Energy Efficiency Optimization for Downlink Cloud RAN with Limited Fronthaul Capacity.

Area of Science:

• Wireless Communications
• Network Engineering
• Signal Processing

Background:

• Fronthaul compression in downlink Cloud Radio Access Networks (C-RANs) addresses capacity limitations.
• Existing methods prioritize spectral efficiency, potentially compromising energy efficiency (EE) for large-scale deployments.
• Maximizing EE in C-RANs with limited fronthaul capacity is a critical challenge.

Purpose of the Study:

• To develop a low-complexity algorithm with a closed-form solution for EE maximization in downlink C-RANs.
• To provide a benchmark for EE maximization using an optimal branch-and-bound solution.
• To propose a robust algorithm that performs well even with imperfect channel state information.

Main Methods:

• Derived an optimal solution using the branch-and-bound approach for benchmarking.
• Transformed the EE maximization problem into a parametric subtractive form.
• Proposed a Two-Layer Decentralized (TLD) algorithm utilizing bisection search and an alternating direction method of multipliers (ADMM) for a parallel, closed-form solution.

Main Results:

• The TLD algorithm achieves near-optimal EE, significantly outperforming spectral efficiency maximization.
• The proposed algorithms demonstrate robust performance against channel errors and lack of perfect channel state information.
• The TLD algorithm offers a low-complexity and efficient solution for EE maximization in C-RANs.

Conclusions:

• The developed TLD algorithm effectively maximizes energy efficiency in C-RANs with limited fronthaul capacity.
• The TLD algorithm provides a practical and scalable solution compared to existing methods.
• Robust extensions of the algorithms ensure reliable performance under realistic channel conditions.