Hybrid Data-Sharing and Compression Strategy for Downlink Cloud Radio Access Network

TL;DR

This paper compares data-sharing and compression strategies for downlink cloud radio access networks, revealing their performance dependence on fronthaul capacity, and introduces a hybrid approach that optimally balances both for improved network efficiency.

Contribution

It introduces a novel hybrid data-sharing and compression strategy with an optimization framework, enhancing performance based on fronthaul capacity conditions.

Findings

Data-sharing outperforms at low capacity

Compression is better at moderate-to-high capacity

Hybrid strategy offers improved performance across capacities

Abstract

This paper studies transmission strategies for the downlink of a cloud radio access network, in which the base stations are connected to a centralized cloud-computing based processor with digital fronthaul or backhaul links. We provide a system-level performance comparison of two fundamentally different strategies, namely the data-sharing strategy and the compression strategy, that differ in the way the fronthaul/backhaul is utilized. It is observed that the performance of both strategies depends crucially on the available fronthaul or backhaul capacity. When the fronthaul/backhaul capacity is low, the datasharing strategy performs better, while under moderate-to-high fronthaul/backhaul capacity, the compression strategy is superior. Using insights from such a comparison, we propose a novel hybrid strategy, combining the data-sharing and compression strategies, that allows for better control over the fronthaul/backhaul capacity utilization. An optimization framework for the hybrid strategy is proposed. Numerical evidence demonstrates the performance gain of the hybrid strategy.