A Coordinated Approach to Channel Estimation in Large-scale Multiple-antenna Systems

TL;DR

This paper proposes a coordinated channel estimation method for large-scale multi-antenna cellular networks that leverages second-order statistics to mitigate pilot contamination, significantly improving performance especially in massive MIMO systems.

Contribution

It introduces a novel low-rate inter-cell coordination scheme utilizing channel covariance information to eliminate pilot contamination effects in massive MIMO systems.

Findings

Pilot contamination can be fully mitigated with large antenna arrays under certain conditions.

The proposed method outperforms traditional estimation techniques in simulations.

Effective even with small antenna arrays.

Abstract

This paper addresses the problem of channel estimation in multi-cell interference-limited cellular networks. We consider systems employing multiple antennas and are interested in both the finite and large-scale antenna number regimes (so-called "massive MIMO"). Such systems deal with the multi-cell interference by way of per-cell beamforming applied at each base station. Channel estimation in such networks, which is known to be hampered by the pilot contamination effect, constitute a major bottleneck for overall performance. We present a novel approach which tackles this problem by enabling a low-rate coordination between cells during the channel estimation phase itself. The coordination makes use of the additional second-order statistical information about the user channels, which are shown to offer a powerful way of discriminating across interfering users with even strongly correlated pilot sequences. Importantly, we demonstrate analytically that in the large-number-of-antennas regime, the pilot contamination effect is made to vanish completely under certain conditions on the channel covariance. Gains over the conventional channel estimation framework are confirmed by our simulations for even small antenna array sizes.