Nanjing University of Posts and Telecommunications

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.

The work introduces PrismQuant, a quantizer-aware rotation framework that aligns the leading activation eigenspace with the group-constant subspace of asymmetric grouped INT4, formulates rotation design as a Ky Fan trace maximization with a provably optimal closed-form solution, realizes it with compact Householder (compact-WY) transforms without gradient training, and evaluates W4A4KV4 post-training quantization on Llama, Qwen, and Mistral (including dense models up to 70B and a 30B mixture-of-experts model): on Llama-3.2-3B it sets the state of the art among compared methods in perplexity and accuracy, on Llama-3.1-70B it attains 3.85 perplexity and 72.46% average zero-shot accuracy (0.22 percentage points below full precision), and in a Llama-3.

The work introduces PrismQuant, a quantizer-aware rotation framework that aligns the leading activation eigenspace with the group-constant subspace of asymmetric grouped INT4, formulates rotation design as a Ky Fan trace maximization with a provably optimal closed-form solution, realizes it with compact Householder (compact-WY) transforms without gradient training, and evaluates W4A4KV4 post-training quantization on Llama, Qwen, and Mistral (including dense models up to 70B and a 30B mixture-of-experts model): on Llama-3.2-3B it sets the state of the art among compared methods in perplexity and accuracy, on Llama-3.1-70B it attains 3.85 perplexity and 72.46% average zero-shot accuracy (0.22 percentage points below full precision), and in a Llama-3.