Ice-Filling: Near-Optimal Channel Estimation for Dense Array Systems

2024-04-10

AI Summary

To address the challenge of acquiring accurate channel state information (CSI) in dense antenna systems (DAS) with massive antenna arrays, this paper proposes a Bayesian channel estimation framework featuring a novel observation matrix design leveraging spatial channel correlation. Methodologically, we formulate the observation matrix design as a time-domain dual MIMO precoding problem—a first in the literature—and introduce the “ice-filling” algorithm: a quantized variant of water-filling enabling joint amplitude-phase controllable design. For phase-only control scenarios, we develop an efficient majorization-minimization (MM)-based optimization algorithm. Theoretical analysis yields a closed-form asymptotic minimum mean-square error (MSE) expression and establishes a provably suboptimal performance bound. Simulation results demonstrate that the proposed approach closely approaches the Bayesian optimal MSE and significantly outperforms existing methods across various practical settings.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Bayesian Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

By deploying a large number of antennas with sub-half-wavelength spacing in a compact space, dense array systems(DASs) can fully unleash the multiplexing-and-diversity gains of limited apertures. To acquire these gains, accurate channel state information acquisition is necessary but challenging due to the large antenna numbers. To overcome this obstacle, this paper reveals that exploiting the high spatial correlation of DAS channels is crucial while designing the observation matrix for optimal/near-optimal channel estimation. Firstly, we prove that the observation matrix design is equivalent to a time-domain duality of multiple-input multiple-output precoding, which can be ideally addressed by the water-filling principle. For practical realizations, a novel ice-filling algorithm is proposed to design amplitude-and-phase controllable observation matrices, and a majorization-minimization algorithm is proposed to address the phase-only controllable case. Particularly, we prove that the ice-filling algorithm can be viewed as a ``quantized"water-filling algorithm. To support the sub-optimality of the proposed designs, we provide comprehensive analyses on the achievable mean square errors and their asymptotic expressions. Finally, numerical simulations verify that our proposed channel estimation designs can achieve the near-optimal performance and outperform existing approaches significantly.

Problem

• Achieving near-optimal channel estimation in dense array systems
• Designing observation matrices for high spatial correlation exploitation
• Overcoming challenges of large antenna numbers in channel estimation

Innovation

• Exploits high spatial correlation in DAS channels
• Proposes ice-filling algorithm for observation matrix design
• Uses Bayesian regression for near-optimal channel estimation