Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Control-Aware Transmit Power Allocation for 6G In-Factory Subnetwork Control Systems

Daniel Abode, Pedro Maia de Sant Ana, Alexander Artemenko, Ramoni Adeogun, Gilberto Berardinelli

TL;DR

This work tackles interference-limited transmit power control for dense in-factory subnetworks coordinating closed-loop plants. It introduces a channel-independent control-aware power policy (CICA) that maps a stability metric to transmit power through a logistic function, with parameters learned by Bayesian optimization using MOTPE to balance average and worst-case LQR costs. The approach avoids channel-gain measurements, enabling fully distributed deployment, and demonstrates through extensive simulations that CICA achieves competitive stability and availability under limited radio resources, outperforming rate-focused baselines. The results highlight the practical potential of control-driven interference management for scalable 6G in-factory networks and point to future work on density sensitivity and sub-band allocation.

Abstract

In this paper, we develop a novel power control solution for subnetworks-enabled distributed control systems in factory settings. We propose a channel-independent control-aware (CICA) policy based on the logistic model and learn the parameters using Bayesian optimization with a multi-objective tree-structured Parzen estimator. The objective is to minimize the control cost of the plants, measured as a finite horizon linear quadratic regulator cost. The proposed policy can be executed in a fully distributed manner and does not require cumbersome measurement of channel gain information, hence it is scalable for large-scale deployment of subnetworks for distributed control applications. With extensive numerical simulation and considering different densities of subnetworks, we show that the proposed method can achieve competitive stability performance and high availability for large-scale distributed control plants with limited radio resources.

Control-Aware Transmit Power Allocation for 6G In-Factory Subnetwork Control Systems

TL;DR

This work tackles interference-limited transmit power control for dense in-factory subnetworks coordinating closed-loop plants. It introduces a channel-independent control-aware power policy (CICA) that maps a stability metric to transmit power through a logistic function, with parameters learned by Bayesian optimization using MOTPE to balance average and worst-case LQR costs. The approach avoids channel-gain measurements, enabling fully distributed deployment, and demonstrates through extensive simulations that CICA achieves competitive stability and availability under limited radio resources, outperforming rate-focused baselines. The results highlight the practical potential of control-driven interference management for scalable 6G in-factory networks and point to future work on density sensitivity and sub-band allocation.

Abstract

In this paper, we develop a novel power control solution for subnetworks-enabled distributed control systems in factory settings. We propose a channel-independent control-aware (CICA) policy based on the logistic model and learn the parameters using Bayesian optimization with a multi-objective tree-structured Parzen estimator. The objective is to minimize the control cost of the plants, measured as a finite horizon linear quadratic regulator cost. The proposed policy can be executed in a fully distributed manner and does not require cumbersome measurement of channel gain information, hence it is scalable for large-scale deployment of subnetworks for distributed control applications. With extensive numerical simulation and considering different densities of subnetworks, we show that the proposed method can achieve competitive stability performance and high availability for large-scale distributed control plants with limited radio resources.
Paper Structure (16 sections, 10 equations, 3 figures, 2 tables, 1 algorithm)

This paper contains 16 sections, 10 equations, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. In-Factory Subnetwork Control System Model
  3. Control Aware Transmit Power Allocation
  4. CICA power allocation
  5. Bayesian Optimization
  6. Proposed Solution
  7. Results and Discussion
  8. Simulation Settings
  9. Subnetwork Deployment
  10. Control System Specification
  11. Training Specification
  12. Benchmark schemes
  13. Performance Evaluation
  14. Stability of the Control Plants
  15. Failure Rate
  16. ...and 1 more sections

Figures (3)

  • Figure 1: In-Factory Subnetwork Control Systems (InF-SCS) supporting closed-loop control of distributed plants.
  • Figure 2: Trained policy $k^* = 0.12$, $\eta^*_0 = 56$
  • Figure 3: Complementary cumulative distribution function (CCDF) of the cart position error and pole angle error