A Convolutional Neural Network based Resource Management Algorithm for NOMA enhanced D2D and Cellular Hybrid Networks

This paper mainly studies the channel and power allocation for the device-to-device (D2D) and cellular hybrid network with non-orthogonal multiple access (NOMA) technology. We formulate the joint channel and power allocation problem as a mixed integer programming problem (MIP). Since the MIP is non-convex and NP-hard, the computational complexity of the traditional optimization method is very high. To overcome this drawback, we construct a convolutional neural network (CNN) to approximate traditional optimization methods. Specifically, the inputs of the CNN are the channel state information of users, and the outputs are the channel allocation and power control policies. The relation between the inputs and the outputs is established by a hidden layer, which consists of a convolutional layer, a pooling layer, and a fully connected layer. The simulation results indicate that the CNN based resource allocation scheme can achieve a good performance with a ultra-low computational complexity.