  The cloud radio access network (Cloud-RAN) has recently been proposed as one
cost-effective and energy-efficient technique for 5G wireless networks. By
moving the signal processing functionality to a single baseband unit (BBU)
pool, centralized signal processing and resource allocation are enabled in
Cloud-RAN, thereby providing the promise of improving the energy efficiency via
effective network adaptation and interference management. In this paper, we
propose a holistic sparse optimization framework to design green Cloud-RAN by
taking into consideration the power consumption of the fronthaul links,
multicast services, as well as user admission control. Specifically, we first
identify the sparsity structures in the solutions of both the network power
minimization and user admission control problems. However, finding the optimal
sparsity structures turns out to be NP-hard, with the coupled challenges of the
l0-norm based objective functions and the nonconvex quadratic QoS constraints
due to multicast beamforming. In contrast to the previous works on convex but
non-smooth sparsity inducing approaches, e.g., the group sparse beamforming
algorithm based on the mixed l1/l2-norm relaxation [1], we adopt the nonconvex
but smoothed lp-minimization (0 < p <= 1) approach to promote sparsity in the
multicast setting, thereby enabling efficient algorithm design based on the
principle of the majorization-minimization (MM) algorithm and the semidefinite
relaxation (SDR) technique. In particular, an iterative reweighted-l2 algorithm
is developed, which will converge to a Karush-Kuhn-Tucker (KKT) point of the
relaxed smoothed lp-minimization problem from the SDR technique. We illustrate
the effectiveness of the proposed algorithms with extensive simulations for
network power minimization and user admission control in multicast Cloud-RAN.
