Machine Learning-Based Resource Optimization for D2D Communication Underlaying Networks

Deploying device-to-device (D2D) communication over underlaying cellular network can significantly enhance the spectrum utilization. However, co-channel interference will occur when D2D pairs share the same radio resource with cellular users. To mitigate the interference within a reasonable range, a machine learning based resource reuse scheme for D2D and cellular users is proposed in this paper. Specifically, we formulate an optimization problem to maximize the total throughput of D2D pairs and cellular users by optimally allocating subcarrier and power within the limits of the interference threshold. Since the formulated problem is a mixed integer non-linear programming problem, we solve it in two steps. First, we assign the orthogonal subcarriers to different cellular users to maximize the total throughput of all cellular users. Then, D2D pairs are allowed to reuse different subcarriers to further enhance the throughput without affecting the performance of cellular users. The second step is still NP-hard and therefore we propose a low-complexity algorithm based on the pointer network, a specific neural network structure proposed recently. Results reveal that, with remarkably reduced complexity, the proposed scheme outperforms the conventional resource allocation algorithms.