Low-Complexity CFO Estimation for Multi-User Massive MIMO Systems

TL;DR

This paper introduces a low-complexity CFO estimation algorithm for multi-user massive MIMO systems, which scales linearly with the number of antennas and improves accuracy as system size grows.

Contribution

A novel CFO estimation method with complexity that scales linearly with antennas and is independent of users, enhancing efficiency in massive MIMO systems.

Findings

Estimation accuracy improves with increasing antennas and users.

Per-user power requirement decreases as 1/√M with more antennas.

Algorithm complexity remains linear with the number of antennas.

Abstract

Low-complexity carrier frequency offset (CFO) estimation and compensation in multi-user massive multiple-input multiple-output (MIMO) systems is a challenging problem. The existing CFO estimation algorithms incur tremendous increase in complexity with increasing number of base station (BS) antennas, $M$ and number of user terminals (UTs) $K$ (i.e. massive MIMO regime). In this paper, we address this problem by proposing a novel low-complexity algorithm for CFO estimation which uses the pilot signal received at the BS during special uplink slots. The total per-channel use complexity of the proposed algorithm increases only linearly with increasing $M$ and is independent of $K$. Analysis reveals that the CFO estimation accuracy can be considerably improved by increasing $M$ and $K$ (i.e. massive MIMO regime). For example, for a fixed $K$ and a fixed training length, the required per-user radiated power during uplink training decreases as $\frac{1}{\sqrt{M}}$ with increasing $M$.