Wireless D2d Network Link Scheduling Based On Graph Embedding

Wireless link scheduling in D2D communication systems aims at maximizing the weighted sum rate of D2D pairs by determining which subset of D2D pairs should be activated. However, it is a non-convex combinatorial optimization problem, which is generally NP-hard and difficult to achieve the optimal solution. Inspired by the recent attempt of introducing machine learning and graph embedding to reach the general goal, we propose an efficient method to solve the weighted sum rate maximization problem. We first model the system as a one-nearest neighbor graph, in which each D2D pair is a node and the strongest interference link for each node is an edge. Then we compute the feature vectors of both weights and nodes by graph embedding and use the feature vectors as the input of a subsequent multi-layer classifier. The parameters of classifier and graph embedding are trained jointly in a supervised manner. Simulation result shows that the proposed method can obtain near-optimal performance with only hundreds of training samples and is capable to be generalized to more complicated scenarios.