What question did this study set out to answer?

The aim is to enhance near-field channel parameter estimation using novel algorithms based on partial relaxation.

April 4, 20260 citationsOpen Access

Partial Relaxation for Near-Field Channel Parameter Estimation: Algorithms and Cramér–Rao Bounds

MAMohammad Abu AqoulahUniversitat Autònoma de Barcelona DGDoğa GürgünoğluAselsan (Turkey)GFGabor FodorEricsson (Sweden)

Key Points

The aim is to enhance near-field channel parameter estimation using novel algorithms based on partial relaxation.
Developed a maximum likelihood estimator for known-pilot scenarios.
Created a covariance-fitting estimator for unknown-symbol circumstances.
Derived and analyzed corresponding Cramér–Rao bounds for performance benchmarking.
Compared the proposed estimators against the near-field 2D-MUSIC baseline.
The PR-based estimators demonstrated improved accuracy in parameter estimation compared to existing methods.
Cramér–Rao bounds provided theoretical limits on the estimation accuracy of the proposed techniques.

Abstract

This record contains the author-accepted manuscript of the paper “Partial Relaxation for Near-Field Channel Parameter Estimation: Algorithms and Cramér–Rao Bounds.” The paper studies near-field channel parameter estimation in a multi-user uplink setup under a spherical-wave model. Based on the partial relaxation (PR) principle, it develops two estimators: a PR-based maximum likelihood estimator for the known-pilot case and a PR-based covariance-fitting estimator for the unknown-symbol case. The work also derives the corresponding Cramér–Rao bounds and benchmarks the proposed estimators against a near-field 2D-MUSIC baseline.

Ask AI

Helpful

Bookmark

View Full Paper