Resource allocation in wireless networks, such as device-to-device (D2D)
communications, is usually formulated as mixed integer nonlinear programming
(MINLP) problems, which are generally NP-hard and difficult to get the optimal
solutions. Traditional methods to solve these MINLP problems are all based on
mathematical optimization techniques, such as the branch-and-bound (B&B)
algorithm that converges slowly and has forbidding complexity for real-time
implementation. Therefore, machine leaning (ML) has been used recently to
address the MINLP problems in wireless communications. In this paper, we use
imitation learning method to accelerate the B&B algorithm. With invariant
problem-independent features and appropriate problem-dependent feature
selection for D2D communications, a good prune policy can be learned in a
supervised manner to speed up the most time-consuming branch process of the B&B
algorithm. Moreover, we develop a mixed training strategy to further reinforce
the generalization ability and a deep neural network with a novel loss function
to achieve better dynamic control over optimality and computational complexity.
Extensive simulation demonstrates that the proposed method can achieve good
optimality and reduce computational complexity simultaneously.