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The cloud radio access network (Cloud-RAN) has recently been proposed as onecost-effective and energy-efficient technique for 5G wireless networks. Bymoving the signal processing functionality to a single baseband unit (BBU)pool, centralized signal processing and resource allocation are enabled inCloud-RAN, thereby providing the promise of improving the energy efficiency viaeffective network adaptation and interference management. In this paper, wepropose a holistic sparse optimization framework to design green Cloud-RAN bytaking into consideration the power consumption of the fronthaul links,multicast services, as well as user admission control. Specifically, we firstidentify the sparsity structures in the solutions of both the network powerminimization and user admission control problems. However, finding the optimalsparsity structures turns out to be NP-hard, with the coupled challenges of thel0-norm based objective functions and the nonconvex quadratic QoS constraintsdue to multicast beamforming. In contrast to the previous works on convex butnon-smooth sparsity inducing approaches, e.g., the group sparse beamformingalgorithm based on the mixed l1/l2-norm relaxation [1], we adopt the nonconvexbut smoothed lp-minimization (0 < p <= 1) approach to promote sparsity in themulticast setting, thereby enabling efficient algorithm design based on theprinciple of the majorization-minimization (MM) algorithm and the semidefiniterelaxation (SDR) technique. In particular, an iterative reweighted-l2 algorithmis developed, which will converge to a Karush-Kuhn-Tucker (KKT) point of therelaxed smoothed lp-minimization problem from the SDR technique. We illustratethe effectiveness of the proposed algorithms with extensive simulations fornetwork power minimization and user admission control in multicast Cloud-RAN.

Smoothed Lp-Minimization for Green Cloud-RAN with User Admission Control