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

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 42 23 ), where N is the number of RRHs in C-RAN.

Scalable Uplink Processing via Sparse Message Passing in C-RAN

“…Decreasing the degree of nodes in the factor graph is equivalent to sparsifying the channel matrix. As proven in our recent work [8], the channel matrix can be significantly sparsified, with compromising a very small percentage of SINR loss. In [8], the entries of H is discarded based on the distance between RRHs and users.…”

“…As proven in our recent work [8], the channel matrix can be significantly sparsified, with compromising a very small percentage of SINR loss. In [8], the entries of H is discarded based on the distance between RRHs and users. Specifically, the (n, k)-th entry in the resulting sparisified channel matrix H is given by…”

“…However, most of existing work considers systems that are much smaller than C-RANs, and thus scalability is not a concern. Recent efforts to reduce the computational complexity of multi-user joint processing in C-RAN 978-1-4799-5952-5/15/$31.00 ©2015 IEEE can be found in [7] and [8]. In particular, [7] proposes a lowcomplexity beamforming algorithm based on the alternative direction method of multiplies (ADMM).…”

“…In particular, [7] proposes a lowcomplexity beamforming algorithm based on the alternative direction method of multiplies (ADMM). Our previous work in [8] proposes a dynamic clustering algorithm with parallel implementation to significantly improve the scalability of the system. However, neither of the algorithms is fully scalable.…”

Fronthaul-Limited Uplink OFDMA in Ultra-Dense CRAN With Hybrid Decoding

“…The maximization problem in (16) can be decomposed into N parallel sub-problems using (14), where each sub-problem corresponds to a single SC n, and has the following structure,…”