# NetMind: Adaptive RAN Baseband Function Placement by GCN Encoding and Maze-solving DRL

arXiv: https://arxiv.org/abs/2401.06722 · HTML: https://mlsanity.com/paper/2401.06722/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2024.

## Built on by (most cited first, 2 of 2)

- [Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions](https://mlsanity.com/paper/2410.18793/) (2024)
- [Towards Scalable O-RAN Resource Management: Graph-Augmented Proximal Policy Optimization](https://mlsanity.com/paper/2509.10499/) (2025)

## Abstract (arXiv)

The disaggregated and hierarchical architecture of advanced RAN presents significant challenges in efficiently placing baseband functions and user plane functions in conjunction with Multi-Access Edge Computing (MEC) to accommodate diverse 5G services. Therefore, this paper proposes a novel approach NetMind, which leverages Deep Reinforcement Learning (DRL) to determine the function placement strategies in RANs with diverse topologies, aiming at minimizing power consumption. NetMind formulates the function placement problem as a maze-solving task, enabling a Markov Decision Process with standardized action space scales across different networks. Additionally, a Graph Convolutional Network (GCN) based encoding mechanism is introduced, allowing features from different networks to be aggregated into a single RL agent. That facilitates the RL agent's generalization capability and minimizes the negative impact of retraining on power consumption. In an example with three sub-networks, NetMind achieves comparable performance to traditional methods that require a dedicated DRL agent for each network, resulting in a 70% reduction in training costs. Furthermore, it demonstrates a substantial 32.76% improvement in power savings and a 41.67% increase in service stability compared to benchmarks from the existing literature.