Pilot contamination mitigation with reduced RF chains

Abstract

In multi-cell massive MIMO systems, estimation accuracy can be severely degraded due to pilot sequences reuse in adjacent cells. This causes inter-cell interference called pilot contamination. An additional challenge is the increase in computational load due to the massive amount of data. It is, therefore, desirable to reduce the number of RF chains. In this paper, we focus on the problem of channel estimation in a multi-cell multi-user MIMO system, while addressing these two challenges. We consider a Bayesian channel estimator and design the pilot sequences to minimize the estimation error. In order to reduce the number of RF chains, we propose an analog combiner mapping the high number of sensors to the low number of RF chains. The optimal pilot sequences are shown to correspond to a user selection solution, where the users with the strongest links are chosen. In addition we show that the number of RF chains can be reduced with no significant loss in estimation performance in cases where the receive array is highly correlated.