Symbolic Framework for Evaluation of NOMA Modulation Impairments Based on Irregular Constellation Diagrams

• Published in: Information (Basel) Vol. 16; no. 6; p. 468
• Main Authors: Stefanovic, Nenad, Mladenovic, Vladimir, Jovanovic, Borisa, Dabora, Ron, Kar, Asutosh
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025
• Subjects: Algorithms; Business metrics; Communication; Communications equipment; Digital signal processing; Digital signal processors; Disability; error vector magnitude; Field programmable gate arrays; Machine learning; modulation impairments; NOMA; Nonorthogonal multiple access; Quadrature amplitude modulation; R&D; Receivers & amplifiers; Research & development; SDR; Signal processing; Simulation; Software radio; symbolic computation; Synchronism; Transmitters
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info16060468

Complexity of non-orthogonal multiple access (NOMA) digital signal processing schemes is particularly relevant in mobile environments because of the varying channel conditions of every single user. In contrast to legacy modulation and coding schemes (MCSs), NOMA MCSs typically have irregular symbol constellations with asymmetric symbol decision regions affecting synchronization at the receiver. Research papers investigating signal processing in this emerging field usually lack sufficient details for facilitating software-defined radio (SDR) implementation. This work presents a new symbolic framework approach for simulating signal processing functions in SDR transmit–receive paths in a dynamic NOMA downlink use case. The proposed framework facilitates simple and intuitive implementation and testing of NOMA schemes and can be easily expanded and implemented on commercially available SDR hardware. We explicitly address several important design and measurement parameters and their relationship to different tasks, including variable constellation processing, carrier and symbol synchronization, and pulse shaping, focusing on quadrature amplitude modulation (QAM). The advantages of the proposed approach include intuitive symbolic modeling in a dynamic framework for NOMA signals; efficient, more accurate, and less time-consuming design flow; and generation of synthetic training data for machine-learning models that could be used for system optimization in real-world use cases.