  This paper focuses on optimal transmit power allocation to maximize the
overall system throughput in a vehicle-to-everything (V2X) communication
system. We propose two methods for solving the power allocation problem namely
the weighted minimum mean square error (WMMSE) algorithm and the deep
learning-based method. In the WMMSE algorithm, we solve the problem using block
coordinate descent (BCD) method. Then we adopt supervised learning technique
for the deep neural network (DNN) based approach considering the power
allocation from the WMMSE algorithm as the target output. We exploit an
efficient implementation of the mini-batch gradient descent algorithm for
training the DNN. Extensive simulation results demonstrate that the DNN
algorithm can provide very good approximation of the iterative WMMSE algorithm
reducing the computational overhead significantly.
