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Li, Haiyuan; Li, Peizheng; Assis, Karcuis; Ullauri, Juan Marcelo Parra; Aijaz, Adnan; Yan, Shuangyi; Simeonidou, Dimitra
NetMind+: Adaptive Baseband Function Placement with GCN Encoding and Incremental Maze-solving DRL for Dynamic and Heterogeneous RANs Journal Article
In: IEEE Transactions on Network and Service Management, vol. 22, no. 4, pp. 3419–3432, 2025, ISSN: 1932-4537.
Abstract | Links | BibTeX | Tags: Advanced RAN, deep reinforcement learning, graph neural network, Incremental learning, MEC, Topology variation
@article{li_netmind_2025,
title = {NetMind+: Adaptive Baseband Function Placement with GCN Encoding and Incremental Maze-solving DRL for Dynamic and Heterogeneous RANs},
author = {Haiyuan Li and Peizheng Li and Karcuis Assis and Juan Marcelo Parra Ullauri and Adnan Aijaz and Shuangyi Yan and Dimitra Simeonidou},
doi = {10.1109/TNSM.2025.3570490},
issn = {1932-4537},
year = {2025},
date = {2025-08-01},
journal = {IEEE Transactions on Network and Service Management},
volume = {22},
number = {4},
pages = {3419–3432},
abstract = {The disaggregated architecture of advanced Radio Access Networks (RANs) with diverse X-haul latencies, in conjunction with resource-limited multi-access edge computing networks, presents significant challenges in designing a general model in placing baseband and user plane functions to accommodate versatile 5G services. This paper proposes a novel approach, NetMind+, which leverages Deep Reinforcement Learning (DRL) to determine the function placement strategies in diverse and evolving RAN topologies, aiming at minimizing power consumption. NetMind+ resolves the problem with a maze-solving strategy, enabling a Markov Decision Process with standardized action space scales across different networks. Additionally, a Graph Convolutional Network (GCN) based encoding and an incremental learning mechanism are introduced, allowing features from different and dynamic networks to be aggregated into a single DRL agent. This facilitates the generalization capability of DRL and minimizes the negative retraining impact. In an example with three sub-networks, NetMind+ demonstrates a substantial 32.76% improvement in power savings and a 41.67% increase in service stability compared to benchmarks from the existing literature. Compared to traditional methods necessitating a dedicated DRL agent for each network, NetMind+ attains comparable performance with 70% of the training cost savings. Furthermore, it demonstrates robust adaptability during network variations, accelerating training speed by 50%.},
keywords = {Advanced RAN, deep reinforcement learning, graph neural network, Incremental learning, MEC, Topology variation},
pubstate = {published},
tppubtype = {article}
}
The disaggregated architecture of advanced Radio Access Networks (RANs) with diverse X-haul latencies, in conjunction with resource-limited multi-access edge computing networks, presents significant challenges in designing a general model in placing baseband and user plane functions to accommodate versatile 5G services. This paper proposes a novel approach, NetMind+, which leverages Deep Reinforcement Learning (DRL) to determine the function placement strategies in diverse and evolving RAN topologies, aiming at minimizing power consumption. NetMind+ resolves the problem with a maze-solving strategy, enabling a Markov Decision Process with standardized action space scales across different networks. Additionally, a Graph Convolutional Network (GCN) based encoding and an incremental learning mechanism are introduced, allowing features from different and dynamic networks to be aggregated into a single DRL agent. This facilitates the generalization capability of DRL and minimizes the negative retraining impact. In an example with three sub-networks, NetMind+ demonstrates a substantial 32.76% improvement in power savings and a 41.67% increase in service stability compared to benchmarks from the existing literature. Compared to traditional methods necessitating a dedicated DRL agent for each network, NetMind+ attains comparable performance with 70% of the training cost savings. Furthermore, it demonstrates robust adaptability during network variations, accelerating training speed by 50%.