Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  investigate the network por minimization problem for the multicast cloud radio access network (Cloud-RAN) with imperfect channel state information (CSI). The key observation is that network por minimization can be achieved by adaptively selecting active remote radio heads (RRHs) via controlling the group-sparsity structure of the beamforming vector. Hover, this yields a non-convex combinatorial optimization problem, for which  propose a three-stage robust group sparse beamforming algorithm. In the first stage, a quadratic variational formulation of the ighted mixed l1/l2-norm is proposed to induce the group-sparsity structure in the aggregated beamforming vector, which indicates those RRHs that can be switched off. A perturbed alternating optimization algorithm is then proposed to solve the resultant non-convex group-sparsity inducing optimization problem by exploiting its convex substructures. In the second stage,  propose a PhaseLift technique based algorithm to solve the feasibility problem with a given active RRH set, which helps determine the active RRHs. Finally, the semidefinite relaxation (SDR) technique is adopted to determine the robust multicast beamformers. Simulation results will demonstrate the convergence of the perturbed alternating optimization algorithm, as ll as, the effectiveness of the proposed algorithm to minimize the network por consumption for multicast Cloud-RAN.
['Yuanming Shi', 'Jun Zhang', 'Khaled Ben Letaief']
I am writing a research paper on a idea Robust Group Sparse Beamforming for Multicast Green Cloud-RAN With Imperfect CSI
[{"from": "human", "value": "I am writing a research paper on a idea Robust Group Sparse Beamforming for Multicast Green Cloud-RAN With Imperfect CSI"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  investigate the network por minimization problem for the multicast cloud radio access network (Cloud-RAN) with imperfect channel state information (CSI). The key observation is that network por minimization can be achieved by adaptively selecting active remote radio heads (RRHs) via controlling the group-sparsity structure of the beamforming vector. Hover, this yields a non-convex combinatorial optimization problem, for which  propose a three-stage robust group sparse beamforming algorithm. In the first stage, a quadratic variational formulation of the ighted mixed l1/l2-norm is proposed to induce the group-sparsity structure in the aggregated beamforming vector, which indicates those RRHs that can be switched off. A perturbed alternating optimization algorithm is then proposed to solve the resultant non-convex group-sparsity inducing optimization problem by exploiting its convex substructures. In the second stage,  propose a PhaseLift technique based algorithm to solve the feasibility problem with a given active RRH set, which helps determine the active RRHs. Finally, the semidefinite relaxation (SDR) technique is adopted to determine the robust multicast beamformers. Simulation results will demonstrate the convergence of the perturbed alternating optimization algorithm, as ll as, the effectiveness of the proposed algorithm to minimize the network por consumption for multicast Cloud-RAN."}]