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 for uplink signal detection. This algorithm allows flexible trade-offs between system performance and other critical parameters, such as computational complexity and channel estimation overhead. Moreover, the algorithm is amenable to parallel processing, and various parallel implementations are discussed for different types of data center architectures. With the proposed algorithm, we show that the complexity scalability can be greatly improved.

Angela Yingjun Zhang教授简介：
Angela Yingjun Zhang received the B.Eng. degree in electronic engineering from Fudan University, Shanghai, China, in 2000, and the Ph.D. degree in electrical and electronic en- gineering from the Hong Kong University of Science and Technology, Hong Kong, in 2004. Since 2005, she has been with the Department of Information Engineering, The Chinese University of Hong Kong, where she is currently an Associate Professor. Her current research interests are mainly focused on wireless communications systems and smart power systems, in particular optimization techniques for such systems.