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"... 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 a ..."
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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 computation time for the optimal linear detector can be reduced from O(N3) to no higher than O(N 42 23), where N is the number of RRHs in C-RAN.
Scalable Coordinated Uplink Processing in Cloud Radio Access Networks
"... 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 pro ..."
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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 show in this paper that the channel matrices can be greatly sparsified without substantially compromising the system capacity. Through rigorous analysis, we derive a simple threshold-based channel matrix sparsification approach. Based on this approach, for reasonably large networks, the non-zero entries in the channel matrix can be reduced to a very low percentage (say 0.13 % ∼ 2%) by compromising only 5 % of SINR. This means each RRH only needs to obtain the CSI of a small number of closest users, resulting in a significant reduction in the channel estimation overhead. On the other hand, the high sparsity of the channel matrix allows us to design detection algorithms that are scalable in the sense that the average computational complexity per user does not grow with the network size. I.
Convex Optimization for Joint Zero-forcing and Antenna Selection in Multiuser MISO Systems
"... Abstract—The problem of joint zero-forcing (ZF) beamforming (BF) together with optimal power allocation (PA) and antenna selection (AS) for throughput maximization is considered in this paper for multi-user multiple input single output (MU-MISO) systems. We introduce a new formulation for the joint ..."
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Abstract—The problem of joint zero-forcing (ZF) beamforming (BF) together with optimal power allocation (PA) and antenna selection (AS) for throughput maximization is considered in this paper for multi-user multiple input single output (MU-MISO) systems. We introduce a new formulation for the joint ZF and PA problem by adapting the algebraic subspace approach which finds a proper set for the optimization variable that inherently satisfies the ZF constraints. Also, the squared group Lasso penalty on the BF matrix is used to linearize (relax) the non-convex, NP-hard problem of joint BF and AS. Extensive simulations show that for the throughput problem, the proposed algorithm performs very closely to the optimal (exhaustive search) joint approach. Index Terms—Multiple input multiple output (MIMO), linear precoding, convex optimization, antenna selection, group Lasso. I.
