The invention relates to a heterogeneous cloud
wireless access network resource allocation method based on deep
reinforcement learning, and belongs to the technical field of mobile communication. Themethod comprises the following steps: 1) taking
queue stability as a constraint, combining congestion control, user association,
subcarrier allocation and power allocation, and establishing a
random optimization model for maximizing the total
throughput of the network; 2) considering the complexity of the scheduling problem, the
state space and the action space of the
system are high-dimensional,and the DRL
algorithm uses a neural network as a
nonlinear approximation function to efficiently solve the problem of dimensionality disasters; and 3) aiming at the complexity and the dynamic variability of the
wireless network environment, introducing a transfer learning
algorithm, and utilizing the
small sample learning characteristics of transfer learning to enable the DRL
algorithm to obtain an optimal
resource allocation strategy under the condition of a small number of samples. According to the method, the total
throughput of the whole network can be maximized, and meanwhile, the requirement of service
queue stability is met. And the method has a very high application value in a mobile communication
system.