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

TL;DR

This paper introduces a scalable, control-aware power control policy for factory subnetworks that minimizes control costs without needing channel gain measurements, enabling efficient large-scale distributed control.

Contribution

It presents a novel, fully distributed power control policy based on Bayesian optimization and logistic modeling, tailored for factory control subnetworks, improving scalability and resource efficiency.

Findings

Achieves competitive stability performance in simulations

Ensures high availability with limited radio resources

Scalable for large-scale deployment of control subnetworks

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.