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Error Performance of Sparse Code Multiple Access Networks with Joint ML Detection
KTH, School of Electrical Engineering (EES), Communication Theory.ORCID iD: 0000-0002-5407-0835
2016 (English)In: 2016 IEEE 83RD VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING), IEEE, 2016Conference paper (Refereed)
Abstract [en]

This paper investigates error performance of sparse code multiple access (SCMA) networks with multiple access channels (MAC) and broadcast channels (BC). We give the closedform expression for the pairwise error probability (PEP) of joint maximum likelihood (ML) detection for multiuser signals over additive white Gaussian noise (AWGN) and Rayleigh fading channels with an arbitrary number of users and multidimensional codebooks. An upper bound for the average symbol error rate (SER) is calculated. The bound is tight in both AWGN channel and Rayleigh fading channels for high SNR regions. The analytical bounds are compared with simulations, and the results confirm the effectiveness of the analysis for both AWGN and Rayleigh fading channels.

Place, publisher, year, edition, pages
IEEE, 2016.
, IEEE Vehicular Technology Conference Proceedings, ISSN 1550-2252
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Transport Systems and Logistics
URN: urn:nbn:se:kth:diva-197029DOI: 10.1109/VTCSpring.2016.7504241ISI: 000386528400193ScopusID: 2-s2.0-84979762828ISBN: 978-1-5090-1698-3OAI: diva2:1053112
IEEE 83rd Vehicular Technology Conference (VTC Spring), MAY 15-18, 2016, Nanjing, PEOPLES R CHINA

QC 20161208

Available from: 2016-12-08 Created: 2016-11-28 Last updated: 2016-12-08Bibliographically approved

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