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This paper focuses on optimal transmit power allocation to maximize theoverall system throughput in a vehicle-to-everything (V2X) communicationsystem. We propose two methods for solving the power allocation problem namelythe weighted minimum mean square error (WMMSE) algorithm and the deeplearning-based method. In the WMMSE algorithm, we solve the problem using blockcoordinate descent (BCD) method. Then we adopt supervised learning techniquefor the deep neural network (DNN) based approach considering the powerallocation from the WMMSE algorithm as the target output. We exploit anefficient implementation of the mini-batch gradient descent algorithm fortraining the DNN. Extensive simulation results demonstrate that the DNNalgorithm can provide very good approximation of the iterative WMMSE algorithmreducing the computational overhead significantly.

Deep Neural Network Based Resource Allocation for V2X Communications