Throughput optimization in heterogeneous MIMO networks: a GNN-based approach

With the development of 5G and IoT networks, Device-to-Device (D2D) communication has become a major paradigm in wireless communication. Most existing approaches for D2D resource allocation are usually time consuming and demand a high computational budget, especially in heterogeneous deployments where the D2D links have different configurations (i.e., different number of transmit and receive antennas). Recently, Graph neural networks (GNNs) have been proposed to solve many problems in the networking domain and have significantly outperformed traditional algorithms, including throughput optimization problems in D2D networks. However, existing throughput optimization works either only apply to MISO or SISO D2D networks or require extremely long runtime on MIMO D2D networks, which makes it hard to apply them in real-world D2D applications. In this paper, we consider the throughput prediction problem across a fixed association of transmitters and receivers to maximize the total throughput in heterogeneous MIMO D2D networks. We model the interference between different link types as heterogeneous edges and learn the optimal beamforming policy using a heterogeneous GNN. Simulation results show that our proposed GNN-based approach achieves a significant speedup compared with the state-of-the-art algorithm, while providing robust performance on large-scale synthetic datasets.