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新论文 · v1arXiv:2611.11849

Joint LSFC and Nakagami-m Estimation From Downlink SINR in GZF-Precoded CF-mMIMO

Longfei Liu, Yichen Guo, Lili Xu, Yihang Cheng, Tao Peng, Wenbo Wang

原文摘要

Accurate characterization of the instantaneous signal-to-interference-plus-noise ratio (SINR) is important for reliability-sensitive cell-free massive multiple-input multiple-output (CF-mMIMO) systems. This paper proposes a joint estimation method for link-specific large-scale fading coefficients (LSFCs) and a common Nakagami-$m$ shape parameter using power and downlink SINR measurements. A tractable SINR distribution is first derived under global zero-forcing (GZF) precoding, Nakagami-$m$ fading, and link-level fractional power control (FPC). Based on the derived probability density function (PDF), a model-driven neural network (NN) is developed to solve the resulting maximum-likelihood estimation (MLE) problem. Simulations show that the reconstructed SINR distributions obtained with the estimated parameters closely match the empirical distributions. With $2 \times 10^{5}$ samples, the proposed method achieves an LSFC root-mean-square error (RMSE) of approximately 1.0 dB and an $m$ RMSE of approximately 0.085.

cs.IT · eess.SP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.18439

RegimeVGGT: Layer-Wise Spatially Preserving Redundancy Removal for Visual Geometry Grounded Transformer

Jinhao You, Shuo Lyu, Zhuohang Lyu, Tanxuan Li, Zibo Zhao, Jiaxiang Hu, Kai Tang, Yichen Guo

原文摘要

Visual Geometry Grounded Transformer (VGGT) recovers dense 3D scene structure from multi-view images in one forward pass, but quadratic cross-frame attention limits its scalability. Existing training-free accelerators exploit token, frame, or attention redundancy; we investigate how layer-wise functional roles can guide compression. Our spectral, probing, and causal analyses reveal three regimes: shallow layers lack cross-view structure, middle layers drive cross-view alignment, and deep layers are redundant for dense geometry yet their cross-frame attention remains essential for pose. RegimeVGGT applies layer-wise U-shaped compression along two axes: Saliency-Guided Banded Merging protects geometry- and edge-salient tokens, while Selectively Protected K/V Downsampling preserves cross-frame spatial coverage and the pose-critical path through a phase-shifted spatial grid, a reference-frame anchor, and uncompressed camera/register tokens. Without any training, RegimeVGGT achieves a 6.7x speedup over the memory-optimized VGGT* at matched reconstruction quality. Project page: https://tracyou07.github.io/regimevggt/

cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2610.27746

S$^2$-MAS: A Society-Scale Multi-Agent Simulation Framework with a 500-Million-Persona Resource

Yang Li, Yike Wang, Di Wan, Chujun He, Zongsi Xu, Yitao Yu, Shuai Zhong, Minghao Pan 等 23 位作者

原文摘要

Simulating economic responses at society scale requires decision rules that respond to household circumstances and accounting rules that track their consequences. We introduce S$^2$-MAS, a multi-agent simulation framework with a resource of 500 million synthetic household personas. It combines trainable candidate-scoring policies, reusable financial scenarios and explicit state transitions. Trained with SFT + RLCD on 2.93 million examples, YN-mini-MLX achieves 74.55% accuracy and 38.37% question-macro F1 on held-out consumer financial-choice tasks, improving on the training-question majority baseline by 2.78 and 6.21 percentage points. A completed application evaluates 12 million distinct households in two purchase-funding conditions: 24 million one-shot choices feed 288 million monthly ledger states in 3 h 59 min on 14 GPUs. Jointly increasing purchase goals and approved limits produces heterogeneous consumption, cash and repayment paths, with much of the consumption variation arising from transitions between deferral and purchase. In a separate 1,060-household retrospective consumption experiment, standalone YN-master-9B has 3.42% lower annual MAE than the deterministic fully rational benchmark, alongside a 19.7% lower predicted population mean than observed. Information-timing tests show how year labels and later outcome information change decisions. Together, these experiments establish an executable alternative for studying heterogeneous household behavior with learned policies and explicit financial accounting.

cs.MA · cs.AI

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arXiv ↗PDF ↗