## 3 Graph Neural Networks for Next-Generation Networks
### GNN in Access Networks
#### Connection Management  
Playing a crucial role in resource management within access networks, connection management aims to achieve smooth, balanced, and fair throughput. Conventional approaches \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Model** & **Baseline** & **Objective** & **Metric** \\ \hline
[57] & DRL, GNN & Default & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[58] & DRL, GCN & Random, CNN+DRL, PG & Data rate maximization &
\begin{tabular}{} \end{tabular} \\ \hline
[59] & DQN, GAT, A2C & DQN, A2C, Hard slicing & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[60] & GCN, Spectral clustering & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[61] & GCN, DNN & Default & Sum rate maximization & Accuracy, average sum rate \\ \hline
[62] & GCN, MWIS & Local greedy solver & Delay minimization & Accuracy, average sum rate \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of application of GNN-based models for resource allocation in access networks. Figure 3: A simplified illustration of a Message-passing neural network (MPNN) on an undirected graph where we aggregate neighbor features by “mixing” them and updating the target node (the red one) using concatenation. to connection management, particularly user-cell association, often rely on sub-optimal and greedy solutions, such as connecting each user to the cell with the highest received signal strength. However, the performance of networks can be enhanced by harnessing the potential of Machine learning (ML)-based solutions. The advent of the next-generation software-defined 5G networks, as defined by the Open Radio Access Network (O-RAN) alliance, facilitates the integration of ML/AI-based techniques to address various challenges in access networks. In this context, [PERSON] et al. [57] propose a novel approach to handle user association and optimize load balancing on top of the O-RAN network architecture using GNNs. The RAN infrastructure and its connections to User equipments (UEs) are conceptualized as a graph of nodes representing radio cells and UEs, and edges encompassing (i) connections between adjacent cells and (ii) association between each UE and its associated cell. GNNs are then proposed as a data-driven method to comprehend and extract features from the underlying graph structure to the end of optimizing connection management based on three quantitive metrics. Accordingly, the optimization involves the joint maximization of the sum of UE throughput, coverage, and the load balancing index. Similarly, [PERSON] et al. [58] leverage GNNs to address the resource management in the Radio access network (RAN). Their focus is primarily on the cognitive radio context, where unlicensed secondary users (SUs) can occasionally access the idle resources of licensed primary users (PUs). In line with the previously introduced work [57], they represent the underlay cognitive radio network as a graph, with vertices representing UE-cell links and edges denoting the interference between them. The radio resource management is then formulated as a decision-making problem where an actor proposes jointly the channel and power allocation while ensuring the constrained proportion of resources between PUs and SUs to maximize the data rate for all users. To achieve this, the authors employ GNNs to exploit the interference information embedded in the constructed graph for the generation of allocation policies. Additionally, they consider the modeling of the users' mobility, resulting in a dynamic graph with a continuous decision-making mechanism. Consequently, the authors adopt a Deep reinforcement learning (DRL) framework for model learning. The simulation results demonstrate the feasibility and convergence of the proposed scheme, showing a significant performance improvement compared to existing approaches.