Graph-based Framework for Flexible Baseband Function Splitting and Placement in C-RAN

TL;DR

This paper introduces a graph-based framework for optimizing the splitting and placement of baseband functions in C-RAN to reduce fronthaul costs, using a genetic algorithm for solution optimization.

Contribution

It presents a novel graph-based approach to model and solve the baseband function placement problem in C-RAN, improving fronthaul efficiency.

Findings

Proper splitting reduces fronthaul costs significantly.

Cooperative processing increases likelihood of centralized placement.

Trade-off observed between fronthaul cost reduction and computational complexity.

Abstract

The baseband-up centralization architecture of radio access networks (C-RAN) has recently been proposed to support efficient cooperative communications and reduce deployment and operational costs. However, the massive fronthaul bandwidth required to aggregate baseband samples from remote radio heads (RRHs) to the central office incurs huge fronthauling cost, and existing baseband compression algorithms can hardly solve this issue. In this paper, we propose a graphbased framework to effectively reduce fronthauling cost through properly splitting and placing baseband processing functions in the network. Baseband transceiver structures are represented with directed graphs, in which nodes correspond to baseband functions, and edges to the information flows between functions. By mapping graph weighs to computational and fronthauling costs, we transform the problem of finding the optimum location to place some baseband functions into the problem of finding the optimum clustering scheme for graph nodes. We then solve this problem using a genetic algorithm with customized fitness function and mutation module. Simulation results show that proper splitting and placement schemes can significantly reduce fronthauling cost at the expense of increased computational cost. We also find that cooperative processing structures and stringent delay requirements will increase the possibility of centralized placement.