parameters are embedded to aprojected gradient descent algorithm, which can be trained by standard deeplearning techniques such as back propagation and stochastic gradient descent.Advantages of the detector are its low computational cost and small number oftrainable parameters, which enables us to treat massive SCDMA systems. Inparticular, its computational cost is smaller than a conventional beliefpropagation (BP) detector while the STPG detector exhibits nearly samedetection performance with a BP detector. We also propose a scalable jointlearning of signature sequences and the STPG detector for signature design.Numerical results show that the joint learning improves multiuser detectionperformance particular in the low SNR regime.

Trainable Projected Gradient Detector for Sparsely Spread Code Division Multiple Access