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Group Sparse Beamforming for Green Cloud-RAN
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In this paper, the authors proposed a new framework to design a green cloud radio access network, which is formulated as a joint RRH selection and power minimization beamforming problem, and the proposed algorithms significantly reduce the network power consumption and demonstrate the importance of considering the transport link power consumption.Abstract:
A cloud radio access network (Cloud-RAN) is a network architecture that holds the promise of meeting the explosive growth of mobile data traffic. In this architecture, all the baseband signal processing is shifted to a single baseband unit (BBU) pool, which enables efficient resource allocation and interference management. Meanwhile, conventional powerful base stations can be replaced by low-cost low-power remote radio heads (RRHs), producing a green and low-cost infrastructure. However, as all the RRHs need to be connected to the BBU pool through optical transport links, the transport network power consumption becomes significant. In this paper, we propose a new framework to design a green Cloud-RAN, which is formulated as a joint RRH selection and power minimization beamforming problem. To efficiently solve this problem, we first propose a greedy selection algorithm, which is shown to provide near- optimal performance. To further reduce the complexity, a novel group sparse beamforming method is proposed by inducing the group-sparsity of beamformers using the weighted $\ell_1/\ell_2$-norm minimization, where the group sparsity pattern indicates those RRHs that can be switched off. Simulation results will show that the proposed algorithms significantly reduce the network power consumption and demonstrate the importance of considering the transport link power consumption.read more
Citations
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Alternating Minimization Algorithms for Hybrid Precoding in Millimeter Wave MIMO Systems
TL;DR: Treating the hybrid precoder design as a matrix factorization problem, effective alternating minimization (AltMin) algorithms will be proposed for two different hybrid precoding structures, i.e., the fully-connected and partially-connected structures, and simulation comparisons between the two hybrid precode structures will provide valuable design insights.
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Fronthaul-constrained cloud radio access networks: insights and challenges
TL;DR: This article comprehensively surveys recent advances in fronthaul-constrained CRANs, including system architectures and key techniques, including compression and quantization, large-scale coordinated processing and clustering, and resource allocation optimization.
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Heterogeneous Cloud Radio Access Networks: A New Perspective for Enhancing Spectral and Energy Efficiencies
TL;DR: In this article, state-of-the-art research achievements and challenges on heterogeneous cloud radio access networks (H-CRANs) are surveyed, in particular, issues of system architectures, spectral and energy efficiency performances, and promising key techniques.
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Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
TL;DR: In this article, the authors comprehensively survey the recent advances of C-RANs, including system architectures, key techniques, and open issues, and discuss the system architectures with different functional splits and the corresponding characteristics.
References
More filters
Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Book
Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Journal ArticleDOI
Model selection and estimation in regression with grouped variables
Ming Yuan,Yi Lin +1 more
TL;DR: In this paper, instead of selecting factors by stepwise backward elimination, the authors focus on the accuracy of estimation and consider extensions of the lasso, the LARS algorithm and the non-negative garrotte for factor selection.
Journal ArticleDOI
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: If the objects of interest are sparse in a fixed basis or compressible, then it is possible to reconstruct f to within very high accuracy from a small number of random measurements by solving a simple linear program.