# Beam Training

Beam training for modular extremely large antenna arrays in hybrid near- and far-field environments no longer keeps a fixed candidate codebook. Prior subarray schemes relied on predefined codebooks or activation patterns and adapted poorly to spatially non-stationary channels; this scheme builds a parallax-aware initial codebook covering each subarray's angular shift, then learns online with a multi-armed bandit, and once a statistical steady state is detected it projects beams across subarrays and prunes noise-only sectors with a likelihood ratio test, compressing the action space. In simulation the scheme's effective spectral efficiency exceeds three comparison schemes across the evaluated SNR range, with markedly reduced training overhead.

Beam training for modular extremely large antenna arrays in hybrid near- and far-field environments no longer keeps a fixed candidate codebook. Prior subarray schemes relied on predefined codebooks or activation patterns and adapted poorly to spatially non-stationary channels; this scheme builds a parallax-aware initial codebook covering each subarray's angular shift, then learns online with a multi-armed bandit, and once a statistical steady state is detected it projects beams across subarrays and prunes noise-only sectors with a likelihood ratio test, compressing the action space. In simulation the scheme's effective spectral efficiency exceeds three comparison schemes across the evaluated SNR range, with markedly reduced training overhead.