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
5G
extreme low-dense code-domain NOMA
code-domain NOMA schemes
information theory
load feature
number-of-resource elements
sparsity feature
Author
Mai T. P. Le
University of Danang
Sapienza University of Rome
Guido Ferrante
Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems
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 1864-1872Subject Categories
Telecommunications
Signal Processing
Computer Science
DOI
10.1049/iet-com.2018.5241