关键词: 终端直通通信, 干扰协调, 图卷积网络, 功率控制, 分布式算法

Abstract: In order to effectively control the co-channel interference in a Device-to-Device (D2D) communication system while reducing the implementation complexity, a graph convolutional network (GCN)-based distributed power allocation algorithm was proposed to maximize the sum weighted bit rate of all D2D links. To achieve the above goals, the system topology was first built into a graph model and the characteristics of nodes and edges as well as the message-passing manners were further defined. Then, the unsupervised learning model was leveraged to train the model parameters of the formulated GCN. After the offline training, each D2D link was able to obtain the optimal power allocation strategy in a distributed manner based on local channel state information and the interaction with neighboring nodes. The experimental results showed that compared with the optimization theory-based algorithm, the proposed algorithm substantially cut down the operation time by 97.41% while suffering only 3.409% performance loss, and compared with the deep reinforcement learning theory-based algorithm, the proposed algorithm exhibited better generalization and stability under different settings of parameters.

Key words: Device-to-Device (D2D) communication, interference coordination, Graph Convolutional Network (GCN), power allocation, distributed algorithms