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Performance Investigation of NOMA versus OMA Techniques for mmWave Massive MIMO Communications

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posted on 13.09.2021, 18:23 by Joydev GhoshJoydev Ghosh, Vishal SharmaVishal Sharma, Huseyin Haci, Saurabh Singh, In-Ho Ra
The fifth-generation (5G) of cellular technology is currently being deployed over the world. In the next decade of mobile networks, beyond 5G (B5G) cellular networks with the under-development advanced technology enablers are expected to be a fully developed system that could offer tremendous opportunities for both enterprises and society at large. B5G in more ambitious scenarios will be capable to facilitate much-improved performance with the significant upgrade of the key parameters such as massive connectivity, ultra-reliable and low latency (URLL), spectral efficiency (SE) and energy efficiency (EE). Equipping non-orthogonal multiple access (NOMA) with other key drivers will help to explore systems’ applicability to cover a wide variety of applications to forge a path for future networks. NOMA empowers the networks with seamless connectivity and can provide a secure transmission strategy for the industrial internet of things (IIoT) anywhere and anytime. Despite being a promising candidate for B5G networks a comprehensive study that covers operating principles, fundamental features and technological feasibility of NOMA at mmWave massive MIMO communications is not available. To address this, a simulation-based comparative study between NOMA and orthogonal multiple access (OMA) techniques for mmWave massive multiple-input and multiple-output (MIMO) communications is presented with performance discussions and identifying technology gaps. Throughout the paper, aspects of operating principles, fundamental features and technological feasibility of NOMA are discussed. Also, it is demonstrated that NOMA not only has good adaptability but also can outperform other OMA techniques for mmWave massive MIMO communications. Some foreseeable challenges and future directions on applying NOMA to B5G networks are also provided.


It was supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP), Korean Government, Ministry of Trade, Industry, and Energy (MOTIE), grant number 20194010201800, and the National Research Foundation of Korea (NRF) grant funded by Ministry of Science and ICT (MSIT), grant number 2021R1A2C2014333.


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School of Computer Science and Robotics, Tomsk Polytechnic University (TPU), Tomsk, Russia

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