In this paper, we propose a reformulation for the Mixed Integer Programming (MIP) problem into an exact and continuous model through using the ℓ2-box technique to recast the binary constraints into a box with an ℓ2 sphere constraint. The reformulated problem can be tackled by a dual ascent algorithm combined with a Majorization-Minimization (MM) method for the subproblems to solve the network power consumption problem of the Cloud Radio Access Network (Cloud-RAN), and which leads to solving a sequence of Difference of Convex (DC) subproblems handled by an inexact MM algorithm. After obtaining the final solution, we use it as the initial result of the bi-section Group Sparse Beamforming (GSBF) algorithm to promote the group-sparsity of beamformers, rather than using the weighted ℓ1/ℓ2-norm. Simulation results indicate that the new method outperforms the bi-section GSBF algorithm by achieving smaller network power consumption, especially in sparser cases, i.e., Cloud-RANs with a lot of Remote Radio Heads (RRHs) but fewer users.
@article{arxiv.1711.10788,
title = {$L_2$-Box Optimization for Green Cloud-RAN via Network Adaptation},
author = {Fan Zhang and Qiong Wu and Hao Wang and Yuanming Shi},
journal= {arXiv preprint arXiv:1711.10788},
year = {2017}
}