# Learning Power Control Protocol for In-Factory 6G Subnetworks

arXiv: https://arxiv.org/abs/2505.05967 · HTML: https://mlsanity.com/paper/2505.05967/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 9 May 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 8 of 8)

- [Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Access](https://mlsanity.com/paper/2007.09948/) (2020)
- [The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.07144/) (2021)
- [Learning Generalized Wireless MAC Communication Protocols via Abstraction](https://mlsanity.com/paper/2206.06331/) (2022)
- [Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach](https://mlsanity.com/paper/2212.14051/) (2022)
- [ML Framework for Wireless MAC Protocol Design](https://mlsanity.com/paper/2402.07208/) (2024)
- [Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless Systems](https://mlsanity.com/paper/2306.11865/) (2023)
- [Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things](https://mlsanity.com/paper/2401.12914/) (2024)
- [Federated Multi-Agent DRL for Radio Resource Management in Industrial 6G in-X subnetworks](https://mlsanity.com/paper/2406.07383/) (2024)

## Abstract (arXiv)

In-X Subnetworks are envisioned to meet the stringent demands of short-range communication in diverse 6G use cases. In the context of In-Factory scenarios, effective power control is critical to mitigating the impact of interference resulting from potentially high subnetwork density. Existing approaches to power control in this domain have predominantly emphasized the data plane, often overlooking the impact of signaling overhead. Furthermore, prior work has typically adopted a network-centric perspective, relying on the assumption of complete and up-to-date channel state information (CSI) being readily available at the central controller. This paper introduces a novel multi-agent reinforcement learning (MARL) framework designed to enable access points to autonomously learn both signaling and power control protocols in an In-Factory Subnetwork environment. By formulating the problem as a partially observable Markov decision process (POMDP) and leveraging multi-agent proximal policy optimization (MAPPO), the proposed approach achieves significant advantages. The simulation results demonstrate that the learning-based method reduces signaling overhead by a factor of 8 while maintaining a buffer flush rate that lags the ideal "Genie" approach by only 5%.