The application provides a multi-service joint downlink
resource allocation method, and belongs to the field of mobile communication; specifically, first, a communication scene including a
base station and users is built, and the distance from all users to the
base station is recorded in each time slot; then, the transmission model of eMBB users and URLLC users is quantitatively given, the maximum
transmission rate of the users is respectively calculated, a neural
network model is constructed, and a connection prediction matrix is output; an optimization target is established based on the high reliability requirement of the URLLC service and the
high transmission rate requirement of the eMBB service, and a comprehensive decision is made in combination with the connection prediction matrix; finally, a
reinforcement learning resource scheduling model is trained based on a Dueling Deep Q-Learning
algorithm, the optimization target function is solved, and thus the optimal
selection strategy of the URLLC and eMBB service
preemption or shared resources is given, and a multi-service joint downlink
resource allocation result is obtained; the application fully considers the coexistence problem of the actual communication scene of multi-
cell and multi-service, efficiently utilizes the
frequency domain resources, and has the significance of meeting the multi-service
transmission index.