用于云RAN中并行PHY层处理的动态嵌套聚类
Dynamic Nested Clustering for Parallel PHY-Layer Processing in Cloud-RANs
摘要：
云无线接入网（C-RAN）以集中处理和基于云的基础设施为特征，是在未来无线蜂窝网络中实现前所未有的系统容量的有前途的解决方案。巨大的容量增益主要来自云服务器上的集中和协调的信号处理。然而，大规模C-RAN中的全面协调需要处理非常大的信道矩阵，导致高计算复杂性和信道估计开销。为了解决这一挑战，我们利用大型C-RAN信道矩阵的近稀疏性，并推导出一个统一的理论框架，集群和并行处理。基于该框架，提出了一种动态嵌套聚类（DNC）算法，该算法不仅大大提高了系统在基带处理和信道估计复杂度方面的可扩展性，而且适用于不同数据中心架构的各种并行处理策略。与所提出的算法，我们表明，最佳线性检测器的计算时间大大减少从$O(N^3)$不高于$O(N^{\frac{42}{23}})$，其中$N$是在C-RAN的RRH的数量。
英文摘要：
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.
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