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# Extremely Large-Scale MIMO

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arXiv Nov 12, 2026
Near-field E-MIMO beamforming now predicts only stream powers: 1.11 ms direct inference, with feasibility reaching 100% on the retained test set after verification and repair
Beamforming for near-field extreme massive MIMO integrated sensing and communication usually requires repeatedly solving nonconvex constrained optimization, and online latency grows with array size. PATBO fixes the communication and sensing beam directions geometrically and lets a Transformer predict only the normalized stream powers, trained with Cramér–Rao bound (CRB) sensitivity weighting. On a retained test set with 1024 antennas, 4 users and 2 targets, direct inference reaches 19.54% joint feasibility, physics-constrained power repair raises it to 87.79%, and optimizer fallback for the remainder brings it to 100%, at 1.11 ms per sample.
Beamforming for near-field extreme massive MIMO integrated sensing and communication usually requires repeatedly solving nonconvex constrained optimization, and online latency grows with array size. PATBO fixes the communication and sensing beam directions geometrically and lets a Transformer predict only the normalized stream powers, trained with Cramér–Rao bound (CRB) sensitivity weighting. On a retained test set with 1024 antennas, 4 users and 2 targets, direct inference reaches 19.54% joint feasibility, physics-constrained power repair raises it to 87.79%, and optimizer fallback for the remainder brings it to 100%, at 1.11 ms per sample.
Mohsin Ali（First author）
PATBO Integrated Sensing and Communication Extremely Large-Scale MIMO Transformer Fisher Information Matrix
researchsquare.com Oct 27, 2026
Modular XL-MIMO beam training stops sweeping the full codebook: parallax lays out candidates, statistics prune the noise-only sectors
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.
Nanjing University of Posts and Telecommunications
Chuanpeng Xu（First author）Yunchao Song（Corresponding author）
Beam Training Extremely Large-Scale MIMO Multi-Armed Bandit Thompson Sampling Integer Programming

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