Adaptive power control boosts efficiency of low-altitude aircraft swarms
Mission Efficiency Optimization in Low-Altitude Economy: Adaptive Power Allocation for Coordinating Heterogeneous Aircraft Swarms
Information Theory
Summary
Coordinating many different types of small aircraft flying close to the ground is tricky because each one has a unique job like sensing, communicating, or carrying out tasks. The authors came up with a new way to measure how well these aircraft work together, called operational-capability entropy. Using this, they designed a method to smartly share power among the aircraft to keep their whole mission running smoothly and efficiently. Their tests show this new method does better than older ways that don’t adjust power based on the aircraft’s roles or conditions.
What this means in practice
- •For drone fleet operators: Coordinate diverse drone roles with adaptive power sharing to improve mission success rates under changing conditions.
- •For wireless communication engineers: Design protocols that optimize power usage in multi-vehicle networks using joint operational capability and channel state information.
Authors
Jiarui Zhang, Wei Feng, Chao Dong, Ning Ge, Qihui Wu
Abstract
With the rapid development of the low-altitude economy, low-altitude operations are booming, where complex missions require collaborative efforts among multiple heterogeneous low-altitude aircrafts (LAAs). Specifically, different LAAs assume distinct roles: some for sensing, some for communication, some for computing, and others for mission execution, together forming a sensing-communication-computing-control (SC3) closed loop, akin to a reflex arc. To enable efficient coordination in such multi-LAA swarms, we introduce the concept of operational-capability entropy (OCE) to quantify the effective work capability of operational LAAs. Accordingly, by jointly considering heterogeneous OCE and channel conditions among LAAs, we formulate the power allocation problem with the goal of minimizing the linear quadratic regulator (LQR) cost, which serves as a metric for mission efficiency. The resulting complex optimization problem is decomposed into two convex subproblems that are solved iteratively, with closed-form solutions derived for each. Simulation results demonstrate that the proposed mission-oriented adaptive power allocation scheme significantly outperforms traditional ones.