天空中的移动性信息容量：高斯信道视角

中文摘要 AI 辅助

现有的空域容量指标主要量化占用率或流量，尽管相同数量的空中飞行器可能产生不同的运动选择。本文建立了“移动性信息容量”作为低空无线网络的信息论度量。它量化了在给定的机动资源预算和环境不确定性下，轨迹观测所揭示的关于有意机动输入的最大信息量。对于一种常见的固定反馈架构，我们构建了一个提升的线性高斯移动性信道，并推导出其有限时域对数行列式容量。成本和不确定性白化给出了时空移动性本征模，其最优机动资源分配遵循注水原理。当非退化模的数量随时间线性增长且其效率渐近对称时，我们得到了类似香农的定律 $R_M^{\rm G}=\frac{B_M}{2}\log_2(1+\mathrm{MNR})$，其中MNR是移动性噪声比。所提出的度量开辟了天空的运动中心容量视角，同时仍作为可区分性基线，而非受碰撞或几何约束的空域容量。

英文摘要

Existing airspace capacity metrics mainly quantify occupancy or flow, although the same number of aerial vehicles may result in different motion alternatives. This letter establishes \emph{mobility information capacity} as an information-theoretic measure for low-altitude wireless networks. It quantifies the maximum information that trajectory observations reveal about intentional maneuver inputs under a given maneuver-resource budget and environmental uncertainty. For a common fixed feedback architecture, we formulate a lifted linear-Gaussian mobility channel and derive its finite-horizon log-determinant capacity. Cost and uncertainty whitening gives the spatiotemporal mobility eigenmodes, whose optimal maneuver-resource allocation follows water-filling. When the number of nondegenerate modes grows linearly with time and their efficiencies become asymptotically symmetric, we arrive at the Shannon-like law $R_M^{\rm G}=\frac{B_M}{2}\log_2(1+\mathrm{MNR})$, where MNR is the mobility-to-noise ratio. The proposed measure opens a motion-centric capacity perspective for the sky, while remaining a distinguishability baseline rather than a collision- or geometry-constrained airspace capacity.

发表机构

• Southern University of Science and Technology（南方科技大学）
• National Mobile Communications Research Laboratory, Southeast University（东南大学移动通信国家重点实验室）
• Technical University of Berlin（柏林工业大学）
• Institute of Mobile Communications, Southwest Jiaotong University（西南交通大学移动通信研究所）

机构由 AI 辅助整理，请以论文原文为准。

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5 pages