  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.
