Edge Information Hub: Orchestrating Satellites, UAVs, MEC, Sensing and Communications for 6G Closed-Loop Controls

TL;DR

This work addresses orchestrating sensing, communication, computing, and control for multiple robots in remote or disaster scenarios using a UAV-mounted Edge Information Hub (EIH). It proposes a sum-LQR-cost minimization framework that jointly optimizes sensor-data splitting, MEC computing allocation, satellite backhaul rates, and downlink transmit powers under energy, backhaul, and time constraints, leveraging a problem transformation and successive convex approximation (SCA) to obtain a tractable solution. The authors derive the optimal data split for given resources, prove a piecewise closed-form for the minimal computation time, and develop an iterative algorithm that converges to a sub-optimal solution, demonstrating significant performance gains over benchmark schemes. The results provide a systematic understanding of how SC$^3$ parameters influence closed-loop control in satellite-UAV-terrestrial networks, with practical implications for 6G-era edge-enabled robotics.

Abstract

An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with not only communication but also sensing and computing functions. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial base stations or mobile edge computing (MEC), the EIH would direct the operations of robots via sensing-communication-computing-control ($\textbf{SC}^3$) closed-loop orchestration. This paper aims to optimize the closed-loop control performance of multiple $\textbf{SC}^3$ loops, with constraints on satellite-backhaul rate, computing capability, and on-board energy. Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources. We first derive the optimal splitting ratio of sensor data, and then recast the problem to a more tractable form. An iterative algorithm is finally proposed to provide a sub-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm. We also uncover the influence of $\textbf{SC}^3$ parameters on closed-loop controls, highlighting more systematic understanding.

Figures (11)

• Figure 1: Illustration of an EIH-empowered $\textbf{SC}^3$ system, where the EIH is mounted on a UAV, and utilizes satellites to backhaul data.
• Figure 2: Illustration of three flows of the remote sensor data.
• Figure 3: Convergence performance of the proposed scheme.
• Figure 4: The LQR cost achieved with different transmit power constraints.
• Figure 5: The LQR cost achieved with different computing capability constraints.

Theorems & Definitions (15)

• Remark 1
• Lemma 1
• Proof
• Proposition 1
• Proof
• Remark 2
• Remark 3
• Lemma 2
• Proof
• Lemma 3