Group Sparse Beamforming for Green Cloud-RAN

By Yuanming Shi, Jun Zhang, Khaled Ben Letaief. IEEE Transactions on Wireless Communications, 2013

482 citations recorded by the source corpus.

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 ℓ1/ℓ2-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.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/691e6db087c457c33f685cb050cf690afd8efa68

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Citations in the corpus

Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “Problems of sparse optimization and low-rank optimization occur frequently in ML and signal processing [165], [166], [167], [168], whose”
Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network (2014, background): “For instance, under the signal-to-interference-and-noise ratio (SINR) constraints at the receivers, [17] considers backhaul minimization while [18], [19] consider network power minimization as the objective.”
Fronthaul-constrained cloud radio access networks: insights and challenges (2015, background): “For example, the mixed l1/lp -norm is adopted in[11] to induce sparsity”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “The algorithmic advantages of the sparse optimization modeling approach are supported by various convex relaxation algorithms [198], [258], e.g., mixed (cid:96) 1 /(cid:96) 2 -norm minimization [80].”
Alternating Minimization Algorithms for Hybrid Precoding in Millimeter Wave MIMO Systems (2016, background): “Further improvement in area spectral efficiency can be achieved by network densification, such as deploying small cells [4], [5] and allowing device-to-device (D2D) communications [6], and enabling advanced cooperation, such as Cloud-RANs [7], [8].”
Content-Centric Sparse Multicast Beamforming for Cache-Enabled Cloud RAN (2015, methodology): “Similar greedy algorithms without cache for user-centric BS clustering are proposed in [17].”
A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs (2017, methodology): “[17] presented a framework to design an energy-efficient cloud RAN, which is formulated as a joint RRH selection and power minimization beamforming problem, and solved by a group sparse beamforming method.”
Federated Learning via Over-the-Air Computation (2018, background): “Sparse and low-rank optimizations have attracted much attention in machine learning, signal processing, high-dimensional statistics, as well as wireless communication [41]–[45].”
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis (2020, methodology): “the optimization-based methods in [19], [20].”
Heterogeneous cloud radio access networks: a new perspective for enhancing spectral and energy efficiencies (2014, methodology): “To decrease the implementation complexity, a joint RRH selection and power minimization beamforming problem was proposed in [8], where a bi-section group sparse beamforming (GSBF) algorithm and an iterative GSBF algorithm are proposed.”
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues (2016, methodology): “In [60], [104], mixed l1/lp-norm approximation method is proposed to handle a group sparse based RRH selection problem.”