Impact Analysis of Antenna Array Geometry on Performance of Semi-blind Structured Channel Estimation for massive MIMO-OFDM systems

TL;DR

This paper investigates how antenna array geometries affect structured channel estimation in massive MIMO-OFDM systems, showing semi-blind methods outperform training-based ones, with cylindrical arrays outperforming linear arrays.

Contribution

It provides a comparative analysis of antenna array geometries on channel estimation performance using Cramér-Rao Bound in massive MIMO-OFDM systems.

Findings

Semi-blind channel estimation outperforms training-based methods.

Uniform Cylindrical Array yields better performance than Uniform Linear Array.

Simulation results confirm the impact of array geometry on estimation accuracy.

Abstract

Channel estimation is always implemented in communication systems to overcome the effect of interference and noise. Especially, in wireless communications, this task is more challenging to improve system performance while saving resources. This paper focuses on investigating the impact of geometries of antenna arrays on the performance of structured channel estimation in massive MIMO-OFDM systems. We use Cram'er Rao Bound to analyze errors in two methods, i.e., training-based and semi-blind-based channel estimations. The simulation results show that the latter gets significantly better performance than the former. Besides, the system with Uniform Cylindrical Array outperforms the traditional Uniform Linear Array one in both estimation methods.