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

TL;DR

XL-MIMO enables unprecedented spatial resolution but requires a shift from planar to spherical-wave modeling, complicating beam training and channel estimation. The paper provides a structured taxonomy of near-field beam training and CSI methods, contrasting polar-domain and DFT-based codebooks and detailing uplink TDD, downlink FDD, and point-to-point estimation approaches. It highlights polar-domain sparsity, distance-aware dictionaries, and hybrid-beamforming considerations, while identifying open challenges such as real-measurement validation, sensing-aided approaches, and FR3-band effects. Together, these insights guide the design of scalable, robust XL-MIMO solutions for 6G and beyond, with emphasis on practical validation and environment-aware adaptability.

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.

Figures (6)

• Figure 1: Near-field channel: a) spherical wave illustration; b) beam energy-spread effect (received beam pattern under far-field beamformers for different user angles and ranges under 256 antennas at 30 GHz); c) finite depth of beamfocusing (512 antennas at 100 GHz).
• Figure 2: Beam training: a) polar-domain-based beam training Cui_TCOMM_22; b) effective Rayleigh distance Hussain_WCNC24.
• Figure 3: Beam training Weng_TVT_24: a) far-field for angle estimation; b) joint angle and distance estimation.
• Figure 4: Beam training zhou2024: a) multi-beam training; b) remove angle ambiguity.
• Figure 5: Systems with hybrid-field channels.