The invention discloses a spectrum
resource management method based on deep
reinforcement learning, and mainly aims to solve the problem that incomplete
channel state information cannot be effectivelyutilized for spectrum and power allocation and spectrum
resource management multitarget optimization in the prior art. According to the implementation scheme, the method comprises the following steps: constructing an adaptive deep
neutral network which takes
channel gain and
noise power as weight parameters by taking spectrum efficiency maximization as an optimization target; and initializing theweight parameters, observing user access information and interference information, calculating a
loss function according to the energy efficiency and fairness of a communication network, updating thechannel
gain and the
noise power layer after layer along the
gradient descent direction of the
loss function, repeatedly training the adaptive deep neural network, and outputting an optimal spectrumresource
management strategy when a training end condition is satisfied. Through adoption of the spectrum
resource management method, the optimal spectrum resource
management strategy can be obtainedon the basis of the incomplete
channel state information, and the spectrum efficiency, energy efficiency and fairness of the communication network are improved effectively. The spectrum resource management method can be applied to spectrum and power allocation in
wireless communication.