Research Article
Per-Antenna Maximum Likelihood Detector for Massive MIMO
@INPROCEEDINGS{10.1007/978-3-319-72823-0_25, author={Senjie Zhang and Zhiqiang He and Baoyu Tian}, title={Per-Antenna Maximum Likelihood Detector for Massive MIMO}, proceedings={5G for Future Wireless Networks. First International Conference, 5GWN 2017, Beijing, China, April 21-23, 2017, Proceedings}, proceedings_a={5GWN}, year={2018}, month={1}, keywords={Massive MIMO Maximum likelihood detection MLD Belief propagation BP Parallel processing 5G}, doi={10.1007/978-3-319-72823-0_25} }- Senjie Zhang
Zhiqiang He
Baoyu Tian
Year: 2018
Per-Antenna Maximum Likelihood Detector for Massive MIMO
5GWN
Springer
DOI: 10.1007/978-3-319-72823-0_25
Abstract
Massive multiple-input multiple-output (MIMO) systems have attracted extensive attention recently due to their potentials to provide high system capacity. The uplink receiver of massive MIMO is with very high complexity due to the large number of antennas at base station, while the processing time budget reduces in order of magnitude due to the low latency requirement in next generation wireless systems like 5G. In this paper, a per-antenna maximum likelihood (PAML) detector is proposed to address this issue. The proposed PAML detector separates and distributes ML detection to a group of observation nodes (VNs). Each VN associates with a receiving antenna and extracts a fraction of soft information using ML detection. The soft information from all VNs is accumulated before being delivered to channel decoder. Thus the degree of parallelism scales up with antenna number. Furthermore, VN of PAML detector works independently each other. High localization benefits parallel processing a lot. Simulation results show that PAML detector approaches ML detector and outperforms MMSE detector.
