Journal
2019 28TH WIRELESS AND OPTICAL COMMUNICATIONS CONFERENCE (WOCC)
Volume -, Issue -, Pages 237-241Publisher
IEEE
DOI: 10.1109/wocc.2019.8770550
Keywords
Massive multiple-input multiple-output (MI-MO); linear minimum mean square error (LMMSE); weighted Neumann series (WNS); matrix inversion; off-line
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Funding
- National Natural Science Foundation of China [61871029]
- Beijing Natural Science Foundation [L172049]
- Beijing Municipal Commission of Science and Technology [Z181100003218008]
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In massive multiple-input multiple-output (MIMO) system, Neumann series (NS) expansion-based linear minimum mean square error (LMMSE) detection has been proposed due to its simple and efficient multi-stage pipeline hardware implementation. However, it suffers from poor performance and slow convergence as the number of the users grows. To address this issue, we proposed a novel weighted Neumann series (WNS)-based LMMSE detection to minimize the error between the exact matrix inversion and the WNS-based matrix inversion. Moreover, the optimal weights are obtained according to on-line learning basis. Numerical results indicate that the learning-based WNS detection outperforms the conventional NS-based detection and achieves near-LMMSE performance with a significantly lower computational complexity.
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