User-centric C-RAN Architecture for Ultra-dense 5G Networks: Challenges and Methodologies

TL;DR

This paper explores a user-centric C-RAN architecture for ultra-dense 5G networks, addressing interference challenges with partial CSI and proposing solutions involving cloud computing and big data techniques.

Contribution

It introduces a dense user-centric C-RAN framework for UDN, focusing on partial CSI utilization and robust intra-cluster channel estimation methods.

Findings

Partial CSI can effectively support cooperative transmission.

Centralized processing enables efficient interference management.

Big data techniques are promising for future dense C-RAN deployment.

Abstract

Ultra-dense networks (UDN) constitute one of the most promising techniques of supporting the 5G mobile system. By deploying more small cells in a fixed area, the average distance between users and access points can be significantly reduced, hence a dense spatial frequency reuse can be exploited. However, severe interference is the major obstacle in UDN. Most of the contributions deal with the interference by relying on cooperative game theory. This paper advocates the application of dense user-centric C-RAN philosophy to UDN, thanks to the recent development of cloud computing techniques. Under dense C-RAN, centralized signal processing can be invoked for supporting CoMP transmission. We summarize the main challenges in dense user-centric C-RANs. One of the most challenging issues is the requirement of the global CSI for the sake of cooperative transmission. We investigate this requirement by only relying on partial CSI, namely, on inter-cluster large-scale CSI. Furthermore, the estimation of the intra-cluster CSI is considered, including the pilot allocation and robust transmission. Finally, we highlight several promising research directions to make the dense user-centric C-RAN become a reality, with special emphasis on the application of the `big data' techniques.