Learning to Branch: Accelerating Resource Allocation in Wireless Networks

Understand

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.