Journal
IEEE WIRELESS COMMUNICATIONS LETTERS
Volume 8, Issue 4, Pages 1137-1140Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2019.2909019
Keywords
Massive MIMO; neural network; machine learning; detection
Categories
Funding
- National Natural Science Foundation of China [61771368]
- Young Elite Scientists Sponsorship Program by CAST [2016QNRC001]
- Youth Talent Support Fund of Science and Technology of Shaanxi Province [2018KJXX-025]
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Massive multiple-input multiple-output (MIMO) is a promising key technology for the fifth-generation (5G) and future mobile wireless network. Although maximum likelihood (ML) detection can get the best detection performance with the lowest bit error rate (BER), its computational complexity significantly increases as the number of antennas increases. Thus, based on neural networks, in this letter we propose a new partial learning (PL)-based detection scheme. Theoretical analyses and simulation results showed that the proposed PL-based detection scheme can achieve low BER with low computational complexity.
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