Asynchronous Channel Training in Multi-Cell Massive MIMO

TL;DR

This paper introduces an asynchronous channel training scheme for multi-cell massive MIMO systems that reduces pilot contamination effects without base station cooperation by leveraging intentional timing mismatches and sampling diversity.

Contribution

The paper proposes a novel asynchronous training scheme, designs estimators, derives MSE bounds, and introduces an optimal delay scheme to improve channel estimation accuracy.

Findings

Asynchronous scheme significantly reduces pilot contamination.

The proposed delay scheme minimizes MSE under certain conditions.

Simulation shows improved uplink rate with asynchronous training.

Abstract

Pilot contamination has been regarded as the main bottleneck in time division duplexing (TDD) multi-cell massive multiple-input multiple-output (MIMO) systems. The pilot contamination problem cannot be addressed with large-scale antenna arrays. We provide a novel asynchronous channel training scheme to obtain precise channel matrices without the cooperation of base stations. The scheme takes advantage of sampling diversity by inducing intentional timing mismatch. Then, the linear minimum mean square error (LMMSE) estimator and the zero-forcing (ZF) estimator are designed. Moreover, we derive the minimum square error (MSE) upper bound of the ZF estimator. In addition, we propose the equally-divided delay scheme which under certain conditions is the optimal solution to minimize the MSE of the ZF estimator employing the identity matrix as pilot matrix. We calculate the uplink achievable rate using maximum ratio combining (MRC) to compare asynchronous and synchronous channel training schemes. Finally, simulation results demonstrate that the asynchronous channel estimation scheme can greatly reduce the harmful effect of pilot contamination.