User-centric Performance Optimization with Remote Radio Head Cooperation in C-RAN

TL;DR

This paper proposes a user-centric optimization algorithm for C-RAN that enhances user equipment capacity by jointly optimizing RRH selection and resource allocation, enabling better network performance evaluation.

Contribution

It introduces a novel joint optimization method for RRH selection and resource allocation in C-RAN, with analysis of computational complexity and an algorithm for capacity maximization.

Findings

The algorithm effectively scales capacity for target UEs.

Analysis shows the problem's computational complexity.

Performance evaluation demonstrates improved network capacity.

Abstract

In a cloud radio access network (C-RAN), distributed remote radio heads (RRHs) are coordinated by baseband units (BBUs) in the cloud. The centralization of signal processing provides flexibility for coordinated multi-point transmission (CoMP) of RRHs to cooperatively serve user equipments (UEs). We target enhancing UEs' capacity performance, by jointly optimizing the selection of RRHs for serving UEs, i.e., resource allocation (and CoMP selection). We analyze the computational complexity of the problem. Next, we prove that under fixed CoMP selection, the optimal resource allocation amounts to solving a so-called iterated function. Towards user-centric network optimization, we propose an algorithm for the joint optimization problem, aiming at maximumly scaling up the capacity for any target UE group of interest. The proposed algorithm enables network-level performance evaluation for quality of experience.