Performance Analysis of Decision Directed Maximum Likelihood MIMO Channel Tracking Algorithm
Ebrahim Karami

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
