Abstract:

Understanding fundamental limits of the various technologies suggested for beyond 5G cellular systems is crucial for developing efficient state-of-the-art designs. A leading technology of major interest is non-orthogonal multiple-access (NOMA). In particular, sparse, or low-density code-domain (LDCD) NOMA is a prominent sub-category, which conceptually relies on multiplexing low-density signatures. The main attractiveness of this class of NOMA schemes is in its inherent receiver complexity reduction, achieved by utilizing message-passing algorithms, and different variants of sparse NOMA have gained much attention in 5G standardization.

Relying on recent results from the spectral theory of large random graphs, we derive in this work an explicit rigorous closed-form analytical expression for the optimum spectral efficiency in the large-system limit of regular sparse NOMA. The latter setting corresponds to the case where only a fixed and finite number of orthogonal resources is allocated to any designated user, and vice versa. The basic Verdú-Shamai (1999) formula for (dense) randomly-spread code-division multiple-access (RS-CDMA) turns out to coincide with the limit of the derived expression, when the number of orthogonal resources per user grows large. Furthermore, regular sparse NOMA is shown to be spectrally more efficient than RS-CDMA across the entire system load range. It may therefore serve as an efficient and tractable means for reducing the throughput gap to orthogonal transmission in the underloaded regime, and to the ultimate Cover-Wyner bound in overloaded systems. The spectral efficiency is also derived in closed form for the sub-optimal linear minimum-mean-square-error (LMMSE) receiver, which again extends the corresponding Verdú-Shamai (1999) formula to regular sparse NOMA.

The talk is based on a joint work with Prof. Benjamin Zaidel from Bar-Ilan University, and Prof. Shlomo Shamai (Shitz) from the Technion, Israel.

Bio:

Ori Shental (Senior Member, IEEE) received the B.Sc. and Ph.D. degrees in electrical engineering from Tel-Aviv University, Tel-Aviv, Israel, in 1996 and 2006, respectively. He is with Qualcomm Research, Bridgewater, NJ, USA. He is also an Adjunct Associate Professor with Columbia University. He was a Postdoctoral Researcher with the University of California at San Diego, from 2006 to 2008. From 1996 to 2003, he served as an R&D Engineer. He had been with Qualcomm Inc. since 2008, except the years 2015 working as a Senior Algorithm Researcher with Toga Networks, and from 2016 to 2020 holding a position as a Member of Technical Staff and a Research Scientist with the Communications Theory Department, Bell Labs, Holmdel, NJ, USA. His fields of research are interdisciplinary and include wireless communications, information theory, signal processing, probabilistic inference, statistical physics and machine learning. He had received several awards for academic and R&D excellence, including the IEEE Communications Society’s 2008 Best Paper Award in the area of signal processing and coding for data storage and the IEEE Globecom 2019 Best Paper Award.