arXiv cs.AI· Lyutianyang Zhang, Yixin Ying, Liu Cao, Vanlin Sathya, Jinke Ren, Shuguang Cui·· 12 小时前AI 评分22
基于光线追踪的动态信道探测：用自回归 Wi-Fi CSI 预测提升波束成形吞吐
Ray-Tracing-Based Dynamic Channel Sounding With Autoregressive Wi-Fi CSI Prediction
AI 导读
研究提出一种动态探测框架，将自回归 CSI 预测与受 SINR 约束的探测间隔选择结合，用带旋转位置嵌入的 Transformer 把设备位置转换为 CSI 序列，跨多个子载波预测 CSI 以更新波束成形。
正文 · 原文
Abstract:Channel sounding in Wi-Fi multiple-input multiple-output (MIMO) systems must balance channel state information (CSI) accuracy against CSI acquisition overhead. Device mobility causes channel aging, while autoregressive CSI prediction introduces errors that accumulate over sounding intervals. However, standardized statistical channel models cannot fully capture the environment-specific channel dynamics encountered in physical artificial intelligence (AI) applications, motivating the use of Sionna-based ray tracing to evaluate CSI prediction under device mobility and evolving propagation geometry. This paper develops a dynamic sounding framework that integrates autoregressive CSI prediction with signal-to-interference-plus-noise ratio (SINR)-constrained interval selection. A Transformer with rotary position embedding that converts device positions into CSI sequences predicts CSI across multiple subcarriers for beamforming updates between sounding exchanges. We formulate sounding-interval selection as a predicted average-throughput maximization problem under per-stream SINR lower-bound constraints, including CSI acquisition and processing overhead. A prediction-error perturbation analysis provides the SINR bounds, and cumulative-sum enumeration globally solves the finite single-variable problem. Actual SINR feasibility holds whenever the prediction errors satisfy the bounds. Ray-tracing experiments assess channel prediction, trajectory transfer, and autoregressive prediction errors. In single-STA Wi-Fi 7 link-level simulations, predicted-CSI beamforming improves mean goodput by 67.3%, 112.7%, and 119.1% over stale CSI for linear, circular, and cosine trajectories, respectively.
| Subjects: | Information Theory (cs.IT); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI) |
| Cite as: | arXiv:2611.05970 [cs.IT] |
| (or arXiv:2611.05970v1 [cs.IT] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05970 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lyutianyang Zhang [view email]
[v1]
Wed, 4 Nov 2026 05:04:42 UTC (7,320 KB)
来源：arXiv cs.AI · arxiv.org