Energy-Effective Power Control Algorithm with Mobility Prediction for 5G Heterogeneous Cloud Radio Access Network.

Area of Science:

• Telecommunications Engineering
• Wireless Communication Systems
• Network Resource Management

Background:

• 5G networks increasingly utilize heterogeneous cloud radio access networks (H-CRAN) for energy efficiency and resource allocation.
• Growing user numbers in 5G H-CRAN necessitate advanced solutions to manage energy consumption challenges.

Purpose of the Study:

• To propose a novel power control algorithm incorporating mobility prediction for enhanced energy efficiency in 5G H-CRAN.
• To address energy consumption issues in 5G H-CRAN, particularly in vehicular mobility scenarios.

Main Methods:

• Developed a power control algorithm that predicts User Equipment (UE) mobility in vehicular scenarios.
• Implemented remote radio head (RRH) switching operations based on mobility prediction outcomes.
• Formulated an optimization problem to maximize energy efficiency under outage probability constraints.
• Utilized Markov mobility prediction and a gradient method for transmission power optimization.

Main Results:

• The proposed algorithm demonstrated significant improvements in energy efficiency compared to existing RRH switching algorithms.
• Simulation results validated the effectiveness of the mobility prediction-driven RRH switching strategy.

Conclusions:

• The proposed power control algorithm with mobility prediction offers a viable solution for improving energy efficiency in 5G H-CRAN.
• This approach is particularly effective in dynamic vehicular mobility environments, optimizing resource allocation and power usage.