Scalable network adaptation for Cloud-RANs: An imitation learning approach

TL;DR

This paper introduces a machine learning approach to approximate the branch-and-bound algorithm for network adaptation in Cloud-RANs, achieving near-optimal solutions with reduced computational complexity.

Contribution

It proposes a novel imitation learning framework to emulate branch-and-bound pruning, scalable to larger problem sizes with minimal training data.

Findings

Significantly outperforms existing methods in simulations.

Reduces computational complexity compared to traditional algorithms.

Scalable to larger problem instances than training data.

Abstract

Network adaptation is essential for the efficient operation of Cloud-RANs. Unfortunately, it leads to highly intractable mixed-integer nonlinear programming problems. Existing solutions typically rely on convex relaxation, which yield performance gaps that are difficult to quantify. Meanwhile, global optimization algorithms such as branch-and-bound can find optimal solutions but with prohibitive computational complexity. In this paper, to obtain near-optimal solutions at affordable complexity, we propose to approximate the branch-and-bound algorithm via machine learning. Specifically, the pruning procedure in branch-and-bound is formulated as a sequential decision problem, followed by learning the oracle's action via imitation learning. A unique advantage of this framework is that the training process only requires a small dataset, and it is scalable to problem instances with larger dimensions than the training setting. This is achieved by identifying and leveraging the problem-size independent features. Numerical simulations demonstrate that the learning based framework significantly outperforms competing methods, with computational complexity much lower than the traditional branch-and-bound algorithm.