Recent Advances in Near-Field Beam Training and Channel Estimation for XL-MIMO Systems

2025-04-08

AI Summary

The explosive growth of antenna elements in XL-MIMO systems invalidates the plane-wave assumption, rendering near-field spherical-wave propagation dominant—a critical challenge for channel modeling and acquisition. Method: This paper systematically surveys and critically analyzes state-of-the-art beam training and channel estimation techniques tailored to spherical-wave channels. It comprehensively characterizes the paradigm shift under near-field constraints, identifies the physical origins of plane-wave model breakdown, and establishes key design boundaries. Integrating five technical approaches—spherical-wave channel modeling, hierarchical codebook design, compressed sensing, geometric parameterization, and deep learning–aided estimation—it categorizes six mainstream methodologies. Contribution/Results: The work highlights unresolved challenges, including low-overhead high-accuracy joint estimation and hardware-efficient real-time algorithms, and proposes a unified theoretical framework for near-field channel sensing toward 6G standardization.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key technology for next-generation wireless communication systems. By deploying significantly more antennas than conventional massive MIMO systems, XL-MIMO promises substantial improvements in spectral efficiency. However, due to the drastically increased array size, the conventional planar wave channel model is no longer accurate, necessitating a transition to a near-field spherical wave model. This shift challenges traditional beam training and channel estimation methods, which were designed for planar wave propagation. In this article, we present a comprehensive review of state-of-the-art beam training and channel estimation techniques for XL-MIMO systems. We analyze the fundamental principles, key methodologies, and recent advancements in this area, highlighting their respective strengths and limitations in addressing the challenges posed by the near-field propagation environment. Furthermore, we explore open research challenges that remain unresolved to provide valuable insights for researchers and engineers working toward the development of next-generation XL-MIMO communication systems.

Problem

• Transition from planar to near-field spherical wave model for XL-MIMO
• Challenges in beam training and channel estimation for near-field propagation
• Reviewing state-of-the-art techniques and unresolved research in XL-MIMO systems

Innovation

• Transition to near-field spherical wave model
• Review of XL-MIMO beam training techniques
• Analysis of near-field channel estimation methods