Semi-Blind Pilot Decontamination for Massive MIMO Systems

Abstract

In multicell multiuser massive multi-input multi-output (MIMO) systems, pilot contamination degrades the uplink (UL) channel estimation performance. To mitigate the effect of pilot contamination, we propose a semiblind channel estimation method that does not require cell cooperation or statistical information of the channels. In the proposed method, we first sequentially estimate the UL data from different users in the target cell. To do that, for each user, we solve a constrained minimization problem to obtain an extracting vector and then use it to extract the desired data source from the observed mixture signal. An efficient algorithm is presented to solve the optimization problem. After the ambiguities in the extracted source are corrected with the aid of the pilot sequence, the estimates of the user UL data can be obtained. Based on the demodulated UL data of all users in the target cell, we finally obtain the least squares (LS) estimate of the channel. The pilot contamination effect is shown to be reduced as the UL data length grows. Simulation results demonstrate that the proposed method significantly outperforms some existing channel estimation methods that do not require cell cooperation or channel statistics.