Dynamic Nested Clustering for Parallel PHY-Layer Processing in Cloud-RANs

TL;DR

This paper introduces a dynamic nested clustering algorithm for Cloud-RANs that significantly reduces computational complexity and enhances scalability by exploiting channel matrix sparsity and enabling parallel processing.

Contribution

It proposes a novel dynamic nested clustering framework that improves scalability and reduces processing complexity in large-scale C-RANs, adaptable to various data center architectures.

Findings

Reduces optimal linear detector computation from O(N^3) to O(N^{42/23})

Provides a unified theoretical framework for clustering and parallel processing

Enhances system scalability and reduces channel estimation overhead

Abstract

Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieve an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly comes from the centralized and coordinated signal processing at the cloud server. However, full-scale coordination in a large-scale C-RAN requires the processing of very large channel matrices, leading to high computational complexity and channel estimation overhead. To resolve this challenge, we exploit the near-sparsity of large C-RAN channel matrices, and derive a unified theoretical framework for clustering and parallel processing. Based on the framework, we propose a dynamic nested clustering (DNC) algorithm that not only greatly improves the system scalability in terms of baseband-processing and channel-estimation complexity, but also is amenable to various parallel processing strategies for different data center architectures. With the proposed algorithm, we show that the computation time for the optimal linear detector is greatly reduced from $O(N^3)$ to no higher than $O(N^{\frac{42}{23}})$, where $N$ is the number of RRHs in C-RAN.