Information Theoretic Performance of Periodogram-based CFO Estimation in Massive MU-MIMO Systems

TL;DR

This paper analyzes the information theoretic performance of a periodogram-based CFO estimator in massive MIMO systems, showing it achieves an array gain comparable to ideal conditions and is more energy efficient than correlation-based methods in certain channels.

Contribution

It provides an information theoretic analysis of a periodogram-based CFO estimator, demonstrating its array gain and energy efficiency advantages in massive MIMO systems.

Findings

Achieves an $ ext{O}(\sqrt{M})$ array gain similar to ideal CFO conditions.

More energy efficient than correlation-based CFO estimators in slowly time-varying channels.

Provides insights into the performance of CFO estimation methods in massive MIMO systems.

Abstract

In this paper, we study the information theoretic performance of the modified time-reversal maximum ratio combining (TR-MRC) receiver (presented in [9]) with the spatially averaged periodogram-based carrier frequency offset (CFO) estimator (proposed in [7]) in multi-user massive MIMO systems. Our analysis shows that an $\mathcal{O}(\sqrt{M})$ array gain is achieved with this periodogram-based CFO estimator, which is same as the array gain achieved in the ideal/zero CFO scenario ($M$ is the number of base station antennas). Information theoretic performance comparison with the correlation-based CFO estimator for massive MIMO systems (proposed in [6]) reveals that this periodogram-based CFO estimator is more energy efficient in slowly time-varying channels.