Self-Calibration for Massive MIMO with Channel Reciprocity and Channel Estimation Errors

TL;DR

This paper introduces two calibration schemes for TDD massive MIMO systems to mitigate channel reciprocity errors caused by RF mismatches, improving system performance with analytical and simulation validation.

Contribution

The paper proposes inverse calibration that accounts for both reciprocity and estimation errors, outperforming traditional relative calibration methods.

Findings

Inverse calibration outperforms relative calibration.

Closed-form ergodic sum rate expressions derived.

Analytical results validated by simulations.

Abstract

In time-division-duplexing (TDD) massive multiple-input multiple-output (MIMO) systems, channel reciprocity is exploited to overcome the overwhelming pilot training and the feedback overhead. However, in practical scenarios, the imperfections in channel reciprocity, mainly caused by radio-frequency mismatches among the antennas at the base station side, can significantly degrade the system performance and might become a performance limiting factor. In order to compensate for these imperfections, we present and investigate two new calibration schemes for TDD-based massive multi-user MIMO systems, namely, relative calibration and inverse calibration. In particular, the design of the proposed inverse calibration takes into account a compound effect of channel reciprocity error and channel estimation error. We further derive closed-form expressions for the ergodic sum rate, assuming maximum ratio transmissions with the compound effect of both errors. We demonstrate that the inverse calibration scheme outperforms the traditional relative calibration scheme. The proposed analytical results are also verified by simulated illustrations.