Particle Filter based Massive MIMO Channel Estimation

TL;DR

This paper introduces a novel particle filter-based approach, PUEnSRF, for accurate and efficient channel estimation in massive MIMO systems, outperforming traditional particle filters in convergence and accuracy.

Contribution

It proposes a new particle filter variant, PUEnSRF, tailored for massive MIMO channel estimation, demonstrating superior performance over conventional methods.

Findings

PUEnSRF achieves higher accuracy in channel estimation.

PUEnSRF converges faster than traditional particle filters.

Simulation results validate the effectiveness of PUEnSRSF.

Abstract

Massive multiple-input multiple-output (MIMO) communication systems have drawn significant interest recently in next-generation wireless communications. The use of a large number of antennas in massive MIMO makes the estimation of channel state information very challenging. Accurate channel state information is essential in capitalizing the advantages of the massive MIMO technology. This paper proposes the application of the Ensemble Square Root Filter (EnSRF) and a variant of EnSRF, namely a Particle wise Update version of the Ensemble Square Root Filter (PUEnSRF) to estimate the time-selective frequency-flat fading channel coefficients in the massive MIMO scenario. Simulation results clearly indicate the remarkably superior accuracy and filter convergence of PUEnSRF estimates as compared to the conventional particle filters.