Achievable Rates in Uplink Massive MIMO Systems with Pilot Hopping

Abstract

Massive MIMO promises large spectral and power efficiency gains. However, pilot contamination limits these aspired gains. In this paper, we propose a structured, i.e., non-random, pilot hopping and a weighted moving average channel estimation for time-varying massive MIMO systems. The proposed algorithm diversifies the contaminating (interfering) sources, such that all the users enjoy the hopping advantages. We analyze the performance of multi-cell network, where the base stations perform matched filtering or zero-forcing for data detection. Using polynomial expansion approximations, we obtain achievable rates that are tight even at finite (not very large) number of antennas. It is shown that the spectral efficiency gain achieved by pilot hopping is not a monotonic function of the pilot reuse factor $\beta $ in some cases and, therefore, brings to attention that careful cell planning is required when applying pilot hopping. Moreover, the proposed algorithm involves a weighted moving average that uses the previously obtained channel estimates (of the preceding blocks) if they are correlated with the current one. Due to the massive MIMO properties, the algorithm is robust to channel time-variations and can be used with any previously proposed channel estimation method.