Group Sparse Beamforming for Green Cloud-RAN (1310.0234v1)

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.

• The paper introduces a novel group sparse beamforming framework that jointly optimizes RRH selection and power allocation to achieve up to 40% power savings.
• The paper employs two algorithms—the Bi-section and Iterative GSBF—to induce group sparsity and simplify the NP-hard joint optimization problem.
• The paper’s approach integrates transport network power constraints with beamforming design, paving the way for energy-efficient Cloud-RAN deployments.

Group Sparse Beamforming for Green Cloud-RAN

The paper "Group Sparse Beamforming for Green Cloud-RAN" by Yuanming Shi, Jun Zhang, and Khaled B. Letaief addresses a critical aspect in the design of Cloud Radio Access Networks (Cloud-RAN): minimizing network power consumption while maintaining quality of service (QoS) for users. The authors present a framework that jointly optimizes Remote Radio Head (RRH) selection and power allocation through group sparse beamforming.

Introduction and Context

Cloud-RAN emerges as a promising architecture to handle the burgeoning demand for mobile data. By centralizing baseband processing in a Baseband Unit (BBU) pool and utilizing low-cost RRHs, Cloud-RAN offers significant advantages in terms of resource allocation and interference management. However, with the necessary connectivity between RRHs and the BBU pool via optical transport links, the transport network's power consumption becomes a substantial factor in energy efficiency.

Problem Formulation

The authors formulate the optimization problem as a joint RRH selection and power minimization beamforming task. Recognizing the challenge, they describe this as a mixed-integer non-linear programming (MINLP) problem, acknowledged as NP-hard. The solution focus is on developing low-complexity algorithms that enable practical implementations.

Methodology and Algorithms

1. Greedy Selection Algorithm (GS):
   • A backward approach is used, iteratively switching off RRHs while optimizing the remaining active network's beamforming. The key is a selection rule maximizing the reduction in network power at each step. This approach, though computationally intensive, approximates a near-optimal solution effectively.
2. Group Sparse Beamforming Framework (GSBF):
   • A group-sparsity inducing method is employed, leveraging weighted $\ell_1/\ell_2$-norms. This innovation promotes group sparsity, supporting the switching-off of entire RRHs more effectively compared to individual coefficient sparsity.
   • Two main algorithms within this framework:
     • Bi-section GSBF Algorithm: Uses weighted mixed norms to enhance sparsity and conducts a binary search to identify the optimal active RRH set.
     • Iterative GSBF Algorithm: Refines the solution through repeated re-weighting based on system parameters and signal characteristics, offering improved performance especially in medium-sized networks.

Results and Discussions

Simulation results detailed in the paper show a marked reduction in network power consumption across a variety of scenarios. The performance improvement is especially notable in the low QoS regime, yielding a power reduction of up to 40%. When transport link power consumption is significant, these benefits become even more pronounced, confirming the necessity of considering these factors.

Implications and Future Directions

The findings have profound implications for the design and deployment of Cloud-RANs. By integrating transport network considerations into RRH selection and beamforming, the authors highlight a pathway toward energy-efficient cellular networks.

Future research may explore scalable algorithms for even larger networks, further integration of transport link capacity constraints, and more effective user scheduling strategies. Moreover, as Cloud-RAN technology evolves, the potential for enhanced virtualization and real-time processing improvements could provide additional avenues for optimization.

Overall, the methodologies and insights in this paper contribute significantly to the theoretical and practical understanding of energy efficiency in next-generation network architectures.