Core Concepts
The paper proposes a co-design of pilot signal and channel estimator tailored for hybrid-field communications in extremely large-scale MIMO systems. It introduces a novel pilot signal design algorithm based on the alternating direction method of multipliers (ADMM) to minimize the mutual coherence of the sensing matrix, and a two-stage channel estimation algorithm that sequentially estimates the line-of-sight (LoS) channel component and the hybrid-field scattering channel components.
Abstract
The paper addresses the challenge of hybrid-field channel estimation in extremely large-scale MIMO (XL-MIMO) systems, which comprise a line-of-sight (LoS) channel component and far-field and near-field scattering channel components.
Key highlights:
Pilot Signal Design: The authors propose an ADMM-based algorithm to design optimal pilot signals that minimize the mutual coherence of the sensing matrix, which is crucial for reliable sparse channel recovery in compressive sensing.
Two-Stage Channel Estimation:
LoS Channel Estimation: The authors use a gradient descent algorithm to estimate the LoS channel component parameters (distance and angle of departure).
Hybrid-Field Scattering Channel Estimation: The authors develop a Bayesian matching pursuit (BMP)-based algorithm to jointly estimate the far-field and near-field scattering channel components, considering both scenarios with and without prior channel knowledge.
The simulation results demonstrate the superiority of the proposed co-design approach over conventional compressive sensing-based methods in terms of sparse channel recovery performance.
Stats
The paper does not provide any specific numerical data or metrics to support the key claims. The results are presented in the form of simulation performance comparisons.