Large-Scale Convex Optimization for Ultra-Dense Cloud-RAN

TL;DR

This paper explores the application of convex optimization techniques to address large-scale design challenges in ultra-dense Cloud-RAN networks, focusing on power minimization and channel estimation.

Contribution

It introduces a two-stage framework for solving large-scale convex optimization problems in Cloud-RAN, emphasizing structure exploitation and parallel implementation.

Findings

Convex optimization effectively addresses large-scale Cloud-RAN challenges.

The proposed framework enables scalable and parallel solutions.

Application to power and channel estimation demonstrates practical benefits.

Abstract

The heterogeneous cloud radio access network (Cloud-RAN) provides a revolutionary way to densify radio access networks. It enables centralized coordination and signal processing for efficient interference management and flexible network adaptation. Thus, it can resolve the main challenges for next-generation wireless networks, including higher energy efficiency and spectral efficiency, higher cost efficiency, scalable connectivity, and low latency. In this article, we shall provide an algorithmic thinking on the new design challenges for the dense heterogeneous Cloud-RAN based on convex optimization. As problem sizes scale up with the network size, we will demonstrate that it is critical to take unique structures of design problems and inherent characteristics of wireless channels into consideration, while convex optimization will serve as a powerful tool for such purposes. Network power minimization and channel state information acquisition will be used as two typical examples to demonstrate the effectiveness of convex optimization methods. We will then present a two-stage framework to solve general large-scale convex optimization problems, which is amenable to parallel implementation in the cloud data center.