Performance Analysis of Decision Directed Maximum Likelihood MIMO Channel Tracking Algorithm

TL;DR

This paper analyzes the performance of a decision directed maximum likelihood MIMO channel tracking algorithm, providing a mathematical framework that accurately predicts error rates and tracking errors under various conditions.

Contribution

It introduces a novel analytical method to evaluate DD ML MIMO channel tracking performance, matching simulation results across different scenarios.

Findings

Analysis closely matches simulations in high rank MIMO channels.

Performance degrades with increased Doppler shift and lower SNR.

The method effectively predicts decision and tracking errors.

Abstract

In this paper, the performance of decision directed (DD) maximum likelihood (ML) channel tracking algorithm is analyzed. The ML channel tracking algorithm presents efficient performance especially in the decision directed mode of the operation. In this paper, after introducing the method for analysis of DD algorithms, the performance of ML Multiple-Input Multiple-Output (MIMO) channel tracking algorithm in the DD mode of operation is analyzed. In this method channel tracking error is evaluated for given decision error rate. Then, the decision error rate is approximated for given channel tracking error. By solving these two derived equations jointly, both the decision error rate and the channel tracking error are computed. The presented analysis is compared with simulation results for different channel ranks, Doppler frequency shifts, and SNRs, and it is shown that the analysis is a good match for simulation results especially in high rank MIMO channels and high Doppler shifts.