Model-based and Data-driven Approaches for Downlink Massive MIMO Channel Estimation

TL;DR

This paper introduces two novel downlink channel estimation methods for Massive MIMO systems that do not require downlink pilots, improving spectral efficiency especially in environments with low channel hardening.

Contribution

It proposes a model-based and a data-driven neural network approach for effective channel gain estimation without downlink pilots, applicable to any channel distribution and precoding.

Findings

Both methods outperform the state-of-the-art in spectral efficiency under low channel hardening.

The model-based method provides closed-form expressions for key precoding schemes.

Performance gains are less pronounced in uncorrelated channel models.

Abstract

We study downlink channel estimation in a multi-cell Massive multiple-input multiple-output (MIMO) system operating in time-division duplex. The users must know their effective channel gains to decode their received downlink data. Previous works have used the mean value as the estimate, motivated by channel hardening. However, this is associated with a performance loss in non-isotropic scattering environments. We propose two novel estimation methods that can be applied without downlink pilots. The first method is model-based and asymptotic arguments are utilized to identify a connection between the effective channel gain and the average received power during a coherence interval. The second method is data-driven and trains a neural network to identify a mapping between the available information and the effective channel gain. Both methods can be utilized for any channel distribution and precoding. For the model-aided method, we derive all expressions in closed form for the case when maximum ratio or zero-forcing precoding is used. We compare the proposed methods with the state-of-the-art using the normalized mean-squared error and spectral efficiency (SE). The results suggest that the two proposed methods provide better SE than the state-of-the-art when there is a low level of channel hardening, while the performance difference is relatively small with the uncorrelated channel model.