Pilot Power Allocation Through User Grouping in Multi-Cell Massive MIMO Systems

TL;DR

This paper introduces a pilot power allocation method based on user grouping in multi-cell massive MIMO systems, optimizing uplink rate and estimation error with simplified calculations for large antenna arrays.

Contribution

It proposes a new RCEE metric, derives closed-form expressions, and develops a pilot power allocation algorithm that enhances uplink performance in massive MIMO systems.

Findings

PPA significantly improves LS estimation performance.

PPA offers notable gains at high frequency reuse factors.

LS estimation approaches MMSE performance under certain conditions.

Abstract

In this paper, we propose a relative channel estimation error (RCEE) metric, and derive closed-form expressions for its expectation $\rm {Exp}_{rcee}$ and the achievable uplink rate holding for any number of base station antennas $M$, with the least squares (LS) and minimum mean squared error (MMSE) estimation methods. It is found that RCEE and $\rm {Exp}_{rcee}$ converge to the same constant value when $M\rightarrow\infty$, resulting in the pilot power allocation (PPA) is substantially simplified and a PPA algorithm is proposed to minimize the average $\rm {Exp}_{rcee}$ per user with a total pilot power budget $P$ in multi-cell massive multiple-input multiple-output systems. Numerical results show that the PPA algorithm brings considerable gains for the LS estimation compared with equal PPA (EPPA), while the gains are only significant with large frequency reuse factor (FRF) for the MMSE estimation. Moreover, for large FRF and large $P$, the performance of the LS approaches to the performance of the MMSE, which means that simple LS estimation method is a very viable when co-channel interference is small. For the achievable uplink rate, the PPA scheme delivers almost the same average achievable uplink rate and improves the minimum achievable uplink rate compared with the EPPA scheme.