On information-theoretic limits of code-domain NOMA for 5G
Journal article, 2018

Motivated by recent theoretical challenges for 5G, this study aims to position relevant results in the literature on code-domain non-orthogonal multiple access (NOMA) from an information-theoretic perspective, given that most of the recent intuition of NOMA relies on another domain, that is, the power domain. Theoretical derivations for several code-domain NOMA schemes are reported and interpreted, adopting a unified framework that focuses on the analysis of the NOMA spreading matrix, in terms of load, sparsity, and regularity features. The comparative analysis shows that it is beneficial to adopt extreme low-dense code-domain NOMA in the large system limit, where the number of resource elements and number of users grow unboundedly while their ratio, called load, is kept constant. Particularly, when optimum receivers are used, the adoption of a regular low-dense spreading matrix is beneficial to the system achievable rates, which are higher than those obtained with either irregular low-dense or dense formats, for any value of load. For linear receivers, which are more favourable in practice due to lower complexity, the regular low-dense NOMA still has better performance in the underloaded regime (load < 1).

regular low-dense NOMA

code-domain nonorthogonal multiple access

system achievable rates

multi-access systems

regularity feature

regular low-dense spreading matrix

information-theoretic limits

NOMA spreading matrix

5G mobile communication

matrix algebra


extreme low-dense code-domain NOMA

code-domain NOMA schemes

information theory

load feature

number-of-resource elements

sparsity feature


Mai T. P. Le

University of Danang

Sapienza University of Rome

Guido Ferrante

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Giuseppe Caso

Sapienza University of Rome

Luca De Nardis

Sapienza University of Rome

Maria-Gabriella Di Benedetto

Sapienza University of Rome

IET Communications

1751-8628 (ISSN) 1751-8636 (eISSN)

Vol. 12 15 1864-1872

Subject Categories


Signal Processing

Computer Science



More information

Latest update