Sensing-Communication-Computing-Control Closed-Loop Optimization for 6G Unmanned Robotic Systems

Fuente: arXiv
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Main Authors: Fang, Xinran, Lei, Chengleyang, Feng, Wei, Chen, Yunfei, Xiao, Ming, Ge, Ning, Wang, Chengxiang
Format: Preprint
Published: 2024
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author Fang, Xinran
Lei, Chengleyang
Feng, Wei
Chen, Yunfei
Xiao, Ming
Ge, Ning
Wang, Chengxiang
author_facet Fang, Xinran
Lei, Chengleyang
Feng, Wei
Chen, Yunfei
Xiao, Ming
Ge, Ning
Wang, Chengxiang
contents Rapid advancements in field robots have brought a new kind of cyber physical system (CPS)--unmanned robotic system--under the spotlight. In the upcoming sixth-generation (6G) era, these systems hold great potential to replace humans in hazardous tasks. This paper investigates an unmanned robotic system comprising a multi-functional unmanned aerial vehicle (UAV), sensors, and actuators. The UAV carries communication and computing modules, acting as an edge information hub (EIH) that transfers and processes information. During the task execution, the EIH gathers sensing data, calculates control commands, and transmits commands to actuators--leading to reflex-arc-like sensing-communication-computing-control ($\mathbf{SC}^3$) loops. Unlike existing studies that design $\mathbf{SC}^3$ loop components separately, we take each $\mathbf{SC}^3$ loop as an integrated structure and propose a goal-oriented closed-loop optimization scheme. This scheme jointly optimizes uplink and downlink (UL&DL) communication and computing within and across the $\mathbf{SC}^3$ loops to minimize the total linear quadratic regulator (LQR) cost. We derive optimal closed-form solutions for intra-loop allocation and propose an efficient iterative algorithm for inter-loop optimization. Under the condition of adequate CPU frequency availability, we derive an approximate closed-form solution for inter-loop bandwidth allocation. Simulation results demonstrate that the proposed scheme achieves a two-tier task-level balance within and across $\mathbf{SC}^3$ loops.
format Preprint
id arxiv_https___arxiv_org_abs_2410_18382
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Sensing-Communication-Computing-Control Closed-Loop Optimization for 6G Unmanned Robotic Systems
Fang, Xinran
Lei, Chengleyang
Feng, Wei
Chen, Yunfei
Xiao, Ming
Ge, Ning
Wang, Chengxiang
Systems and Control
Signal Processing
Rapid advancements in field robots have brought a new kind of cyber physical system (CPS)--unmanned robotic system--under the spotlight. In the upcoming sixth-generation (6G) era, these systems hold great potential to replace humans in hazardous tasks. This paper investigates an unmanned robotic system comprising a multi-functional unmanned aerial vehicle (UAV), sensors, and actuators. The UAV carries communication and computing modules, acting as an edge information hub (EIH) that transfers and processes information. During the task execution, the EIH gathers sensing data, calculates control commands, and transmits commands to actuators--leading to reflex-arc-like sensing-communication-computing-control ($\mathbf{SC}^3$) loops. Unlike existing studies that design $\mathbf{SC}^3$ loop components separately, we take each $\mathbf{SC}^3$ loop as an integrated structure and propose a goal-oriented closed-loop optimization scheme. This scheme jointly optimizes uplink and downlink (UL&DL) communication and computing within and across the $\mathbf{SC}^3$ loops to minimize the total linear quadratic regulator (LQR) cost. We derive optimal closed-form solutions for intra-loop allocation and propose an efficient iterative algorithm for inter-loop optimization. Under the condition of adequate CPU frequency availability, we derive an approximate closed-form solution for inter-loop bandwidth allocation. Simulation results demonstrate that the proposed scheme achieves a two-tier task-level balance within and across $\mathbf{SC}^3$ loops.
title Sensing-Communication-Computing-Control Closed-Loop Optimization for 6G Unmanned Robotic Systems
topic Systems and Control
Signal Processing
url https://arxiv.org/abs/2410.18382