Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach
Open accessAbstract
Non-orthogonal multiple access (NOMA) has emerged as a promising radio access technique for enabling the performance enhancements promised by the fifth-generation (5G) networks in terms of connectivity, latency, and spectrum efficiency. In the NOMA uplink, detection based on successive interference cancellation (SIC) with device clustering has been suggested. If the receivers are equipped with multiple antennas, SIC can be combined with minimum mean-squared error (MMSE) beamforming. However, there exists a tradeoff between the NOMA cluster size and the incurred SIC error. Larger clusters lead to larger errors but they are desirable from the spectrum efficiency and connectivity point of view. To enable the deployment of large clusters, we propose a novel online learning detection method for the NOMA uplink. We design an online adaptive filter in the sum space of linear and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design is robust against variations of a dynamic wireless network that can deteriorate the performance of a purely nonlinear adaptive filter. We demonstrate by simulations that the proposed method outperforms (symbol level) MMSE-SIC based detection for large cluster sizes.
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ML for optimal user clustering and power allocation in mmWave NOMA has been proposed.
“Also, ML for optimal user clustering and power allocation in mmWave NOMA has been proposed in [231].”
From Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019) · cites this paper as [231]
The Introduction and Section III describe an online-learning method for detecting signals in a clustered NOMA uplink. They do not describe an ML method for optimizing user clustering or power allocation, or identify the setting as mmWave NOMA. The citation supports the ML-for-NOMA aspect, but not the proposed optimization attributed to it. (LLM, conf=0.86)
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Cites
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Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
Nawaz, S. J., Sharma, S. K., Wyne, S., Patwary, M. N., & Asaduzzaman, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909490 · Cited by 658 · see citing works
- Authors
- Awan, D. A., Cavalcante, R. L. G., Yukawa, M., & Stanczak, S.
- Affiliations
- Technische Universität Berlin (DE)
- Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute (DE)
- Keio University (JP)
- Venue
- 2018 IEEE International Conference on Communications (ICC)
- Publisher
- IEEE
- Published
- 2018-05
- Pages
- 1-6
- ISSN
- 1550-3607 (ISSN-L)
- OpenAlex
- W2766095357
- Cited by
- 37
- References
- 21
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 9 · 2020: 11 · 2019: 4 · 2018: 1
- FWCI
- 9.97
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference on Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Commission (760809)
- Deutsche Forschungsgemeinschaft (STA 864/9-1)
- Japan Society for the Promotion of Science (15K06081)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › PAPR reduction in OFDM
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Techniques
- Keywords
- non-orthogonal multiple access, reproducing kernel Hilbert space, successive interference cancellation, device clustering, MMSE beamforming, nonlinear adaptive filtering, spectral efficiency
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