A data driven approach in less expensive robust transmitting coverage and power optimization.

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Wireless Communications

Background:

• Transmitter placement is crucial for network performance, balancing signal coverage and energy consumption.
• Uncertainty in deployment environments necessitates robust optimization strategies.
• Existing methods can be computationally intensive, limiting practical application.

Purpose of the Study:

• To develop a reduced-cost algorithm for multi-objective robust transmitter placement.
• To optimize transmitter positions considering both signal coverage maximization and energy consumption minimization.
• To enhance the reliability of signal coverage control in multi-antenna systems.

Main Methods:

• A hybrid Kriging/Grey Wolf Optimizer (GWO) approach combined with robust design optimization.
• Kriging interpolation is used to approximate signal coverages, reducing computational load.
• Multi-objective optimization model considering robustness, accuracy, energy consumption, and signal coverage.

Main Results:

• The proposed method effectively estimates the Pareto frontier for robust transmitter placement.
• Significant computational cost reduction was achieved compared to standalone GWO and NSGA-II (350% and 320% less time, respectively).
• Demonstrated utility in analyzing the sensitivity of transmitter placement under uncertainty.

Conclusions:

• The hybrid Kriging/GWO approach offers an efficient and cost-effective solution for robust transmitter placement.
• This method provides a practical means to achieve optimal network design with reduced computational resources.
• The approach is valuable for real-world wireless network deployment and optimization.