NetMind+: Adaptive Baseband Function Placement With GCN Encoding and Incremental Maze-Solving DRL for Dynamic and Heterogeneous RANs

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%.