Mobility information capacity offers new metric for drone airspace control
Mobility Information Capacity in the Sky: A Gaussian Channel Perspective
Information Theory
Summary
Measuring how many drones can safely share airspace usually counts how many are there or how fast they move. This paper suggests a new way to measure the "mobility information capacity," which captures how much we can learn about a drone’s intended movements despite uncertainty and limited maneuvering resources. The authors develop a model treating drone movement like information flowing through a noisy channel and derive a formula similar to classic communication theories. This approach helps better understand the true potential for coordinating drone traffic in crowded skyspace.
What this means in practice
- •For drone traffic controllers: Use this measure to estimate how much maneuver information can be extracted to improve coordination and predictability of drones in crowded low-altitude airspace.
- •For wireless communication engineers: Incorporate mobility channel models to design communication systems that better handle motion-related uncertainties in networks with moving aerial vehicles.
A theory result. No direct application yet.
Authors
Weijie Yuan, Fan Liu, Shuangyang Li, Lin Zhou, Pingzhi Fan
Abstract
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.