Enhanced Group Sparse Beamforming for Green Cloud-RAN: A Random Matrix Approach

• Published in: IEEE transactions on wireless communications Vol. 17; no. 4; pp. 2511 - 2524
• Main Authors: Yuanming Shi, Jun Zhang, Wei Chen, Letaief, Khaled B.
• Format: Journal Article
• Language: English
• Published: IEEE 01.04.2018
• Subjects: Algorithm design and analysis; Array signal processing; Cloud computing; Cloud-RAN; green communications; Lagrangian duality; Power demand; random matrix theory; Signal processing algorithms; smoothed l<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">p -minimization; sparse optimization; Wireless networks
• ISSN: 1536-1276
• DOI: 10.1109/TWC.2018.2797203

Group sparse beamforming is a general framework to minimize the network power consumption for cloud radio access networks, which, however, suffers high computational complexity. In particular, a complex optimization problem needs to be solved to obtain the remote radio head (RRH) ordering criterion in each transmission block, which will help to determine the active RRHs and the associated fronthaul links. In this paper, we propose innovative approaches to reduce the complexity of this key step in group sparse beamforming. Specifically, we first develop a smoothed ℓ p -minimization approach with the iterative reweighted-ℓ 2 algorithm to return a Karush-Kuhn- Tucker (KKT) point solution, as well as enhance the capability of inducing group sparsity in the beamforming vectors. By leveraging the Lagrangian duality theory, we obtain closedform solutions at each iteration to reduce the computational complexity. The well-structured solutions provide opportunities to apply the large-dimensional random matrix theory to derive deterministic approximations for the RRH ordering criterion. Such an approach helps to guide the RRH selection only based on the statistical channel state information, which does not require frequent update, thereby significantly reducing the computation overhead. Simulation results shall demonstrate the performance gains of the proposed ℓ p -minimization approach, as well as the effectiveness of the large system analysis-based framework for computing the RRH ordering criterion.