Optimal WDM Power Allocation via Deep Learning for Radio on Free Space Optics Systems
This work addresses power allocation optimization for RoFSO systems, which is an incremental improvement for enhancing transmission efficiency in free space optical networks.
The paper tackles the problem of optimal power allocation for Wavelength Division Multiplexing (WDM) in Radio on Free Space Optics (RoFSO) systems to maximize weighted total capacity under power and safety constraints, achieving significant performance gains over average equal power allocation in simulations.
Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous wireless services, is able to transmit multiple radio frequency signals at high rates in free space optical networks. This paper investigates the optimal design of power allocation for Wavelength Division Multiplexing (WDM) transmission in RoFSO systems. The proposed problem is a weighted total capacity maximization problem with two constraints of total power limitation and eye safety concern. The model-based Stochastic Dual Gradient algorithm is presented first, which solves the problem exactly by exploiting the null duality gap. The model-free Primal-Dual Deep Learning algorithm is then developed to learn and optimize the power allocation policy with Deep Neural Network (DNN) parametrization, which can be utilized without any knowledge of system models. Numerical simulations are performed to exhibit significant performance of our algorithms compared to the average equal power allocation.