A Unified Beam Training Framework for Near-Field MIMO With Arbitrarily-Oriented Arrays

In near-field multiple-input multiple-output (MIMO) communications, the arbitrary orientation of antenna arrays introduces unique challenges to beam training, as energy distributions become highly dependent on array configurations and exhibit complex spatial patterns. To address these challenges, we present a unified dual-stage two-dimensional beam training framework that optimizes both angular coverage and beamwidth so as to adapt to varying channel characteristics of the two-sided near-field channels. We introduce a two-stage adaptive beam training framework based on a generalized angular-domain channel representation that decomposes array responses into position-dependent and structure-dependent components, revealing how spatial energy distributions evolve with array orientations. Specifically, in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stage I</i>, we extract channel energy distribution patterns through initial beam sweeping. The adaptive beam training scheme in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stage II</i> consists of three strategies: hierarchical refinement for concentrated energy scenarios, angular rotation for transitional energy distributions, and beamwidth expansion for dispersed energy patterns. This unified framework integrates these techniques into a coherent system, adapting to the full spectrum of near-field channel conditions without prior knowledge of array configurations. Extensive simulations demonstrate that our framework significantly outperforms conventional methods in achievable rate and reliability metrics.