$L_2$-Box Optimization for Green Cloud-RAN via Network Adaptation

TL;DR

This paper introduces an innovative $ ext{L}_2$-box optimization approach for Cloud-RAN that reformulates the power minimization problem into a continuous model, leading to more efficient solutions and reduced network power consumption.

Contribution

It presents a novel $ ext{L}_2$-box reformulation for mixed-integer problems in Cloud-RAN, combined with a dual ascent and MM algorithm, improving power efficiency over existing methods.

Findings

Achieves smaller network power consumption than previous algorithms.

Effective in sparser Cloud-RAN configurations with many RRHs and fewer users.

Outperforms bi-section GSBF algorithm in simulations.

Abstract

In this paper, we propose a reformulation for the Mixed Integer Programming (MIP) problem into an exact and continuous model through using the $\ell_2$-box technique to recast the binary constraints into a box with an $\ell_2$ 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 $\ell_1 / \ell_2$-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.