A Coordinated Approach to Channel Estimation in Large-Scale Multiple-Antenna Systems

TLDR

Massive MIMO systems suffer from pilot contamination, a major bottleneck in multi‑cell networks where per‑cell beamforming is used to mitigate interference. The study proposes a coordinated channel‑estimation strategy for multi‑cell interference‑limited networks. The method coordinates cells at low rate during estimation, exploiting second‑order channel statistics to distinguish interfering users even with correlated pilots. Analytically, the approach eliminates pilot contamination in the massive‑antenna limit under covariance conditions, and simulations show performance gains even with small 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.