  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.
