Neural network-based optimal and adaptable power allocation for real-time FSO-RF communications using Jetson nano

Power allocation (PA) is a significant and challenging real-time optimization problem in the management of optical-radio wireless networks. Analytical methods involve numerous calculations and require an extended processing time. Therefore, DNNs are employed to design a fast, real-time PA system with the required accuracy. In this paper, the innovation of a real-time optimal PA system is presented, enabling two separate three-layer DNNs to perform FSO-RF PA in parallel. The WMMSE algorithm is utilized on various RF channel models, including fading and user priorities, to produce training data. Additionally, the analytical algorithm for calculating BER is used to adjust the transmitter power interval for different FSO channel models, accommodating various modulation schemes and transmitter and receiver sizes. Finally, the DNNs were implemented on Jetson Nano, and the results were compared and validated with analytical methods. The implemented system shows 1.6 Gbps for the sum rate and an average accuracy of 97.82% for the RF channel with 10, 20, and 30 users, while the FSO channel achieves 1.6 Gbps for the data rate and an average accuracy of 98.87%. The implemented system exhibits suitable accuracy and speed in comparison to analytical algorithms for real-time optimal PA in FSO-RF wireless networks.