Energy-Efficient Optimization for Energy-Harvesting-Enabled mmWave-UAV Heterogeneous Networks

Area of Science:

• Wireless Communication Networks
• Energy Harvesting Technologies
• Optimization Algorithms

Background:

• 5G heterogeneous networks benefit from Energy Harvesting (EH) for Device-to-Device (D2D) communication, addressing limited battery capacity.
• Existing research on EH-based D2D communication has not fully explored Air-to-Ground (A2G) millimeter-Wave (mmWave) networks.

Purpose of the Study:

• To improve the network Energy Efficiency (EE) of EH-enabled D2D communications in A2G mmWave networks.
• To reduce the time complexity of beam alignment for mmWave-enabled D2D Users (DUs).

Main Methods:

• A joint optimization algorithm for beamwidth selection, power control, and EH time ratio using alternating optimization.
• Iterative optimization of variables, employing game theory for beamwidth, Dinkelbach method and Successive Convex Approximation (SCA) for power, and linear fractional programming for EH time ratio.

Main Results:

• The proposed algorithm demonstrates convergence and effectiveness through simulations.
• It significantly outperforms fixed strategies and approaches the performance of complex methods like exhaustive search, PSO, and GA with reduced time complexity.

Conclusions:

• The developed algorithm successfully enhances network EE and reduces beam alignment complexity in mmWave A2G D2D networks.
• This research provides a practical and efficient solution for optimizing energy harvesting in future wireless communication systems.