Artificial Neural Network Optimizes Drone Energy Consumption

The future of networks must include high transmission speeds and flexible coverage, and one way to do so is by using networks of unmanned aerial vehicles, or drones. This method has several disadvantages, including the need for a wide range of antennas causing higher losses during signal propagation. Furthermore, all this requires lots of energy, and drone batteries have limited capacity.

Researchers are looking for new approaches to optimize energy consumption in these networks. A group of researchers from Russia, Egypt, China, Saudi Arabia, and Uzbekistan have created an artificial neural network to solve this problem.

Professor Ammar Muthanna, Director of the Scientific Center for Modeling Wireless 5G Networks at RUDN University, explained: “A network built using drones expands network capacity and coverage. In addition, drones are used as mobile charging stations to supply power to low-power gadgets. Since batteries on drones are typically limited in capacity, it is important to make tradeoffs between coverage area and energy use, as well as maintenance time. To improve coverage and energy efficiency, it is important to allocate resources, namely subchannels, transmission power, and user services.”

According to Techxplore, mathematicians have developed an optimization system called IRA-AEODL (intelligent resource allocation using an artificial ecosystem optimizer with deep learning), which distributes resources in a wireless network to drones. The IRA-AEODL system has reportedly significantly improved the performance of other known systems- raising efficiency up to 30%.

“Our network resource allocation technique has improved performance. Compared to other approaches, our algorithm is more stable from a mathematical point of view. The model can quickly find the optimal solution to the problem,” explained Muthanna.