Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 26, Issue 4, Pages 607-611Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2019.2902017
Keywords
Approximate Bayesian inference; massive MIMO; phase noise; symbol detection
Categories
Funding
- National Natural Science Foundation of China (NSFC) for Distinguished Young Scholars of China [61625106]
- NSFC [61531011]
- Ministry of Science and Technology of Taiwan [MOST 106-2221-E-110-019]
- ITRI in Hsinchu, Taiwan
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In this letter, we investigate the symbol detection of an uplink massive multiple-input multiple-output system impaired by phase noise at the transmitter and receiver sides. We propose a low-complexity iterative algorithm using approximate Bayesian inference based on the framework of generalized expectation consistent signal recovery to recover the symbol vector from nonlinear noisy measurements. Numerical results show that the proposed algorithm outperforms the existing algorithm and approaches the symbol error rate limit of a genie detector in high signal-to-noise ratio (SNR) regime, while the performance loss is very small in medium SNR. In particular, the complexity of proposed algorithm is quadratic, which makes it particularly suitable for large systems.
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