Improving performance of SCMA MPA decoders using estimation of conditional probabilities

Alireza Ghaffari (1) , Mathieu Leonardon (1, 2, 3, 4) , Yvon Savaria (1) , Christophe Jego (2, 3, 4) , Camille Leroux (2, 4, 3)

1 EPM - École Polytechnique de Montréal
2 IMS - Laboratoire de l'intégration, du matériau au système
3 Bordeaux INP - Institut Polytechnique de Bordeaux
4 UB - Université de Bordeaux

Sparse code multiple access (SCMA) is a new type of non-orthogonal modulation suggested for 5G systems offering lower bit-error rate and higher spectral efficiency. There are many challenges when designing high throughput SCMA message passing decoders to meet the standards expected from 5G networks. Particularly, the message passing algorithm (MPA) needs many exponential computations to calculate conditional probabilities in case of Gaussian noise channels. This paper describes a sub-optimal modeling of noise using polynomial probability distributions rather than a normal distribution to eliminate the exponential calculations for MPA detectors. Simulation results demonstrate that an estimated SCMA MPA reaches the desired bit-error rate performance with much lower computational/hardware complexity.

Mots clés: 5G; Sparse Code Multiple Access (SCMA); MPA Decoder; Uplink; iterative multi-user detection; BER

Domaines: Electronique; Micro et nanotechnologies/Microélectronique; Traitement du signal et de l'image [eess.SP]; Calcul parallèle, distribué et partagé [cs.DC]

Alireza Ghaffari, Mathieu Leonardon, Yvon Savaria, Christophe Jego, Camille Leroux. Improving performance of SCMA MPA decoders using estimation of conditional probabilities. 2017 15th IEEE International New Circuits and Systems Conference (NEWCAS), Jun 2017, Strasbourg, France. pp.21-24, ⟨10.1109/NEWCAS.2017.8010095⟩. ⟨hal-03762203⟩