TY - GEN
T1 - Detection for 5G-NOMA
T2 - 2018 IEEE International Conference on Communications, ICC 2018
AU - Awan, Daniyal Amir
AU - Cavalcante, Renato L.G.
AU - Yukawa, Masahiro
AU - Stanczak, Slawomir
N1 - Funding Information:
ACKNOWLEDGMENT This work has been performed in the framework of the Horizon 2020 project ONE5G (ICT-760809)receiving funds from the European Union. The authors would like to acknowledge the contributions of their colleagues in the project, although the views expressed in this contribution are those of the authors and do not necessarily represent the project. This research was also supported by Grant STA 864/9-1 from German Research Foundation (DFG) and JSPS Grants-in-Aid (15K06081).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/27
Y1 - 2018/7/27
N2 - 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.
AB - 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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U2 - 10.1109/ICC.2018.8422449
DO - 10.1109/ICC.2018.8422449
M3 - Conference contribution
AN - SCOPUS:85051418736
SN - 9781538631805
T3 - IEEE International Conference on Communications
BT - 2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 May 2018 through 24 May 2018
ER -