Research On The Problem Of Pilot Contamination In Massive MIMO Channel Estimation

Massive MIMO is considered as one of the promising technologies for next generation network (5G).When BS comprises large number of antennas, massive MIMO systems can provide a remarkable increase in reliability and data rate with simple signal processing. However, the imperfect CSI is caused by the channel estimation error and the performance of massive MIMO systems is deteriorated by problems like pilot contamination. This paper just focuses on the research of contamination in massive MIMO channel estimation. The main works are as follows:First of all, the research status at home and abroad is summarized. Then the foundation knowledge of massive MIMO systems including the system model, the transmission process, the contamination and the traditional channel estimation methods is systematically studied.Secondly, most studies are based on the assumption of ideal channels in previous works, while this paper focuses on the study of physical channel model. From the correlation channel model, this paper deduces the propagation based analytical channel model i.e. finite scatterers model and then gives the pilot based channel estimate algorithms for the large-scale MIMO system model based on finite scatterers model. In the physical channel model, finite number of scattering centers can be found from BS in this model. More accurately, the angular domain is partitioned into a large but finite number of directions, so-called angle of arrival(AOA), which is smaller than the number of BS antennas.Then, the problem of contamination that is persist with physical channel model is analyzed. Considering the blind or semi-blind estimation can better increasing the spectral efficiency, after this research direction this paper this paper considers on SVD-based channel estimation with angle of arrival for physical channel model. Through mathematical deduction, we get a new channel estimator for large-scale MIMO systems and compare with some typical channel estimation algorithms by simulations. The results show that first the proposed estimator outperforms dramatically LS-based channel estimation in estimation error performance, even in strong inter-cell interference environment and is more suitable for the massive MIMO whose the channel estimation error decreases when the number of antennas grows. Second compared with traditional subspace-based semi-blind estimate algorithm, it not only has higher estimation accuracy, also has the advantage of not lost CSI. Finally, compared with another new recently EVD-based semi-blind channel estimate algorithm, the channel estimation error of SVD-based method is still smaller for physical channel model.