Pilot Decontamination Through Pilot Sequence Hopping in Massive MIMO Systems

TL;DR

This paper proposes a pilot sequence hopping method combined with a modified Kalman filter to significantly reduce pilot contamination in massive MIMO systems without inter-cell coordination, improving channel estimation accuracy especially at low mobility.

Contribution

It introduces a novel pilot hopping scheme and a modified Kalman filter for effective pilot contamination suppression in massive MIMO networks.

Findings

Mean squared error reduced by up to an order of magnitude at low mobility.

Pilot contamination can be significantly suppressed without inter-cell coordination.

The method improves channel estimation accuracy in massive MIMO systems.

Abstract

This work concerns wireless cellular networks applying massive multiple-input multiple-output (MIMO) technology. In such a system, the base station in a given cell is equipped with a very large number (hundreds or even thousands) of antennas and serves multiple users. Estimation of the channel from the base station to each user is performed at the base station using an uplink pilot sequence. Such a channel estimation procedure suffers from pilot contamination. Orthogonal pilot sequences are used in a given cell but, due to the shortage of orthogonal sequences, the same pilot sequences must be reused in neighboring cells, causing pilot contamination. The solution presented in this paper suppresses pilot contamination, without the need for coordination among cells. Pilot sequence hopping is performed at each transmission slot, which provides a randomization of the pilot contamination. Using a modified Kalman filter, it is shown that such randomized contamination can be significantly suppressed. Comparisons with conventional estimation methods show that the mean squared error can be lowered as much as an order of magnitude at low mobility.